Segmentation and Classification of Burn Color Images

National Research Council Canada - National Science Library

Acha, Begonya

2001-01-01

.... In the classification part, we take advantage of color information by clustering, with a vector quantization algorithm, the color centroids of small squares, taken from the burnt segmented part of the image, in the (V1, V2) plane into two possible groups, where V1 and V2 are the two chrominance components of the CIE Lab representation.

Color classification of veal carcasses: Past, present and future

NARCIS (Netherlands)

Lucassen, M.P.; Alferdinck, J.W.A.M.; Megen, R. van

2010-01-01

In The Netherlands, veal carcasses are classified on color, conformation and fatness. In the past 20 years, major efforts have been put into the development of a reliable color classification system. Initially, the color of the musculus rectus abdominis was visually matched to a 10-point scale.

Automated color classification of urine dipstick image in urine examination

Science.gov (United States)

Rahmat, R. F.; Royananda; Muchtar, M. A.; Taqiuddin, R.; Adnan, S.; Anugrahwaty, R.; Budiarto, R.

2018-03-01

Urine examination using urine dipstick has long been used to determine the health status of a person. The economical and convenient use of urine dipstick is one of the reasons urine dipstick is still used to check people health status. The real-life implementation of urine dipstick is done manually, in general, that is by comparing it with the reference color visually. This resulted perception differences in the color reading of the examination results. In this research, authors used a scanner to obtain the urine dipstick color image. The use of scanner can be one of the solutions in reading the result of urine dipstick because the light produced is consistent. A method is required to overcome the problems of urine dipstick color matching and the test reference color that have been conducted manually. The method proposed by authors is Euclidean Distance, Otsu along with RGB color feature extraction method to match the colors on the urine dipstick with the standard reference color of urine examination. The result shows that the proposed approach was able to classify the colors on a urine dipstick with an accuracy of 95.45%. The accuracy of color classification on urine dipstick against the standard reference color is influenced by the level of scanner resolution used, the higher the scanner resolution level, the higher the accuracy.

Color vision test

Science.gov (United States)

... present from birth) color vision problems: Achromatopsia -- complete color blindness , seeing only shades of gray Deuteranopia -- difficulty telling ... Vision test - color; Ishihara color vision test Images Color blindness tests References Bowling B. Hereditary fundus dystrophies. In: ...

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine.

Science.gov (United States)

Qi, Zhen; Tu, Li-Ping; Chen, Jing-Bo; Hu, Xiao-Juan; Xu, Jia-Tuo; Zhang, Zhi-Feng

2016-01-01

Background and Goal . The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods . Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L * a * b * of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results . The L * a * b * values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions . At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

Science.gov (United States)

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

Directory of Open Access Journals (Sweden)

Haidi Ibrahim

2012-10-01

Full Text Available Ripeness classification of oil palm fresh fruit bunches (FFBs during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

A Neural-Network-Based Approach to White Blood Cell Classification

Directory of Open Access Journals (Sweden)

Mu-Chun Su

2014-01-01

Full Text Available This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Improving scale invariant feature transform with local color contrastive descriptor for image classification

Science.gov (United States)

Guo, Sheng; Huang, Weilin; Qiao, Yu

2017-01-01

Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.

Color image definition evaluation method based on deep learning method

Science.gov (United States)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Soccer player recognition by pixel classification in a hybrid color space

Science.gov (United States)

Vandenbroucke, Nicolas; Macaire, Ludovic; Postaire, Jack-Gerard

1997-08-01

Soccer is a very popular sport all over the world, Coaches and sport commentators need accurate information about soccer games, especially about the players behavior. These information can be gathered by inspectors who watch the soccer match and report manually the actions of the players involved in the principal phases of the game. Generally, these inspectors focus their attention on the few players standing near the ball and don't report about the motion of all the other players. So it seems desirable to design a system which automatically tracks all the players in real- time. That's why we propose to automatically track each player through the successive color images of the sequences acquired by a fixed color camera. Each player which is present in the image, is modelized by an active contour model or snake. When, during the soccer match, a player is hidden by another, the snakes which track these two players merge. So, it becomes impossible to track the players, except if the snakes are interactively re-initialized. Fortunately, in most cases, the two players don't belong to the same team. That is why we present an algorithm which recognizes the teams of the players by pixels representing the soccer ground which must be withdrawn before considering the players themselves. To eliminate these pixels, the color characteristics of the ground are determined interactively. In a second step, dealing with windows containing only one player of one team, the color features which yield the best discrimination between the two teams are selected. Thanks to these color features, the pixels associated to the players of the two teams form two separated clusters into a color space. In fact, there are many color representation systems and it's interesting to evaluate the features which provide the best separation between the two classes of pixels according to the players soccer suit. Finally, the classification process for image segmentation is based on the three most

Nearest patch matching for color image segmentation supporting neural network classification in pulmonary tuberculosis identification

Science.gov (United States)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2016-03-01

Pulmonary tuberculosis is a deadly infectious disease which occurs in many countries in Asia and Africa. In Indonesia, many people with tuberculosis disease are examined in the community health center. Examination of pulmonary tuberculosis is done through sputum smear with Ziehl - Neelsen staining using conventional light microscope. The results of Ziehl - Neelsen staining will give effect to the appearance of tuberculosis (TB) bacteria in red color and sputum background in blue color. The first examination is to detect the presence of TB bacteria from its color, then from the morphology of the TB bacteria itself. The results of Ziehl - Neelsen staining in sputum smear give the complex color images, so that the clinicians have difficulty when doing slide examination manually because it is time consuming and needs highly training to detect the presence of TB bacteria accurately. The clinicians have heavy workload to examine many sputum smear slides from the patients. To assist the clinicians when reading the sputum smear slide, this research built computer aided diagnose with color image segmentation, feature extraction, and classification method. This research used K-means clustering with patch technique to segment digital sputum smear images which separated the TB bacteria images from the background images. This segmentation method gave the good accuracy 97.68%. Then, feature extraction based on geometrical shape of TB bacteria was applied to this research. The last step, this research used neural network with back propagation method to classify TB bacteria and non TB bacteria images in sputum slides. The classification result of neural network back propagation are learning time (42.69±0.02) second, the number of epoch 5000, error rate of learning 15%, learning accuracy (98.58±0.01)%, and test accuracy (96.54±0.02)%.

Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

Directory of Open Access Journals (Sweden)

Huidong He

2017-01-01

Full Text Available The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE. Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

[Web-based analysis of Stilling's color plates].

Science.gov (United States)

Kuchenbecker, J

2014-12-01

Color vision tests with pseudoisochromatic plates currently represent the most common procedure for the screening of congenital color vision deficiencies. By means of a web-based color vision test, new and old color plates can be tested for diagnostic quality without major effort. A total of 16 digitized Stilling's color plates of the 11th edition from 1907 were included in a web-based color vision test (http://www.farbsehtest.de). The χ(2)-test was used to check whether the Stilling color plates showed similar results to the nine previously evaluated Ishihara color plates. A total of 518 subjects including101 (19.5 %) female subjects with a mean age of 34.6 ± 17 years, took the web-based test with the 25 plates. For all participants the range for the correctly recognized plates was between 5.2 % (n = 27) and 97.7 % (n = 506) for the Stilling color plates and between 64.9 % (n = 336) and 100 % (n = 518) for the Ishihara color plates. For participants with more than 5 errors (n = 247), the range for correctly recognized plates was between 2.0 % (n = 5) and 98.0 % (n = 242) for the Stilling plates and between 42.5 % (n = 105) and 100 % (n = 247) for the Ishihara plates. Taking all color plates and all participants into account there was a significantly higher incidence of erroneous recognition of the Stilling color plates (3038 false and 5250 true answers) compared to the Ishihara color plates (1511 false and 3151 true answers) (p plates could be used for the test edition of the Velhagen/Broschmann/Kuchenbecker color plates from 2014. Overall, the Stilling color plates were recognized with a higher incidence of error by all participants in the web-based test compared to the utilized Ishihara color plates, which in most cases was attributable to ambiguity of some symbols.

Computerized Classification Testing with the Rasch Model

Science.gov (United States)

Eggen, Theo J. H. M.

2011-01-01

If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

OpenAIRE

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Ne...

DECISION LEVEL FUSION OF LIDAR DATA AND AERIAL COLOR IMAGERY BASED ON BAYESIAN THEORY FOR URBAN AREA CLASSIFICATION

Directory of Open Access Journals (Sweden)

H. Rastiveis

2015-12-01

Full Text Available Airborne Light Detection and Ranging (LiDAR generates high-density 3D point clouds to provide a comprehensive information from object surfaces. Combining this data with aerial/satellite imagery is quite promising for improving land cover classification. In this study, fusion of LiDAR data and aerial imagery based on Bayesian theory in a three-level fusion algorithm is presented. In the first level, pixel-level fusion, the proper descriptors for both LiDAR and image data are extracted. In the next level of fusion, feature-level, using extracted features the area are classified into six classes of “Buildings”, “Trees”, “Asphalt Roads”, “Concrete roads”, “Grass” and “Cars” using Naïve Bayes classification algorithm. This classification is performed in three different strategies: (1 using merely LiDAR data, (2 using merely image data, and (3 using all extracted features from LiDAR and image. The results of three classifiers are integrated in the last phase, decision level fusion, based on Naïve Bayes algorithm. To evaluate the proposed algorithm, a high resolution color orthophoto and LiDAR data over the urban areas of Zeebruges, Belgium were applied. Obtained results from the decision level fusion phase revealed an improvement in overall accuracy and kappa coefficient.

Colorful ESL Test Papers And Spatial Intelligence:

Directory of Open Access Journals (Sweden)

Dennis Moradkhan

2014-11-01

Full Text Available The purpose of this research was to find out whether introducing color as an element which may appeal to spatially-intelligent candidates affects their performance on ESL grammar tests. 52 participants were given two parallel grammar tests, one in black and white and the other bearing the full spectrum of colors in the natural daylight. In order to identify the candidates with visual-spatial learning style, the participants and their teachers were asked to respond to Visual-Spatial Identifier rating scale. Based on the results, no significant relationship was found between the performance of candidates on the colorful and black and white grammar tests and their visual-spatial intelligence. It was concluded that other variables including the method of applying colors, the type and combination of colors as well as the differential impact of different colors on candidates with different cultural backgrounds needed to be addressed before any conclusions can be drawn about the application of color in language assessment.

Butterfly Classification by HSI and RGB Color Models Using Neural Networks

Directory of Open Access Journals (Sweden)

Jorge E. Grajales-Múnera

2013-11-01

Full Text Available This study aims the classification of Butterfly species through the implementation of Neural Networks and Image Processing. A total of 9 species of Morpho genre which has blue as a characteristic color are processed. For Butterfly segmentation we used image processing tools such as: Binarization, edge processing and mathematical morphology. For data processing RGB values are obtained for every image which are converted to HSI color model to identify blue pixels and obtain the data to the proposed Neural Networks: Back-Propagation and Perceptron. For analysis and verification of results confusion matrix are built and analyzed with the results of neural networks with the lowest error levels. We obtain error levels close to 1% in classification of some Butterfly species.

Analysis on correlation between overall classification on color doppler ultrasound and clinical stages of atherosclerosis obliterans

International Nuclear Information System (INIS)

Zhang Dongmei; Liu Meihan; Shi Weidong; Chen Enqi; Li Xinying; Lin Yu

2010-01-01

Objective: To investigate the correlation and the clinical significance between the overall classification on color Doppler ultrasound and the clinical stages of atherosclerosis obliterans (ASO), and evaluate the extent of arterial lesions comprehensively. Methods: 125 patients of ASO, who were divided into three groups of mild, moderate and severe with Color Doppler ultrasound according to differences of occlusion, quantity, degree of stenosis and collateral number, were analyzed with clinical stages, then their associations were studied with Spearman rank analysis. Results: The clinical manifestations of ASO patients who were divided into three groups of mild, moderate and severe according to overall classification on color Doppler ultrasound were respectively gradually serious, which had positive correlations with the stages of I, II and III according to clinical stages. Spearman rank analysis showed that the correlation coefficients (rs)was 0.797 2 between two groups (P<0.01), there was good consistency between the overall classification on color Doppler ultrasound and the clinical stagesof ASO. Conclusion: The overall classification of ASO on color Doppler ultrasound has considered impact of many other factors on the clinical symptoms,such as the level of the local narrow, narrow scope, segments of occlusion and collateral arteries, which divides the lesions more objectively, shows good consistency with the clinical stages. (authors)

Knowledge-based approach to video content classification

Science.gov (United States)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Color film spectral properties test experiment for target simulation

Science.gov (United States)

Liu, Xinyue; Ming, Xing; Fan, Da; Guo, Wenji

2017-04-01

In hardware-in-loop test of the aviation spectra camera, the liquid crystal light valve and digital micro-mirror device could not simulate the spectrum characteristics of the landmark. A test system frame was provided based on the color film for testing the spectra camera; and the spectrum characteristics of the color film was test in the paper. The result of the experiment shows that difference was existed between the landmark and the film spectrum curse. However, the spectrum curse peak should change according to the color, and the curse is similar with the standard color traps. So, if the quantity value of error between the landmark and the film was calibrated and the error could be compensated, the film could be utilized in the hardware-in-loop test for the aviation spectra camera.

SB certification handout material requirements, test methods, responsibilities, and minimum classification levels for mixture-based specification for flexible base.

Science.gov (United States)

2012-10-01

A handout with tables representing the material requirements, test methods, responsibilities, and minimum classification levels mixture-based specification for flexible base and details on aggregate and test methods employed, along with agency and co...

The mere exposure effect is sensitive to color information: evidence for color effects in a perceptual implicit memory test.

Science.gov (United States)

Hupbach, Almut; Melzer, André; Hardt, Oliver

2006-01-01

Priming effects in perceptual tests of implicit memory are assumed to be perceptually specific. Surprisingly, changing object colors from study to test did not diminish priming in most previous studies. However, these studies used implicit tests that are based on object identification, which mainly depends on the analysis of the object shape and therefore operates color-independently. The present study shows that color effects can be found in perceptual implicit tests when the test task requires the processing of color information. In Experiment 1, reliable color priming was found in a mere exposure design (preference test). In Experiment 2, the preference test was contrasted with a conceptually driven color-choice test. Altering the shape of object from study to test resulted in significant priming in the color-choice test but eliminated priming in the preference test. Preference judgments thus largely depend on perceptual processes. In Experiment 3, the preference and the color-choice test were studied under explicit test instructions. Differences in reaction times between the implicit and the explicit test suggest that the implicit test results were not an artifact of explicit retrieval attempts. In contrast with previous assumptions, it is therefore concluded that color is part of the representation that mediates perceptual priming.

A practical approach to the classification of IRAS sources using infrared colors alone

International Nuclear Information System (INIS)

Walker, H.J.; Volk, K.; Wainscoat, R.J.; Schwartz, D.E.; Cohen, M.

1989-01-01

Zones of the IRAS color-color planes in which a variety of different types of known source occur, have been defined for the purpose of obtaining representative IRAS colors for them. There is considerable overlap between many of these zones, rendering a unique classification difficult on the basis of IRAS colors alone, although galactic latitude can resolve ambiguities between galactic and extragalactic populations. The color dependence of these zones on the presence of spectral emission/absorption features and on the spatial extent of the sources has been investigated. It is found that silicate emission features do not significantly influence the IRAS colors. Planetary nebulae may show a dependence of color on the presence of atomic or molecular features in emission, although the dominant cause of this effect may be the underlying red continua of nebulae with strong atomic lines. Only small shifts are detected in the colors of individual spatially extended sources when total flux measurements are substituted for point-source measurements. 36 refs

Testing Children for Color Blindness

Science.gov (United States)

... Stories Español Eye Health / News Testing Children for Color Blindness Leer en Español: Pruebas para Detectar Daltonismo en ... study shows that kids can be tested for color blindness as soon as age 4, finds Caucasian boys ...

Pitch Based Sound Classification

DEFF Research Database (Denmark)

Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U

2006-01-01

A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

Science.gov (United States)

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Automatic Hierarchical Color Image Classification

Directory of Open Access Journals (Sweden)

Jing Huang

2003-02-01

Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

Color-Based Image Retrieval from High-Similarity Image Databases

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg; Carstensen, Jens Michael

2003-01-01

Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce...... a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval...... performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition....

Image Retrieval based on Integration between Color and Geometric Moment Features

International Nuclear Information System (INIS)

Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

2012-01-01

Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

A Color-Opponency Based Biological Model for Color Constancy

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Yongjie Li

2011-05-01

Full Text Available Color constancy is the ability of the human visual system to adaptively correct color-biased scenes under different illuminants. Most of the existing color constancy models are nonphysiologically plausible. Among the limited biological models, the great majority is Retinex and its variations, and only two or three models directly simulate the feature of color-opponency, but only of the very earliest stages of visual pathway, i.e., the single-opponent mechanisms involved at the levels of retinal ganglion cells and lateral geniculate nucleus (LGN neurons. Considering the extensive physiological evidences supporting that both the single-opponent cells in retina and LGN and the double-opponent neurons in primary visual cortex (V1 are the building blocks for color constancy, in this study we construct a color-opponency based color constancy model by simulating the opponent fashions of both the single-opponent and double-opponent cells in a forward manner. As for the spatial structure of the receptive fields (RF, both the classical RF (CRF center and the nonclassical RF (nCRF surround are taken into account for all the cells. The proposed model was tested on several typical image databases commonly used for performance evaluation of color constancy methods, and exciting results were achieved.

Hierarchical structure for audio-video based semantic classification of sports video sequences

Science.gov (United States)

Kolekar, M. H.; Sengupta, S.

2005-07-01

A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

High-speed potato grading and quality inspection based on a color vision system

Science.gov (United States)

Noordam, Jacco C.; Otten, Gerwoud W.; Timmermans, Toine J. M.; van Zwol, Bauke H.

2000-03-01

A high-speed machine vision system for the quality inspection and grading of potatoes has been developed. The vision system grades potatoes on size, shape and external defects such as greening, mechanical damages, rhizoctonia, silver scab, common scab, cracks and growth cracks. A 3-CCD line-scan camera inspects the potatoes in flight as they pass under the camera. The use of mirrors to obtain a 360-degree view of the potato and the lack of product holders guarantee a full view of the potato. To achieve the required capacity of 12 tons/hour, 11 SHARC Digital Signal Processors perform the image processing and classification tasks. The total capacity of the system is about 50 potatoes/sec. The color segmentation procedure uses Linear Discriminant Analysis (LDA) in combination with a Mahalanobis distance classifier to classify the pixels. The procedure for the detection of misshapen potatoes uses a Fourier based shape classification technique. Features such as area, eccentricity and central moments are used to discriminate between similar colored defects. Experiments with red and yellow skin-colored potatoes have shown that the system is robust and consistent in its classification.

Theory of color symmetry for periodic and quasiperiodic crystals

International Nuclear Information System (INIS)

Lifshitz, R.

1997-01-01

The author presents a theory of color symmetry applicable to the description and classification of periodic as well as quasiperiodic colored crystals. This theory is an extension to multicomponent fields of the Fourier-space approach of Rokhsar, Wright, and Mermin. It is based on the notion of indistinguishability and a generalization of the traditional concepts of color point group and color space group. The theory is applied toward (I) the classification of all black and white space-group types on standard axial quasicrystals in two and three dimensions; (II) the classification of all black and white space-group types in the icosahedral system; (III) the determination of the possible numbers of colors in a standard two-dimensional N-fold symmetric color field whose components are all indistinguishable; and (IV) the classification of two-dimensional decagonal and pentagonal n-color space-group types, explicitly listed for n≤25. copyright 1997 The American Physical Society

Robust tissue classification for reproducible wound assessment in telemedicine environments

Science.gov (United States)

Wannous, Hazem; Treuillet, Sylvie; Lucas, Yves

2010-04-01

In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.

Mimicking human texture classification

NARCIS (Netherlands)

Rogowitz, B.E.; van Rikxoort, Eva M.; van den Broek, Egon; Pappas, T.N.; Schouten, Theo E.; Daly, S.J.

2005-01-01

In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was

Identification and classification of similar looking food grains

Science.gov (United States)

Anami, B. S.; Biradar, Sunanda D.; Savakar, D. G.; Kulkarni, P. V.

2013-01-01

This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color - HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.

Chemometric classification of pigmented rice varieties based on antioxidative properties in relation to color

Directory of Open Access Journals (Sweden)

Phaiwan Pramai

2016-10-01

Full Text Available The pigmented Thai rice varieties including red and black color and non-pigmented rice (white collected from different growth sites in the north of Thailand and were determined for color and antioxidant properties. Anthocyanins were the major compound in group of black rice (21.15-441.96 mg/100 g rice. Total phenolic, flavonoid, and -tocopherol contents were highest in the black rice followed by red rice and antioxidant capacities were predominant in pigmented varieties. Black rice grown in mountainous area presented the highest antioxidant activity compared to the other growing locations. The color parameters, especially L* value presented the negative correlations with antioxidant parameters, while the antioxidant contents, excepted -oryzanol content had significant correlation with antioxidant capacities. Pigmented rice varieties could be clearly classified into 4 groups using PCA and HCA, which provided a good indicator to classify pigmented rice varieties based on color and antioxidative properties.

Fuzzy-based simulation of real color blindness.

Science.gov (United States)

Lee, Jinmi; dos Santos, Wellington P

2010-01-01

About 8% of men are affected by color blindness. That population is at a disadvantage since they cannot perceive a substantial amount of the visual information. This work presents two computational tools developed to assist color blind people. The first one tests color blindness and assess its severity. The second tool is based on Fuzzy Logic, and implements a method proposed to simulate real red and green color blindness in order to generate synthetic cases of color vision disturbance in a statistically significant amount. Our purpose is to develop correction tools and obtain a deeper understanding of the accessibility problems faced by people with chromatic visual impairment.

Verification of rapid method for estimation of added food colorant type in boiled sausages based on measurement of cross section color

Science.gov (United States)

Jovanović, J.; Petronijević, R. B.; Lukić, M.; Karan, D.; Parunović, N.; Branković-Lazić, I.

2017-09-01

During the previous development of a chemometric method for estimating the amount of added colorant in meat products, it was noticed that the natural colorant most commonly added to boiled sausages, E 120, has different CIE-LAB behavior compared to artificial colors that are used for the same purpose. This has opened the possibility of transforming the developed method into a method for identifying the addition of natural or synthetic colorants in boiled sausages based on the measurement of the color of the cross-section. After recalibration of the CIE-LAB method using linear discriminant analysis, verification was performed on 76 boiled sausages, of either frankfurters or Parisian sausage types. The accuracy and reliability of the classification was confirmed by comparison with the standard HPLC method. Results showed that the LDA + CIE-LAB method can be applied with high accuracy, 93.42 %, to estimate food color type in boiled sausages. Natural orange colors can give false positive results. Pigments from spice mixtures had no significant effect on CIE-LAB results.

An effective image classification method with the fusion of invariant feature and a new color descriptor

Science.gov (United States)

Mansourian, Leila; Taufik Abdullah, Muhamad; Nurliyana Abdullah, Lili; Azman, Azreen; Mustaffa, Mas Rina

2017-02-01

Pyramid Histogram of Words (PHOW), combined Bag of Visual Words (BoVW) with the spatial pyramid matching (SPM) in order to add location information to extracted features. However, different PHOW extracted from various color spaces, and they did not extract color information individually, that means they discard color information, which is an important characteristic of any image that is motivated by human vision. This article, concatenated PHOW Multi-Scale Dense Scale Invariant Feature Transform (MSDSIFT) histogram and a proposed Color histogram to improve the performance of existing image classification algorithms. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes

Directory of Open Access Journals (Sweden)

Dilip K. Prasad

2016-03-01

Full Text Available We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds. The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS, moderate-resolution imaging spectroradiometer (MODIS, sea-viewing wide field-of-view sensor (SeaWiFS, coastal zone color scanner (CZCS, ocean and land colour instrument (OLCI, and visible infrared imaging radiometer suite (VIIRS sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

Classification of user performance in the Ruff Figural Fluency Test based on eye-tracking features

Directory of Open Access Journals (Sweden)

Borys Magdalena

2017-01-01

Full Text Available Cognitive assessment in neurological diseases represents a relevant topic due to its diagnostic significance in detecting disease, but also in assessing progress of the treatment. Computer-based tests provide objective and accurate cognitive skills and capacity measures. The Ruff Figural Fluency Test (RFFT provides information about non-verbal capacity for initiation, planning, and divergent reasoning. The traditional paper form of the test was transformed into a computer application and examined. The RFFT was applied in an experiment performed among 70 male students to assess their cognitive performance in the laboratory environment. Each student was examined in three sequential series. Besides the students’ performances measured by using in app keylogging, the eye-tracking data obtained by non-invasive video-based oculography were gathered, from which several features were extracted. Eye-tracking features combined with performance measures (a total number of designs and/or error ratio were applied in machine learning classification. Various classification algorithms were applied, and their accuracy, specificity, sensitivity and performance were compared.

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography.

Science.gov (United States)

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2017-09-01

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

An experimental test of the role of structural blue and melanin-based chestnut coloration in aggressive contests in male eastern bluebirds

Directory of Open Access Journals (Sweden)

Austin eMercadante

2014-06-01

Full Text Available Male eastern bluebirds (Sialia sialis have feathers with either structurally based blue coloration or melanin-based chestnut coloration, and they hold territories during the breeding season that they defend vigorously. We tested whether the melanin pigmentation or structural coloration of feathers serve as signals during intrasexual aggressive encounters by placing color-modified stuffed bluebirds in male territories. We recorded the time to attack and the intensity of attacks on each model, and we then compared the response to color-enhanced versus color-reduced models. Male bluebirds attacked models with brighter and more chromatic blue coloration significantly more often than they attacked models with darker and less chromatic blue coloration. In contrast, the darkness of the chestnut breast coloration did not have a significant effect on the rate at which models were attacked. We conclude that territorial male bluebirds perceive intruding males with brighter blue coloration as a greater threat than males with drabber blue coloration, presumably because blue coloration is a signal of fighting ability. In contrast, the chestnut coloration of breast feathers appears to be a signal of gender and sexual maturity and not a graded signal of social status.

Highway pavement performance test for colored thin anti-skidding layers

Science.gov (United States)

Gao, Wei; Cui, Wei; Xu, Ming

2018-03-01

Based on the actual service condition of highway pavement colored thin anti-skidding layers, with materials of color quartz sand and two-component acrylic resin as basis, we designed such tests as the bond strength, shearing strength, tear strength, fatigue performance and aggregate polished value, and included the freeze-thaw cycle and de-icing salt and other factors in the experiment, connecting with the climate characteristics of circumpolar latitude and low altitude in Heilongjiang province. Through the pavement performance test, it is confirmed that the colored thin anti-skidding layers can adapt to cold and humid climate conditions, and its physical mechanical properties are good.

Significance of perceptually relevant image decolorization for scene classification

Science.gov (United States)

Viswanathan, Sowmya; Divakaran, Govind; Soman, Kutti Padanyl

2017-11-01

Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

[A study of biomechanical method for urine test based on color difference estimation].

Science.gov (United States)

Wang, Chunhong; Zhou, Yue; Zhao, Hongxia; Zhou, Fengkun

2008-02-01

The biochemical analysis of urine is an important inspection and diagnosis method in hospitals. The conventional method of urine analysis covers mainly colorimetric visual appraisement and automation detection, in which the colorimetric visual appraisement technique has been superseded basically, and the automation detection method is adopted in hospital; moreover, the price of urine biochemical analyzer on market is around twenty thousand RMB yuan (Y), which is hard to enter into ordinary families. It is known that computer vision system is not subject to the physiological and psychological influence of person, its appraisement standard is objective and steady. Therefore, according to the color theory, we have established a computer vision system, which can carry through collection, management, display, and appraisement of color difference between the color of standard threshold value and the color of urine test paper after reaction with urine liquid, and then the level of an illness can be judged accurately. In this paper, we introduce the Urine Test Biochemical Analysis method, which is new and can be popularized in families. Experimental result shows that this test method is easy-to-use and cost-effective. It can realize the monitoring of a whole course and can find extensive applications.

Cluster Based Text Classification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

ON A POSSIBLE SIZE/COLOR RELATIONSHIP IN THE KUIPER BELT

International Nuclear Information System (INIS)

Pike, R. E.; Kavelaars, J. J.

2013-01-01

Color measurements and albedo distributions introduce non-intuitive observational biases in size-color relationships among Kuiper Belt Objects (KBOs) that cannot be disentangled without a well characterized sample population with systematic photometry. Peixinho et al. report that the form of the KBO color distribution varies with absolute magnitude, H. However, Tegler et al. find that KBO color distributions are a property of object classification. We construct synthetic models of observed KBO colors based on two B–R color distribution scenarios: color distribution dependent on H magnitude (H-Model) and color distribution based on object classification (Class-Model). These synthetic B–R color distributions were modified to account for observational flux biases. We compare our synthetic B–R distributions to the observed ''Hot'' and ''Cold'' detected objects from the Canada-France Ecliptic Plane Survey and the Meudon Multicolor Survey. For both surveys, the Hot population color distribution rejects the H-Model, but is well described by the Class-Model. The Cold objects reject the H-Model, but the Class-Model (while not statistically rejected) also does not provide a compelling match for data. Although we formally reject models where the structure of the color distribution is a strong function of H magnitude, we also do not find that a simple dependence of color distribution on orbit classification is sufficient to describe the color distribution of classical KBOs

Automated artery-venous classification of retinal blood vessels based on structural mapping method

Science.gov (United States)

Joshi, Vinayak S.; Garvin, Mona K.; Reinhardt, Joseph M.; Abramoff, Michael D.

2012-03-01

Retinal blood vessels show morphologic modifications in response to various retinopathies. However, the specific responses exhibited by arteries and veins may provide a precise diagnostic information, i.e., a diabetic retinopathy may be detected more accurately with the venous dilatation instead of average vessel dilatation. In order to analyze the vessel type specific morphologic modifications, the classification of a vessel network into arteries and veins is required. We previously described a method for identification and separation of retinal vessel trees; i.e. structural mapping. Therefore, we propose the artery-venous classification based on structural mapping and identification of color properties prominent to the vessel types. The mean and standard deviation of each of green channel intensity and hue channel intensity are analyzed in a region of interest around each centerline pixel of a vessel. Using the vector of color properties extracted from each centerline pixel, it is classified into one of the two clusters (artery and vein), obtained by the fuzzy-C-means clustering. According to the proportion of clustered centerline pixels in a particular vessel, and utilizing the artery-venous crossing property of retinal vessels, each vessel is assigned a label of an artery or a vein. The classification results are compared with the manually annotated ground truth (gold standard). We applied the proposed method to a dataset of 15 retinal color fundus images resulting in an accuracy of 88.28% correctly classified vessel pixels. The automated classification results match well with the gold standard suggesting its potential in artery-venous classification and the respective morphology analysis.

Determining color difference thresholds in denture base acrylic resin.

Science.gov (United States)

Ren, Jiabao; Lin, Hong; Huang, Qingmei; Zheng, Gang

2015-11-01

In restorative prostheses, color is important, but the choice of color difference formula used to quantify color change in acrylic resins is not straightforward. The purpose of this in vitro study was to choose a color difference formula that best represented differences between the calculated color and the observed imperceptible to unacceptable color and to determine the corresponding perceptibility and acceptability threshold of color stability for denture base acrylic resins. A total of 291 acrylic resin denture base plates were fabricated and subjected to radiation tests from zero to 42 hours in accordance with ISO 7491:2000. Color was measured with a portable spectrophotometer, and color differences were calculated with 3 International Commission on Illumination (CIE) formulas: CIELab, CMC(1:1), and CIEDE2000. Thirty-four observers with no deficiencies in color perception participated in psychophysical perceptibility and acceptability assessments under controlled conditions in vitro. These 2 types of assessments were regressed to each observer by each formula to generate receiver operator characteristic (ROC) curves. Areas under the curves (AUCs) were then calculated and analyzed to exclude observers with poor color discrimination. AUCs were subjected to 1-way ANOVA (α=.05) to deter the statistical significance of discriminability among the 3 formulas in terms of perceptibility and acceptability judgments. Student-Newman-Keuls tests (α=.05) were used for post hoc comparison. CMC(1:1) and CIEDE2000 formulas performed better for imperceptible to unacceptable color differences, with corresponding CMC(1:1) and CIEDE2000 values for perceptibility of 2.52 and 1.72, respectively, and acceptability thresholds of 6.21 and 4.08, respectively. Formulas CMC(1:1) and CIEDE2000 possess higher discriminability than that of CIELab in the assessment of perceptible color difference threshold of denture base acrylic resin. A statistically significant difference exists

Color evaluation of computer-generated color rainbow holography

International Nuclear Information System (INIS)

Shi, Yile; Wang, Hui; Wu, Qiong

2013-01-01

A color evaluation approach for computer-generated color rainbow holography (CGCRH) is presented. Firstly, the relationship between color quantities of a computer display and a color computer-generated holography (CCGH) colorimetric system is discussed based on color matching theory. An isochromatic transfer relationship of color quantity and amplitude of object light field is proposed. Secondly, the color reproduction mechanism and factors leading to the color difference between the color object and the holographic image that is reconstructed by CGCRH are analyzed in detail. A quantitative color calculation method for the holographic image reconstructed by CGCRH is given. Finally, general color samples are selected as numerical calculation test targets and the color differences between holographic images and test targets are calculated based on our proposed method. (paper)

A Comparison of Computer-Based Classification Testing Approaches Using Mixed-Format Tests with the Generalized Partial Credit Model

Science.gov (United States)

Kim, Jiseon

2010-01-01

Classification testing has been widely used to make categorical decisions by determining whether an examinee has a certain degree of ability required by established standards. As computer technologies have developed, classification testing has become more computerized. Several approaches have been proposed and investigated in the context of…

[Research on developping the spectral dataset for Dunhuang typical colors based on color constancy].

Science.gov (United States)

Liu, Qiang; Wan, Xiao-Xia; Liu, Zhen; Li, Chan; Liang, Jin-Xing

2013-11-01

The present paper aims at developping a method to reasonably set up the typical spectral color dataset for different kinds of Chinese cultural heritage in color rendering process. The world famous wall paintings dating from more than 1700 years ago in Dunhuang Mogao Grottoes was taken as typical case in this research. In order to maintain the color constancy during the color rendering workflow of Dunhuang culture relics, a chromatic adaptation based method for developping the spectral dataset of typical colors for those wall paintings was proposed from the view point of human vision perception ability. Under the help and guidance of researchers in the art-research institution and protection-research institution of Dunhuang Academy and according to the existing research achievement of Dunhuang Research in the past years, 48 typical known Dunhuang pigments were chosen and 240 representative color samples were made with reflective spectral ranging from 360 to 750 nm was acquired by a spectrometer. In order to find the typical colors of the above mentioned color samples, the original dataset was devided into several subgroups by clustering analysis. The grouping number, together with the most typical samples for each subgroup which made up the firstly built typical color dataset, was determined by wilcoxon signed rank test according to the color inconstancy index comprehensively calculated under 6 typical illuminating conditions. Considering the completeness of gamut of Dunhuang wall paintings, 8 complementary colors was determined and finally the typical spectral color dataset was built up which contains 100 representative spectral colors. The analytical calculating results show that the median color inconstancy index of the built dataset in 99% confidence level by wilcoxon signed rank test was 3.28 and the 100 colors are distributing in the whole gamut uniformly, which ensures that this dataset can provide reasonable reference for choosing the color with highest

Memory-Context Effects of Screen Color in Multiple-Choice and Fill-In Tests

Science.gov (United States)

Prestera, Gustavo E.; Clariana, Roy; Peck, Andrew

2005-01-01

In this experimental study, 44 undergraduates completed five computer-based instructional lessons and either two multiplechoice tests or two fill-in-the-blank tests. Color-coded borders were displayed during the lesson, adjacent to the screen text and illustrations. In the experimental condition, corresponding border colors were shown at posttest.…

Prediction and Confirmation of V-type Asteroids Beyond 2.5 AU Based on SDSS Colors

Science.gov (United States)

Binzel, Richard P.; Masi, G.; Foglia, S.

2006-09-01

We apply a taxonomic classification system developed by Masi et al. (2006, submitted to Icarus) to identify C-, S-, and V-type asteroids present within the 3rd Release of the Sloan Digital Sky Survey Moving Object Catalog (SDSS MOC3). The classifications deduced by Masi et al. for 43,000 asteroids using SDSS colors are based on the taxonomy of Bus (1999; MIT Ph.D. thesis). To link SDSS colors to the Bus taxonomy, Masi et al. (2006) use 149 objects measured in common by both SDSS and the Small Main-Belt Asteroid Spectroscopic Survey (SMASS) (Bus and Binzel 2002, Icarus 158, 106). We report results of direct testing of SDSS V-type classification predictions for six objects, where the tests were performed by visible wavelength spectroscopy (Lazzaro et al. 2004, Icarus 172, 179) and target of opportunity near-infrared spectroscopy obtained using the NASA Infrared Telescope Facility (IRTF). Vesta-like spectra and a V-type taxonomy are confirmed for five of the six predicted V-type objects sampled. Most interestingly, the SDSS taxonomy correctly predicted the V-type spectral characteristics for asteroid (21238) 1995 WV7, a 6 km asteroid located far from Vesta across the 3:1 mean motion resonance at 2.54 AU. (Proper elements a,e,i: 2.54 AU, 0.14, and 10.8 deg.) Given the 2 km/sec ejection velocity required from Vesta to reach the current orbit, and the difficulty of migrating across the 3:1 resonance (at 2.5 AU) by a process such as Yarkovsky drift or via secular resonances (Carruba et al. 2005, Astron. Astrophys. 441, 819), asteroid 21238 may be a new candidate for a basaltic asteroid having no relationship to Vesta.

A risk-based classification scheme for genetically modified foods. II: Graded testing.

Science.gov (United States)

Chao, Eunice; Krewski, Daniel

2008-12-01

This paper presents a graded approach to the testing of crop-derived genetically modified (GM) foods based on concern levels in a proposed risk-based classification scheme (RBCS) and currently available testing methods. A graded approach offers the potential for more efficient use of testing resources by focusing less on lower concern GM foods, and more on higher concern foods. In this proposed approach to graded testing, products that are classified as Level I would have met baseline testing requirements that are comparable to what is widely applied to premarket assessment of GM foods at present. In most cases, Level I products would require no further testing, or very limited confirmatory analyses. For products classified as Level II or higher, additional testing would be required, depending on the type of the substance, prior dietary history, estimated exposure level, prior knowledge of toxicity of the substance, and the nature of the concern related to unintended changes in the modified food. Level III testing applies only to the assessment of toxic and antinutritional effects from intended changes and is tailored to the nature of the substance in question. Since appropriate test methods are not currently available for all effects of concern, future research to strengthen the testing of GM foods is discussed.

Spectrophotometer-Based Color Measurements

Science.gov (United States)

2017-10-24

equipment. There are several American Society for Testing and Materials ( ASTM ) chapters covering the use of spectrometers for color measurements (refs. 3...Perkin Elmer software and procedures described in ASTM chapter E308 (ref. 3). All spectral data was stored on the computer. A summary of the color...similarity, or lack thereof, between two colors (ref. 5). In this report, the Euclidean distance metric, E, is used and recommended in ASTM D2244

Image mosaicking based on feature points using color-invariant values

Science.gov (United States)

Lee, Dong-Chang; Kwon, Oh-Seol; Ko, Kyung-Woo; Lee, Ho-Young; Ha, Yeong-Ho

2008-02-01

In the field of computer vision, image mosaicking is achieved using image features, such as textures, colors, and shapes between corresponding images, or local descriptors representing neighborhoods of feature points extracted from corresponding images. However, image mosaicking based on feature points has attracted more recent attention due to the simplicity of the geometric transformation, regardless of distortion and differences in intensity generated by camera motion in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a real digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

Real-time, resource-constrained object classification on a micro-air vehicle

Science.gov (United States)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

Directory of Open Access Journals (Sweden)

Rashmi Mukherjee

2014-01-01

Full Text Available The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough scheme for chronic wound (CW evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM, were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793.

Feature-based attention is functionally distinct from relation-based attention: The double dissociation between color-based capture and color-relation-based capture of attention.

Science.gov (United States)

Du, Feng; Jiao, Jun

2016-04-01

The present study used a spatial blink task and a cuing task to examine the boundary between feature-based capture and relation-based capture. Feature-based capture occurs when distractors match the target feature such as target color. The occurrence of relation-based capture is contingent upon the feature relation between target and distractor (e.g., color relation). The results show that color distractors that match the target-nontarget color relation do not consistently capture attention when they appear outside of the attentional window, but distractors appearing outside the attentional window that match the target color consistently capture attention. In contrast, color distractors that best match the target-nontarget color relation but not the target color, are more likely to capture attention when they appear within the attentional window. Consistently, color cues that match the target-nontarget color relation produce a cuing effect when they appear within the attentional window, while target-color matched cues do not. Such a double dissociation between color-based capture and color-relation-based capture indicates functionally distinct mechanisms for these 2 types of attentional selection. This also indicates that the spatial blink task and the uninformative cuing task are measuring distinctive aspects of involuntary attention. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Integrating Globality and Locality for Robust Representation Based Classification

Directory of Open Access Journals (Sweden)

Zheng Zhang

2014-01-01

Full Text Available The representation based classification method (RBCM has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC method and collaborative representation classification (CRC method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy.

Application of Pattern Recognition Method for Color Assessment of Oriental Tobacco based on HPLC of Polyphenols

Directory of Open Access Journals (Sweden)

Dagnon S

2014-12-01

Full Text Available The color of Oriental tobaccos was organoleptically assayed, and high performance liquid chromatography (HPLC of polyphenols was performed. The major tobacco polyphenols (chlorogenic acid, its isomers, and rutin, as well as scopoletin and kaempferol-3-rutinoside were quantified. HPLC polyphenol profiles were processed by pattern recognition method (PRM, and the values of indexes of similarity (Is,% between the cultivars studied were determined. It was shown that data from organoleptic color assessment and from PRM based on HPLC profiles of polyphenols of the cultivars studied are largely compatible. Hence, PRM can be suggested as an additional tool for objective color evaluation and classification of Oriental tobacco.

7 CFR 51.3418 - Optional test for fry color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Optional test for fry color. 51.3418 Section 51.3418... color. Fry color may be determined in accordance with contract specifications by using the Munsell Color...°F or 21/2 minutes at 375°F. 5 Munsell Color Standards for Frozen French Fried Potatoes, Third...

Color Image Evaluation for Small Space Based on FA and GEP

Directory of Open Access Journals (Sweden)

Li Deng

2014-01-01

Full Text Available Aiming at the problem that color image is difficult to quantify, this paper proposes an evaluation method of color image for small space based on factor analysis (FA and gene expression programming (GEP and constructs a correlation model between color image factors and comprehensive color image. The basic color samples of small space and color images are evaluated by semantic differential method (SD method, color image factors are selected via dimension reduction in FA, factor score function is established, and by combining the entropy weight method to determine each factor weights then the comprehensive color image score is calculated finally. The best fitting function between color image factors and comprehensive color image is obtained by GEP algorithm, which can predict the users’ color image values. A color image evaluation system for small space is developed based on this model. The color evaluation of a control room on AC frequency conversion rig is taken as an example, verifying the effectiveness of the proposed method. It also can assist the designers in other color designs and provide a fast evaluation tool for testing users’ color image.

Automated classification of Permanent Scatterers time-series based on statistical characterization tests

Science.gov (United States)

Berti, Matteo; Corsini, Alessandro; Franceschini, Silvia; Iannacone, Jean Pascal

2013-04-01

The application of space borne synthetic aperture radar interferometry has progressed, over the last two decades, from the pioneer use of single interferograms for analyzing changes on the earth's surface to the development of advanced multi-interferogram techniques to analyze any sort of natural phenomena which involves movements of the ground. The success of multi-interferograms techniques in the analysis of natural hazards such as landslides and subsidence is widely documented in the scientific literature and demonstrated by the consensus among the end-users. Despite the great potential of this technique, radar interpretation of slope movements is generally based on the sole analysis of average displacement velocities, while the information embraced in multi interferogram time series is often overlooked if not completely neglected. The underuse of PS time series is probably due to the detrimental effect of residual atmospheric errors, which make the PS time series characterized by erratic, irregular fluctuations often difficult to interpret, and also to the difficulty of performing a visual, supervised analysis of the time series for a large dataset. In this work is we present a procedure for automatic classification of PS time series based on a series of statistical characterization tests. The procedure allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) and retrieve for each trend a series of descriptive parameters which can be efficiently used to characterize the temporal changes of ground motion. The classification algorithms were developed and tested using an ENVISAT datasets available in the frame of EPRS-E project (Extraordinary Plan of Environmental Remote Sensing) of the Italian Ministry of Environment (track "Modena", Northern Apennines). This dataset was generated using standard processing, then the

Color reproduction system based on color appearance model and gamut mapping

Science.gov (United States)

Cheng, Fang-Hsuan; Yang, Chih-Yuan

2000-06-01

By the progress of computer, computer peripherals such as color monitor and printer are often used to generate color image. However, cross media color reproduction by human perception is usually different. Basically, the influence factors are device calibration and characterization, viewing condition, device gamut and human psychology. In this thesis, a color reproduction system based on color appearance model and gamut mapping is proposed. It consists of four parts; device characterization, color management technique, color appearance model and gamut mapping.

Classification of Diabetic Macular Edema and Its Stages Using Color Fundus Image

Institute of Scientific and Technical Information of China (English)

Muhammad Zubair; Shoab A. Khan; Ubaid Ullah Yasin

2014-01-01

Diabetic macular edema (DME) is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. The only cure is timely diagnosis, prevention, and treatment of the disease. This paper presents an automated system for the diagnosis and classification of DME using color fundus image. In the proposed technique, first the optic disc is removed by applying some preprocessing steps. The preprocessed image is then passed through a classifier for segmentation of the image to detect exudates. The classifier uses dynamic thresholding technique by using some input parameters of the image. The stage classification is done on the basis of anearly treatment diabetic retinopathy study (ETDRS) given criteria to assess the severity of disease. The proposed technique gives a sensitivity, specificity, and accuracy of 98.27%, 96.58%, and 96.54%, respectively on publically available database.

EFFICIENT SELECTION AND CLASSIFICATION OF INFRARED EXCESS EMISSION STARS BASED ON AKARI AND 2MASS DATA

Energy Technology Data Exchange (ETDEWEB)

Huang Yafang; Li Jinzeng [National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012 (China); Rector, Travis A. [University of Alaska, 3211 Providence Drive, Anchorage, AK 99508 (United States); Mallamaci, Carlos C., E-mail: ljz@nao.cas.cn [Observatorio Astronomico Felix Aguilar, Universidad Nacional de San Juan (Argentina)

2013-05-15

The selection of young stellar objects (YSOs) based on excess emission in the infrared is easily contaminated by post-main-sequence stars and various types of emission line stars with similar properties. We define in this paper stringent criteria for an efficient selection and classification of stellar sources with infrared excess emission based on combined Two Micron All Sky Survey (2MASS) and AKARI colors. First of all, bright dwarfs and giants with known spectral types were selected from the Hipparcos Catalogue and cross-identified with the 2MASS and AKARI Point Source Catalogues to produce the main-sequence and the post-main-sequence tracks, which appear as expected as tight tracks with very small dispersion. However, several of the main-sequence stars indicate excess emission in the color space. Further investigations based on the SIMBAD data help to clarify their nature as classical Be stars, which are found to be located in a well isolated region on each of the color-color (C-C) diagrams. Several kinds of contaminants were then removed based on their distribution in the C-C diagrams. A test sample of Herbig Ae/Be stars and classical T Tauri stars were cross-identified with the 2MASS and AKARI catalogs to define the loci of YSOs with different masses on the C-C diagrams. Well classified Class I and Class II sources were taken as a second test sample to discriminate between various types of YSOs at possibly different evolutionary stages. This helped to define the loci of different types of YSOs and a set of criteria for selecting YSOs based on their colors in the near- and mid-infrared. Candidate YSOs toward IC 1396 indicating excess emission in the near-infrared were employed to verify the validity of the new source selection criteria defined based on C-C diagrams compiled with the 2MASS and AKARI data. Optical spectroscopy and spectral energy distributions of the IC 1396 sample yield a clear identification of the YSOs and further confirm the criteria defined

EFFICIENT SELECTION AND CLASSIFICATION OF INFRARED EXCESS EMISSION STARS BASED ON AKARI AND 2MASS DATA

International Nuclear Information System (INIS)

Huang Yafang; Li Jinzeng; Rector, Travis A.; Mallamaci, Carlos C.

2013-01-01

The selection of young stellar objects (YSOs) based on excess emission in the infrared is easily contaminated by post-main-sequence stars and various types of emission line stars with similar properties. We define in this paper stringent criteria for an efficient selection and classification of stellar sources with infrared excess emission based on combined Two Micron All Sky Survey (2MASS) and AKARI colors. First of all, bright dwarfs and giants with known spectral types were selected from the Hipparcos Catalogue and cross-identified with the 2MASS and AKARI Point Source Catalogues to produce the main-sequence and the post-main-sequence tracks, which appear as expected as tight tracks with very small dispersion. However, several of the main-sequence stars indicate excess emission in the color space. Further investigations based on the SIMBAD data help to clarify their nature as classical Be stars, which are found to be located in a well isolated region on each of the color-color (C-C) diagrams. Several kinds of contaminants were then removed based on their distribution in the C-C diagrams. A test sample of Herbig Ae/Be stars and classical T Tauri stars were cross-identified with the 2MASS and AKARI catalogs to define the loci of YSOs with different masses on the C-C diagrams. Well classified Class I and Class II sources were taken as a second test sample to discriminate between various types of YSOs at possibly different evolutionary stages. This helped to define the loci of different types of YSOs and a set of criteria for selecting YSOs based on their colors in the near- and mid-infrared. Candidate YSOs toward IC 1396 indicating excess emission in the near-infrared were employed to verify the validity of the new source selection criteria defined based on C-C diagrams compiled with the 2MASS and AKARI data. Optical spectroscopy and spectral energy distributions of the IC 1396 sample yield a clear identification of the YSOs and further confirm the criteria defined

A color based face detection system using multiple templates

Institute of Scientific and Technical Information of China (English)

王涛; 卜佳俊; 陈纯

2003-01-01

A color based system using multiple templates was developed and implemented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the frontal human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors. This chroma chart is used to generate, from the original color image, a gray scale image whose gray value at a pixel shows its likelihood of representing the skin. The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates matching is used to determine if a given skin region represents a frontal human face or not. Test of the system with more than 400 color images showed that the resulting detection rate was 83%, which is better than most color-based face detection systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.

Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

Directory of Open Access Journals (Sweden)

Inhye Yoon

2015-03-01

Full Text Available Since incoming light to an unmanned aerial vehicle (UAV platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i image segmentation based on geometric classes; (ii generation of the context-adaptive transmission map; and (iii intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

Science.gov (United States)

Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

2015-03-19

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Illuminant color estimation based on pigmentation separation from human skin color

Science.gov (United States)

Tanaka, Satomi; Kakinuma, Akihiro; Kamijo, Naohiro; Takahashi, Hiroshi; Tsumura, Norimichi

2015-03-01

Human has the visual system called "color constancy" that maintains the perceptive colors of same object across various light sources. The effective method of color constancy algorithm was proposed to use the human facial color in a digital color image, however, this method has wrong estimation results by the difference of individual facial colors. In this paper, we present the novel color constancy algorithm based on skin color analysis. The skin color analysis is the method to separate the skin color into the components of melanin, hemoglobin and shading. We use the stationary property of Japanese facial color, and this property is calculated from the components of melanin and hemoglobin. As a result, we achieve to propose the method to use subject's facial color in image and not depend on the individual difference among Japanese facial color.

The Classification of Race, Ethnicity, Color, or National Origin (CRECNO) Initiative:A Guide to the Projected Impacts on Californians

OpenAIRE

Michaelson, Richard; Probert, Michelle; Swearingen, Van; Wolf, Marc

2003-01-01

Californians are scheduled to vote on the Classification of Race, Ethnicity, Color, or National Origin (CRECNO) Initiative in a special recall election on October 7, 2003. If passed by voters, the initiative will amend Article I of the California Constitution effective January 1, 2005, banning the state from classifying any individual by race, ethnicity, color, or national origin, except for certain purposes or under specified circumstances. CRECNO defines "classifying" as "separating, sorti...

A kernel-based multi-feature image representation for histopathology image classification

International Nuclear Information System (INIS)

Moreno J; Caicedo J Gonzalez F

2010-01-01

This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

J Carlos Moreno

2010-09-01

Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

Differences in physical-fitness test scores between actively and passively recruited older adults : Consequences for norm-based classification

NARCIS (Netherlands)

van Heuvelen, M.J.G.; Stevens, M.; Kempen, G.I.J.M.

This study investigated differences in physical-fitness test scores between actively and passively recruited older adults and the consequences thereof for norm-based classification of individuals. Walking endurance, grip strength, hip flexibility, balance, manual dexterity, and reaction time were

Termination Criteria for Computerized Classification Testing

Directory of Open Access Journals (Sweden)

Nathan A. Thompson

2011-02-01

Full Text Available Computerized classification testing (CCT is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as - pass- and - fail.- Like adaptive testing for point estimation of ability, the key component is the termination criterion, namely the algorithm that decides whether to classify the examinee and end the test or to continue and administer another item. This paper applies a newly suggested termination criterion, the generalized likelihood ratio (GLR, to CCT. It also explores the role of the indifference region in the specification of likelihood-ratio based termination criteria, comparing the GLR to the sequential probability ratio test. Results from simulation studies suggest that the GLR is always at least as efficient as existing methods.

Interactive classification and content-based retrieval of tissue images

Science.gov (United States)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Style-based classification of Chinese ink and wash paintings

Science.gov (United States)

Sheng, Jiachuan; Jiang, Jianmin

2013-09-01

Following the fact that a large collection of ink and wash paintings (IWP) is being digitized and made available on the Internet, their automated content description, analysis, and management are attracting attention across research communities. While existing research in relevant areas is primarily focused on image processing approaches, a style-based algorithm is proposed to classify IWPs automatically by their authors. As IWPs do not have colors or even tones, the proposed algorithm applies edge detection to locate the local region and detect painting strokes to enable histogram-based feature extraction and capture of important cues to reflect the styles of different artists. Such features are then applied to drive a number of neural networks in parallel to complete the classification, and an information entropy balanced fusion is proposed to make an integrated decision for the multiple neural network classification results in which the entropy is used as a pointer to combine the global and local features. Evaluations via experiments support that the proposed algorithm achieves good performances, providing excellent potential for computerized analysis and management of IWPs.

A color based face detection system using multiple templates

Institute of Scientific and Technical Information of China (English)

王涛; 卜佳酸; 陈纯

2003-01-01

A color based system using multiple templates was developed and implemented for detecting hu-man faces in color images.The algorithm comsists of three image processing steps.The first step is human skin color statistics.Then it separates skin regions from non-skin regions.After that,it locates the frontal human face(s) within the skin regions.In the first step,250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors.This chroma chart is used to generate,from the original color image,a gray scale image whose gray value at a pixel shows its likelihood of representing the shin,The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into sep-arate skin regions from non skin regions.Finally,multiple face templates matching is used to determine if a given skin region represents a frontal human face or not.Test of the system with more than 400 color images showed that the resulting detection rate was 83%,which is better than most colou-based face detection sys-tems.The average speed for face detection is 0.8 second/image(400×300pixels) on a Pentium 3(800MHz) PC.

Generalized classification of welds according to defect type based on raidation testing results

International Nuclear Information System (INIS)

Adamenko, A.A.; Demidko, V.G.

1980-01-01

Constructed is a generalized classification of welds according to defect type, with respect to real danger of defect, which in the first approximation is proportional to relatively decrease of the thickness, and with respect to defect potential danger which can be determined by its pointing. According to this classification the welded joints are divided into five classes according to COMECON guides. The division into classes is carried out according to two-fold numerical criterium which is applicable in case of the presence of experimental data on three defect linear sizes. The above classification is of main importance while automatic data processing of the radiation testing

Content-Based Image Retrieval Benchmarking: Utilizing color categories and color distributions

NARCIS (Netherlands)

van den Broek, Egon; Kisters, Peter M.F.; Vuurpijl, Louis G.

From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color

Color vision deficiency compensation for Visual Processing Disorder using Hardy-Rand-Rittler test and color transformation

Science.gov (United States)

Balbin, Jessie R.; Pinugu, Jasmine Nadja J.; Bautista, Joshua Ian C.; Nebres, Pauline D.; Rey Hipolito, Cipriano M.; Santella, Jose Anthony A.

2017-06-01

Visual processing skill is used to gather visual information from environment however, there are cases that Visual Processing Disorder (VPD) occurs. The so called visual figure-ground discrimination is a type of VPD where color is one of the factors that contributes on this type. In line with this, color plays a vital role in everyday living, but individuals that have limited and inaccurate color perception suffers from Color Vision Deficiency (CVD) and still not aware on their case. To resolve this case, this study focuses on the design of KULAY, a Head-Mounted Display (HMD) device that can assess whether a user has a CVD or not thru the standard Hardy-Rand-Rittler (HRR) test. This test uses pattern recognition in order to evaluate the user. In addition, color vision deficiency simulation and color correction thru color transformation is also a concern of this research. This will enable people with normal color vision to know how color vision deficient perceives and vice-versa. For the accuracy of the simulated HRR assessment, its results were validated thru an actual assessment done by a doctor. Moreover, for the preciseness of color transformation, Structural Similarity Index Method (SSIM) was used to compare the simulated CVD images and the color corrected images to other reference sources. The output of the simulated HRR assessment and color transformation shows very promising results indicating effectiveness and efficiency of the study. Thus, due to its form factor and portability, this device is beneficial in the field of medicine and technology.

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Science.gov (United States)

Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

2014-03-01

This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

Multispectral colour analysis for quantitative evaluation of pseudoisochromatic color deficiency tests

Science.gov (United States)

Ozolinsh, Maris; Fomins, Sergejs

2010-11-01

Multispectral color analysis was used for spectral scanning of Ishihara and Rabkin color deficiency test book images. It was done using tunable liquid-crystal LC filters built in the Nuance II analyzer. Multispectral analysis keeps both, information on spatial content of tests and on spectral content. Images were taken in the range of 420-720nm with a 10nm step. We calculated retina neural activity charts taking into account cone sensitivity functions, and processed charts in order to find the visibility of latent symbols in color deficiency plates using cross-correlation technique. In such way the quantitative measure is found for each of diagnostics plate for three different color deficiency carrier types - protanopes, deutanopes and tritanopes. Multispectral color analysis allows to determine the CIE xyz color coordinates of pseudoisochromatic plate design elements and to perform statistical analysis of these data to compare the color quality of available color deficiency test books.

Representing Color Ensembles.

Science.gov (United States)

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-10-01

Colors are rarely uniform, yet little is known about how people represent color distributions. We introduce a new method for studying color ensembles based on intertrial learning in visual search. Participants looked for an oddly colored diamond among diamonds with colors taken from either uniform or Gaussian color distributions. On test trials, the targets had various distances in feature space from the mean of the preceding distractor color distribution. Targets on test trials therefore served as probes into probabilistic representations of distractor colors. Test-trial response times revealed a striking similarity between the physical distribution of colors and their internal representations. The results demonstrate that the visual system represents color ensembles in a more detailed way than previously thought, coding not only mean and variance but, most surprisingly, the actual shape (uniform or Gaussian) of the distribution of colors in the environment.

Evaluation of functional color vision requirements and current color vision screening tests for air traffic control specialists.

Science.gov (United States)

1990-08-01

An experiment was conducted to evaluate the relation of type and degree of color vision deficiency and aeromedical color vision screening test scores to performance of color-dependent tasks of Air Traffic Control Specialists. The subjects included 37...

Can the Farnsworth D15 Color Vision Test Be Defeated through Practice?

Science.gov (United States)

Ng, Jason S; Liem, Sophia C

2018-05-01

This study suggests that it is possible for some patients with severe red-green color vision deficiency to do perfectly on the Farnsworth D15 test after practicing it. The Farnsworth D15 is a commonly used test to qualify people for certain occupations. For patients with color vision deficiency, there may be high motivation to try to pass the test through practice to gain entry into a particular occupation. There is no evidence in the literature on whether it is possible for patients to learn to pass the D15 test through practice. Ten subjects with inherited red-green color vision deficiency and 15 color-normal subjects enrolled in the study. All subjects had anomaloscope testing, color vision book tests, and a Farnsworth D15 at an initial visit. For the D15, the number of major crossovers was determined for each subject. Failing the D15 was determined as greater than 1 major crossover. Subjects with color vision deficiency practiced the D15 as long as desired to achieve a perfect score and then returned for a second visit for D15 testing. A paired t test was used to analyze the number of major crossovers at visit 1 versus visit 2. Color-normal subjects did not have any major crossovers. Subjects with color vision deficiency had significantly (P color vision deficiency, and this should be considered in certain cases where occupational entry is dependent on D15 testing.

Locality-preserving sparse representation-based classification in hyperspectral imagery

Science.gov (United States)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern.

Science.gov (United States)

Oh, Paul; Lee, Sukho; Kang, Moon Gi

2017-06-28

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method.

Attribute-based classification for zero-shot visual object categorization.

Science.gov (United States)

Lampert, Christoph H; Nickisch, Hannes; Harmeling, Stefan

2014-03-01

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

Assessment of performance validity in the Stroop Color and Word Test in mild traumatic brain injury patients: a criterion-groups validation design.

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Guise, Brian J; Thompson, Matthew D; Greve, Kevin W; Bianchini, Kevin J; West, Laura

2014-03-01

The current study assessed performance validity on the Stroop Color and Word Test (Stroop) in mild traumatic brain injury (TBI) using criterion-groups validation. The sample consisted of 77 patients with a reported history of mild TBI. Data from 42 moderate-severe TBI and 75 non-head-injured patients with other clinical diagnoses were also examined. TBI patients were categorized on the basis of Slick, Sherman, and Iverson (1999) criteria for malingered neurocognitive dysfunction (MND). Classification accuracy is reported for three indicators (Word, Color, and Color-Word residual raw scores) from the Stroop across a range of injury severities. With false-positive rates set at approximately 5%, sensitivity was as high as 29%. The clinical implications of these findings are discussed. © 2012 The British Psychological Society.

Postpartum unconscious dynamics emerging from the Lüscher color test in Ethiopian women.

Science.gov (United States)

Zanardo, Vincenzo; Gabrieli, Catia; Volpe, Francesca; Savio, Francesca; Straface, Gianluca; Soldera, Gino

2017-06-01

The aim of this study was to explore the feasibility of the Lüscher color test (LCT), a psychological instrument based on theory that colors are selected in unconscious way and that the color sensory perception of color is objective and universal. The research has involved 24 Ethiopian women, which delivered at the Getche Health Center in Gurage. It seemed to be relevant for the majority of Ethiopian women identify the rejected color (58.66%), the gray, than the favorite color, the yellow 33.33%). The yellow color suggests that they better express their personality in a physical context, while the gray color indicates that they want to live this experience intensely. This exploratory work lays the foundations for further studies in disadvantaged women, both in developing low-income Countries as well as in industrialized Countries characterized by an high level of emigration, and for clinical applications by the complete LCT version.

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

Science.gov (United States)

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

NIM: A Node Influence Based Method for Cancer Classification

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Yiwen Wang

2014-01-01

Full Text Available The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.

A New Classification Approach Based on Multiple Classification Rules

OpenAIRE

Zhongmei Zhou

2014-01-01

A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

Tree Colors: Color Schemes for Tree-Structured Data.

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Tennekes, Martijn; de Jonge, Edwin

2014-12-01

We present a method to map tree structures to colors from the Hue-Chroma-Luminance color model, which is known for its well balanced perceptual properties. The Tree Colors method can be tuned with several parameters, whose effect on the resulting color schemes is discussed in detail. We provide a free and open source implementation with sensible parameter defaults. Categorical data are very common in statistical graphics, and often these categories form a classification tree. We evaluate applying Tree Colors to tree structured data with a survey on a large group of users from a national statistical institute. Our user study suggests that Tree Colors are useful, not only for improving node-link diagrams, but also for unveiling tree structure in non-hierarchical visualizations.

Color-Object Interference: Further Tests of an Executive Control Account

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La Heij, Wido; Boelens, Harrie

2011-01-01

Young children are slower in naming the color of a meaningful picture than in naming the color of an abstract form (Stroop-like color-object interference). The current experiments tested an executive control account of this phenomenon. First, color-object interference was observed in 6- and 8-year-olds but not in 12- and 16-year-olds (Experiment…

Column Selection for Biomedical Analysis Supported by Column Classification Based on Four Test Parameters.

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Plenis, Alina; Rekowska, Natalia; Bączek, Tomasz

2016-01-21

This article focuses on correlating the column classification obtained from the method created at the Katholieke Universiteit Leuven (KUL), with the chromatographic resolution attained in biomedical separation. In the KUL system, each column is described with four parameters, which enables estimation of the FKUL value characterising similarity of those parameters to the selected reference stationary phase. Thus, a ranking list based on the FKUL value can be calculated for the chosen reference column, then correlated with the results of the column performance test. In this study, the column performance test was based on analysis of moclobemide and its two metabolites in human plasma by liquid chromatography (LC), using 18 columns. The comparative study was performed using traditional correlation of the FKUL values with the retention parameters of the analytes describing the column performance test. In order to deepen the comparative assessment of both data sets, factor analysis (FA) was also used. The obtained results indicated that the stationary phase classes, closely related according to the KUL method, yielded comparable separation for the target substances. Therefore, the column ranking system based on the FKUL-values could be considered supportive in the choice of the appropriate column for biomedical analysis.

Failure diagnosis using deep belief learning based health state classification

International Nuclear Information System (INIS)

Tamilselvan, Prasanna; Wang, Pingfeng

2013-01-01

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

Qualitative evaluations and comparisons of six night-vision colorization methods

Science.gov (United States)

Zheng, Yufeng; Reese, Kristopher; Blasch, Erik; McManamon, Paul

2013-05-01

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test ("Ishihara Color Blindness Test") and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF colorization and for quantitative evaluation using an objective metric such as objective evaluation index

Content-based image retrieval: Color-selection exploited

NARCIS (Netherlands)

Broek, E.L. van den; Vuurpijl, L.G.; Kisters, P. M. F.; Schmid, J.C.M. von; Moens, M.F.; Busser, R. de; Hiemstra, D.; Kraaij, W.

2002-01-01

This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11

Content-Based Image Retrieval: Color-selection exploited

NARCIS (Netherlands)

Moens, Marie-Francine; van den Broek, Egon; Vuurpijl, L.G.; de Brusser, Rik; Kisters, P.M.F.; Hiemstra, Djoerd; Kraaij, Wessel; von Schmid, J.C.M.

2002-01-01

This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11

Colored cool colorants based on rare earth metal ions

Energy Technology Data Exchange (ETDEWEB)

Sreeram, Kalarical Janardhanan; Aby, Cheruvathoor Poulose; Nair, Balachandran Unni; Ramasami, Thirumalachari [Chemical Laboratory, Central Leather Research Institute, Council of Scientific and Industrial Research, Adyar, Chennai 600 020 (India)

2008-11-15

Colored pigments with high near infrared reflectance and not based on toxic metal ions like cadmium, lead and cobalt are being sought as cool colorants. Through appropriate doping two pigments Ce-Pr-Mo and Ce-Pr-Fe have been developed to offer a reddish brown and reddish orange color, respectively. These pigments have been characterized and found to be highly crystalline with an average size of 300 nm. A shift in band gap energy from 2.21 to 2.18 eV has been observed when Li{sub 2}CO{sub 3} was used as a mineralizer. Scanning electron microscope-energy dispersive X-ray analysis (SEM-EDAX) measurement indicate a uniform grind shape and distribution of metal ion, with over 65% reflectance in the NIR region, these pigments can well serve as cool colorants. (author)

Classification of solid industrial waste based on ecotoxicology tests using Daphnia magna: an alternative

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William Gerson Matias

2005-11-01

Full Text Available The adequate treatment and final disposal of solid industrial wastes depends on their classification into class I or II. This classification is proposed by NBR 10.004; however, it is complex and time-consuming. With a view to facilitating this classification, the use of assays with Daphnia magna is proposed. These assays make possible the identification of toxic chemicals in the leach, which denotes the presence of one of the characteristics described by NBR 10.004, the toxicity, which is a sufficient argument to put the waste into class I. Ecotoxicological tests were carried out with ten samples of solid wastes of frequent production and, on the basis of the results from EC(I50/48h of those samples in comparison with the official classification of NBR 10.004, limits were established for the classification of wastes into class I or II. A coincidence in the classification of 50% of the analyzed samples was observed. In cases in which there is no coherence between the methods, the method proposed in this work classifies the waste into class I. These data are preliminary, but they reveal that the classification system proposed here is promising because of its quickness and economic viability.

Deep learning for EEG-Based preference classification

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Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Asteroid taxonomic classifications

International Nuclear Information System (INIS)

Tholen, D.J.

1989-01-01

This paper reports on three taxonomic classification schemes developed and applied to the body of available color and albedo data. Asteroid taxonomic classifications according to two of these schemes are reproduced

A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

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JORGE FLORES CRUZ

2017-01-01

Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

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K. Bakuła

2016-06-01

Full Text Available Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images, spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and

Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

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Jordi Palacín

2012-06-01

Full Text Available This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

7 CFR 52.1006 - Color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Color. 52.1006 Section 52.1006 Agriculture Regulations... United States Standards for Grades of Dates Factors of Quality § 52.1006 Color. (a) (A) classification. Whole or pitted dates that possess a good color may be given a score of 18 to 20 points. “Good color...

Toward the establishment of standardized in vitro tests for lipid-based formulations, part 4: proposing a new lipid formulation performance classification system.

Science.gov (United States)

Williams, Hywel D; Sassene, Philip; Kleberg, Karen; Calderone, Marilyn; Igonin, Annabel; Jule, Eduardo; Vertommen, Jan; Blundell, Ross; Benameur, Hassan; Müllertz, Anette; Porter, Christopher J H; Pouton, Colin W

2014-08-01

The Lipid Formulation Classification System Consortium looks to develop standardized in vitro tests and to generate much-needed performance criteria for lipid-based formulations (LBFs). This article highlights the value of performing a second, more stressful digestion test to identify LBFs near a performance threshold and to facilitate lead formulation selection in instances where several LBF prototypes perform adequately under standard digestion conditions (but where further discrimination is necessary). Stressed digestion tests can be designed based on an understanding of the factors that affect LBF performance, including the degree of supersaturation generated on dispersion/digestion. Stresses evaluated included decreasing LBF concentration (↓LBF), increasing bile salt, and decreasing pH. Their capacity to stress LBFs was dependent on LBF composition and drug type: ↓LBF was a stressor to medium-chain glyceride-rich LBFs, but not more hydrophilic surfactant-rich LBFs, whereas decreasing pH stressed tolfenamic acid LBFs, but not fenofibrate LBFs. Lastly, a new Performance Classification System, that is, LBF composition independent, is proposed to promote standardized LBF comparisons, encourage robust LBF development, and facilitate dialogue with the regulatory authorities. This classification system is based on the concept that performance evaluations across three in vitro tests, designed to subject a LBF to progressively more challenging conditions, will enable effective LBF discrimination and performance grading. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Dichotomous classification of black-colored metal using spectral analysis

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Abramovich A.O.

2017-05-01

Full Text Available The task of detecting metal objects in different environments has always been important. To solve it metal detectors are used. They are designed to detect and identify objects that in their electric or magnetic properties different from the environment in which they are located. The most common among them are the metal detectors of the «detection of very low frequency» type (Very Low Frequency (VLF detectors. They use eddy current testing for detecting metal targets, which solves the problem of dichotomous distinction, that is a problem of splitting (or set into two parts (subsets: black or colored target. The target distinction is performed by a threshold level of the received signal. However, this approach does not allow to identify the type of target, if two samples of different metals are nearby. To overcome the above described limitations we propose another way of distinction based on the use of spectral analysis, which occurs in the metal detector antenna by Foucault current. We show that the problem of dichotomous distinction can be solved in just a measurement of width and area by the envelope of amplitude spectrum (hereinafter spectrum of the received signal. In this regard the laboratory model using eddy current metal detector will combat withdrawal from two samples – steel and copper, located along and calculate its range. The task of distinguishing between metal targets reduced to determining the hit spectra of reference samples obtained spectrum. The ratio between the areas is measured and reference spectra indicates the percentage of specific metals (e.g. two identical samples of different metals lying side by side. Signal processing is performed by specially designed program that compares two spectra along posted samples of black and colored metals with base.

Color Functionality Used in Visual Display for Occupational and Environmental Safety and Managing Color Vision Deficiency.

Science.gov (United States)

Ochiai, Nobuhisa; Kondo, Hiroyuki

2017-01-01

The effects of color perception are utilized in visual displays for the purpose of safety in the workplace and in daily life. These effects, generally known as color functionality, are divided into four classifications: visibility, legibility, conspicuity and discriminability. This article focuses on the relationship between the color functionality of color schemes used in visual displays for occupational and environmental safety and color vision deficiency (particularly congenital red-green color deficiency), a critical issue in ophthalmology, and examines the effects of color functionality on the perception of the color red in individuals with protan defects. Due to abrupt system reforms, current Japanese clinical ophthalmology finds itself in a situation where it is insufficiently prepared to handle congenital red-green color deficiencies. Indeed, occupational problems caused by color vision deficiencies have been almost completely neglected, and are an occupational safety and health concern that will need to be solved in the future. This report will present the guidelines for the color vision testing established by the British Health and Safety Executive (HSE), a pioneering example of a model meant to solve these problems. Issues relating to the creation of guidelines adapted to Japanese clinical ophthalmology will also be examined, and we will discuss ways to utilize color functionality used in visual displays for occupational and environmental safety to help manage color vision deficiency.

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

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Laurent Trassoudaine

2013-03-01

Full Text Available Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.

Ligand and structure-based classification models for Prediction of P-glycoprotein inhibitors

DEFF Research Database (Denmark)

Klepsch, Freya; Poongavanam, Vasanthanathan; Ecker, Gerhard Franz

2014-01-01

an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and non-inhibitors, correctly predicting 73/75 % of the external test set compounds. Classification based on the docking experiments using the scoring function Chem...

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

Science.gov (United States)

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Experimental tests for heritable morphological color plasticity in non-native brown trout (Salmo trutta populations.

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Peter A H Westley

Full Text Available The success of invasive species is frequently attributed to phenotypic plasticity, which facilitates persistence in novel environments. Here we report on experimental tests to determine whether the intensity of cryptic coloration patterns in a global invader (brown trout, Salmo trutta was primarily the result of plasticity or heritable variation. Juvenile F1 offspring were created through experimental crosses of wild-caught parents and reared for 30 days in the laboratory in a split-brood design on either light or dark-colored gravel substrate. Skin and fin coloration quantified with digital photography and image analysis indicated strong plastic effects in response to substrate color; individuals reared on dark substrate had both darker melanin-based skin color and carotenoid-based fin colors than other members of their population reared on light substrate. Slopes of skin and fin color reaction norms were parallel between environments, which is not consistent with heritable population-level plasticity to substrate color. Similarly, we observed weak differences in population-level color within an environment, again suggesting little genetic control on the intensity of skin and fin colors. Taken as whole, our results are consistent with the hypothesis that phenotypic plasticity may have facilitated the success of brown trout invasions and suggests that plasticity is the most likely explanation for the variation in color intensity observed among these populations in nature.

Organizational Data Classification Based on the Importance Concept of Complex Networks.

Science.gov (United States)

Carneiro, Murillo Guimaraes; Zhao, Liang

2017-08-01

Data classification is a common task, which can be performed by both computers and human beings. However, a fundamental difference between them can be observed: computer-based classification considers only physical features (e.g., similarity, distance, or distribution) of input data; by contrast, brain-based classification takes into account not only physical features, but also the organizational structure of data. In this paper, we figure out the data organizational structure for classification using complex networks constructed from training data. Specifically, an unlabeled instance is classified by the importance concept characterized by Google's PageRank measure of the underlying data networks. Before a test data instance is classified, a network is constructed from vector-based data set and the test instance is inserted into the network in a proper manner. To this end, we also propose a measure, called spatio-structural differential efficiency, to combine the physical and topological features of the input data. Such a method allows for the classification technique to capture a variety of data patterns using the unique importance measure. Extensive experiments demonstrate that the proposed technique has promising predictive performance on the detection of heart abnormalities.

Cross-media color reproduction using the frequency-based spatial gamut mapping algorithm based on human color vision

Science.gov (United States)

Wu, Guangyuan; Niu, Shijun; Li, Xiaozhou; Hu, Guichun

2018-04-01

Due to the increasing globalization of printing industry, remoting proofing will become the inevitable development trend. Cross-media color reproduction will occur in different color gamuts using remote proofing technologies, which usually leads to the problem of incompatible color gamut. In this paper, to achieve equivalent color reproduction between a monitor and a printer, a frequency-based spatial gamut mapping algorithm is proposed for decreasing the loss of visual color information. The design of algorithm is based on the contrast sensitivity functions (CSF), which exploited CSF spatial filter to preserve luminance of the high spatial frequencies and chrominance of the low frequencies. First we show a general framework for how to apply CSF spatial filter in retention of relevant visual information. Then we compare the proposed framework with HPMINDE, CUSP, Bala's algorithm. The psychophysical experimental results indicated the good performance of the proposed algorithm.

Different effects of color-based and location-based selection on visual working memory.

Science.gov (United States)

Li, Qi; Saiki, Jun

2015-02-01

In the present study, we investigated how feature- and location-based selection influences visual working memory (VWM) encoding and maintenance. In Experiment 1, cue type (color, location) and cue timing (precue, retro-cue) were manipulated in a change detection task. The stimuli were color-location conjunction objects, and binding memory was tested. We found a significantly greater effect for color precues than for either color retro-cues or location precues, but no difference between location pre- and retro-cues, consistent with previous studies (e.g., Griffin & Nobre in Journal of Cognitive Neuroscience, 15, 1176-1194, 2003). We also found no difference between location and color retro-cues. Experiment 2 replicated the color precue advantage with more complex color-shape-location conjunction objects. Only one retro-cue effect was different from that in Experiment 1: Color retro-cues were significantly less effective than location retro-cues in Experiment 2, which may relate to a structural property of multidimensional VWM representations. In Experiment 3, a visual search task was used, and the result of a greater location than color precue effect suggests that the color precue advantage in a memory task is related to the modulation of VWM encoding rather than of sensation and perception. Experiment 4, using a task that required only memory for individual features but not for feature bindings, further confirmed that the color precue advantage is specific to binding memory. Together, these findings reveal new aspects of the interaction between attention and VWM and provide potentially important implications for the structural properties of VWM representations.

Color planner for designers based on color emotions

Science.gov (United States)

Cheng, Ka-Man; Xin, John H.; Taylor, Gail

2002-06-01

During the color perception process, an associated feeling or emotion is induced in our brains, and this kind of emotion is termed as 'color emotion.' The researchers in the field of color emotions have put many efforts in quantifying color emotions with the standard color specifications and evaluating the influence of hue, lightness and chroma to the color emotions of human beings. In this study, a color planner was derived according to these findings so that the correlation of color emotions and standard color specifications was clearly indicated. Since people of different nationalities usually have different color emotions as different cultural and traditional backgrounds, the subjects in this study were all native Hong Kong Chinese and the color emotion words were all written in Chinese language in the visual assessments. Through the color planner, the designers from different areas, no matter fashion, graphic, interior or web site etc., can select suitable colors for inducing target color emotions to the customers or product-users since different colors convey different meanings to them. In addition, the designers can enhance the functionality and increase the attractiveness of their designed products by selecting suitable colors.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

Science.gov (United States)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

Science.gov (United States)

Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

2017-10-26

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

Dual color radiometer imagery and test results

International Nuclear Information System (INIS)

Silver, A.; Carlen, F.; Link, D.; Zegel, F.

1989-01-01

This paper presents a review of the technical characteristics of the Dual Color Radiometer and recent data and test results. The Dual Color Radiometer is a state-of-the-art device that provides simultaneous pixel to pixel registered thermal imagery in both the 3 to 5 and 8 to 12 micron regions. The device is unique in terms of its spatial and temperature resolution of less than 0.10 degrees C temperature and 0.10 milliradian spatial resolution. In addition, the device is tailored for use by the Automatic Target Recognizer (ATR) community

THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

Directory of Open Access Journals (Sweden)

J. E. Cubillas

2016-06-01

Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

"Does Hope Change? Testing a Project-Based Health Intervention among Urban Students of Color"

Science.gov (United States)

Zusevics, Kaija L.; Johnson, Sheri

2014-01-01

Hope is positively correlated with educational attainment and health. Interventions based on project-based learning (PBL) may increase youth hope. This study examined how a PBL intervention affected hope among urban students of color. Students in health classes were invited to participate. A PBL health class was implemented in four classrooms. The…

Performance Comparison of Assorted Color Spaces for Multilevel Block Truncation Coding based Face Recognition

OpenAIRE

H.B. Kekre; Sudeep Thepade; Karan Dhamejani; Sanchit Khandelwal; Adnan Azmi

2012-01-01

The paper presents a performance analysis of Multilevel Block Truncation Coding based Face Recognition among widely used color spaces. In [1], Multilevel Block Truncation Coding was applied on the RGB color space up to four levels for face recognition. Better results were obtained when the proposed technique was implemented using Kekre’s LUV (K’LUV) color space [25]. This was the motivation to test the proposed technique using assorted color spaces. For experimental analysis, two face databas...

7 CFR 51.2283 - Off color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Off color. 51.2283 Section 51.2283 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Color Requirements § 51.2283 Off color. The term “off color” is not a color classification, but shall be applied to any lot which fails to meet the...

Single underwater image enhancement based on color cast removal and visibility restoration

Science.gov (United States)

Li, Chongyi; Guo, Jichang; Wang, Bo; Cong, Runmin; Zhang, Yan; Wang, Jian

2016-05-01

Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods.

7 CFR 52.806 - Color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Color. 52.806 Section 52.806 Agriculture Regulations... United States Standards for Grades of Frozen Red Tart Pitted Cherries Factors of Quality § 52.806 Color. (a) (A) Classification. Frozen red tart pitted cherries that possess a good red color may be given a...

7 CFR 52.778 - Color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Color. 52.778 Section 52.778 Agriculture Regulations... United States Standards for Grades of Canned Red Tart Pitted Cherries 1 Factors of Quality § 52.778 Color. (a) (A) classification. Canned red tart pitted cherries that have a good color may be given a score...

Application of Bayesian Classification to Content-Based Data Management

Science.gov (United States)

Lynnes, Christopher; Berrick, S.; Gopalan, A.; Hua, X.; Shen, S.; Smith, P.; Yang, K-Y.; Wheeler, K.; Curry, C.

2004-01-01

The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

Color vision tests comparison: Farnsworth D-15 versus Lanthony D-15

Science.gov (United States)

Szmigiel, Marta; Geniusz, Malwina; Geniusz, Maciej K.

2017-09-01

Disorder of color vision in humans is the inability to perceive differences between some or all of the colors that are normally perceived by others. Color blindness is usually a birth defect, a genetically determined. For this reason it is much more common in men than women. This paper presents the results of the test FarnsworthD-15 and Lanthony D-15 on a group of volunteers, both adults and children. The study was conducted to compare the results of both tests.

Location and Classification of Moving Fruits in Real Time with a Single Color Camera Localización y Clasificación de Frutas Móviles en Tiempo Real con una Cámara Individual a Color

Directory of Open Access Journals (Sweden)

José F Reyes

2009-06-01

Full Text Available Quality control of fruits to satisfy increasingly competitive food markets requires the implementation of automatic servovisual systems in fruit processing operations to cope with market challenges. A new and fast method for identifying and classifying moving fruits by processing single color images from a static camera in real time was developed and tested. Two algorithms were combined to classify and track moving fruits on image plane using representative color features. The method allows classifying the fruit by color segmentation and estimating its position on the image plane, which provides a reliable algorithm to be implemented in robotic manipulation of fruits. To evaluate the methodology an experimental real time system simulating a conveyor belt and real fruit was used. Testing of the system indicates that with natural lighting conditions and proper calibration of the system a minimum error of 2% in classification of fruits is feasible. The methodology allows for very simple implementation, and although operational results are promising, even higher accuracy may be possible if structured illumination is used.El control de calidad en frutas y hortalizas para satisfacer mercados cada vez mas exigentes, requiere la implementación de sistemas automáticos servo visuales en operaciones de de procesamiento de frutas para responder a estos desafíos de mercado. En está trabajo se desarrollo y evaluó un nuevo método para identificar y clasificar frutas en movimiento mediante el procesamiento en tiempo real imágenes a color capturadas por una cámara individual estática. Se combinaron dos algoritmos para clasificar y rastrear frutas en movimiento en el plano de imagen utilizando aspectos representativos de color. El método permite clasificar las frutas en base a segmentación de color y estimar su posición en el plano de imagen, lo cual proporciona un algoritmo confiable para ser implementado en un brazo robótico.de manipulación de

STELLAR COLOR REGRESSION: A SPECTROSCOPY-BASED METHOD FOR COLOR CALIBRATION TO A FEW MILLIMAGNITUDE ACCURACY AND THE RECALIBRATION OF STRIPE 82

International Nuclear Information System (INIS)

Yuan, Haibo; Liu, Xiaowei; Xiang, Maosheng; Huang, Yang; Zhang, Huihua; Chen, Bingqiu

2015-01-01

In this paper we propose a spectroscopy-based stellar color regression (SCR) method to perform accurate color calibration for modern imaging surveys, taking advantage of millions of stellar spectra now available. The method is straightforward, insensitive to systematic errors in the spectroscopically determined stellar atmospheric parameters, applicable to regions that are effectively covered by spectroscopic surveys, and capable of delivering an accuracy of a few millimagnitudes for color calibration. As an illustration, we have applied the method to the Sloan Digital Sky Survey (SDSS) Stripe 82 data. With a total number of 23,759 spectroscopically targeted stars, we have mapped out the small but strongly correlated color zero-point errors present in the photometric catalog of Stripe 82, and we improve the color calibration by a factor of two to three. Our study also reveals some small but significant magnitude dependence errors in the z band for some charge-coupled devices (CCDs). Such errors are likely to be present in all the SDSS photometric data. Our results are compared with those from a completely independent test based on the intrinsic colors of red galaxies presented by Ivezić et al. The comparison, as well as other tests, shows that the SCR method has achieved a color calibration internally consistent at a level of about 5 mmag in u – g, 3 mmag in g – r, and 2 mmag in r – i and i – z. Given the power of the SCR method, we discuss briefly the potential benefits by applying the method to existing, ongoing, and upcoming imaging surveys

How redundant are redundant color adjectives? An efficiency-based analysis of color overspecification

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Paula eRubio-Fernández

2016-02-01

Full Text Available Color adjectives tend to be used redundantly in referential communication. I propose that redundant color adjectives are often intended to exploit a color contrast in the visual context and hence facilitate object identification, despite not being necessary to establish unique reference. Two language-production experiments investigated two types of factors that may affect the use of redundant color adjectives: factors related to the efficiency of color in the visual context and factors related to the semantic category of the noun. The results of Experiment 1 confirmed that people produce redundant color adjectives when color may facilitate object recognition; e.g., they do so more often in polychrome displays than in monochrome displays, and more often in English (pre-nominal position than in Spanish (post-nominal position. Redundant color adjectives are also used when color is a central property of the object category; e.g., people referred to the color of clothes more often than to the color of geometrical figures (Experiment 1, and they overspecified atypical colors more often than variable and stereotypical colors (Experiment 2. These results are relevant for pragmatic models of referential communication based on Gricean pragmatics and informativeness. An alternative analysis is proposed, which focuses on the efficiency and pertinence of color in a given referential situation.

26 CFR 1.410(b)-4 - Nondiscriminatory classification test.

Science.gov (United States)

2010-04-01

..., nature of compensation (i.e., salaried or hourly), geographic location, and similar bona fide business... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES Pension, Profit-Sharing, Stock Bonus Plans, Etc. § 1.410(b)-4 Nondiscriminatory classification test. (a) In general. A plan satisfies the nondiscriminatory classification test of...

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

Directory of Open Access Journals (Sweden)

Kohei Inoue

2017-07-01

Full Text Available A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement.

Application of In-Segment Multiple Sampling in Object-Based Classification

Directory of Open Access Journals (Sweden)

Nataša Đurić

2014-12-01

Full Text Available When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN and Support Vector Machine (SVM. Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

Classification in Astronomy: Past and Present

Science.gov (United States)

Feigelson, Eric

2012-03-01

Astronomers have always classified celestial objects. The ancient Greeks distinguished between asteros, the fixed stars, and planetos, the roving stars. The latter were associated with the Gods and, starting with Plato in his dialog Timaeus, provided the first mathematical models of celestial phenomena. Giovanni Hodierna classified nebulous objects, seen with a Galilean refractor telescope in the mid-seventeenth century into three classes: "Luminosae," "Nebulosae," and "Occultae." A century later, Charles Messier compiled a larger list of nebulae, star clusters and galaxies, but did not attempt a classification. Classification of comets was a significant enterprise in the 19th century: Alexander (1850) considered two groups based on orbit sizes, Lardner (1853) proposed three groups of orbits, and Barnard (1891) divided them into two classes based on morphology. Aside from the segmentation of the bright stars into constellations, most stellar classifications were based on colors and spectral properties. During the 1860s, the pioneering spectroscopist Angelo Secchi classified stars into five classes: white, yellow, orange, carbon stars, and emission line stars. After many debates, the stellar spectral sequence was refined by the group at Harvard into the familiar OBAFGKM spectral types, later found to be a sequence on surface temperature (Cannon 1926). The spectral classification is still being extended with recent additions of O2 hot stars (Walborn et al. 2002) and L and T brown dwarfs (Kirkpatrick 2005). Townley (1913) reviews 30 years of variable star classification, emerging with six classes with five subclasses. The modern classification of variable stars has about 80 (sub)classes, and is still under debate (Samus 2009). Shortly after his confirmation that some nebulae are external galaxies, Edwin Hubble (1926) proposed his famous bifurcated classification of galaxy morphologies with three classes: ellipticals, spirals, and irregulars. These classes are still

Assessment of Color Vision Among School Children: A Comparative Study Between The Ishihara Test and The Farnsworth D-15 Test

Directory of Open Access Journals (Sweden)

Rajesh Kishor Shrestha

2016-10-01

Full Text Available Introduction: Color vision is one of the important attribute of visual perception. The study was conducted at different schools of Kathmandu to compare the  ndings of the Ishihara Pseudoisochromatic test and the Farnsworth D-15 test.  Method: A cross-sectional study was conducted among 2120 students of four schools of Kathmandu. Assessment included visual acuity measurement, slit lamp examination of anterior segment and fundus examination with direct ophthalmoscopy. Each student was assessed with the Ishihara pseudoisochromatic test and the Farnsworth D-15 test. The Chi-square test was performed to analyse color vision defect detected by the Ishihara test and the Farnsworth D-15 test. Results: A total of 2120 students comprising of 1114 males (52.5% and 1006 females (47.5% were recruited in the study with mean age of 12.2 years (SD 2.3 years. The prevalence of color vision defect as indicated by the Ishihara was 2.6 and as indicated by the D-15 test was 2.15 in males.  Conclusion: For school color vision screening, the Ishihara color test and the Farnsworth D-15 test have equal capacity to detect congenital color vision defect and they complement each other.  Keywords: color vision; children; defect; Farnsworth D-15; Ishihara.

7 CFR 51.1447 - Fairly uniform in color.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Fairly uniform in color. 51.1447 Section 51.1447... color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ...

Green colorants based on energetic azole borates.

Science.gov (United States)

Glück, Johann; Klapötke, Thomas M; Rusan, Magdalena; Stierstorfer, Jörg

2014-11-24

The investigation of green-burning boron-based compounds as colorants in pyrotechnic formulations as alternative for barium nitrate, which is a hazard to health and to the environment, is reported. Metal-free and nitrogen-rich dihydrobis(5-aminotetrazolyl)borate salts and dihydrobis(1,3,4-triazolyl)borate salts have been synthesized and characterized by NMR spectroscopy, elemental analysis, mass spectrometry, and vibrational spectroscopy. Their thermal and energetic properties have been determined as well. Several pyrotechnic compositions using selected azolyl borate salts as green colorants were investigated. Formulations with ammonium dinitramide and ammonium nitrate as oxidizers and boron and magnesium as fuels were tested. The burn time, dominant wavelength, spectral purity, luminous intensity, and luminous efficiency as well as the thermal and energetic properties of these compositions were measured. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ICF-based classification and measurement of functioning.

Science.gov (United States)

Stucki, G; Kostanjsek, N; Ustün, B; Cieza, A

2008-09-01

If we aim towards a comprehensive understanding of human functioning and the development of comprehensive programs to optimize functioning of individuals and populations we need to develop suitable measures. The approval of the International Classification, Disability and Health (ICF) in 2001 by the 54th World Health Assembly as the first universally shared model and classification of functioning, disability and health marks, therefore an important step in the development of measurement instruments and ultimately for our understanding of functioning, disability and health. The acceptance and use of the ICF as a reference framework and classification has been facilitated by its development in a worldwide, comprehensive consensus process and the increasing evidence regarding its validity. However, the broad acceptance and use of the ICF as a reference framework and classification will also depend on the resolution of conceptual and methodological challenges relevant for the classification and measurement of functioning. This paper therefore describes first how the ICF categories can serve as building blocks for the measurement of functioning and then the current state of the development of ICF based practical tools and international standards such as the ICF Core Sets. Finally it illustrates how to map the world of measures to the ICF and vice versa and the methodological principles relevant for the transformation of information obtained with a clinical test or a patient-oriented instrument to the ICF as well as the development of ICF-based clinical and self-reported measurement instruments.

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

Directory of Open Access Journals (Sweden)

Thomas J. Lampoltshammer

2015-07-01

Full Text Available The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi- automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.

Feature-based automatic color calibration for networked camera system

Science.gov (United States)

Yamamoto, Shoji; Taki, Keisuke; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

2011-01-01

In this paper, we have developed a feature-based automatic color calibration by using an area-based detection and adaptive nonlinear regression method. Simple color matching of chartless is achieved by using the characteristic of overlapping image area with each camera. Accurate detection of common object is achieved by the area-based detection that combines MSER with SIFT. Adaptive color calibration by using the color of detected object is calculated by nonlinear regression method. This method can indicate the contribution of object's color for color calibration, and automatic selection notification for user is performed by this function. Experimental result show that the accuracy of the calibration improves gradually. It is clear that this method can endure practical use of multi-camera color calibration if an enough sample is obtained.

Stroop Color-Word Interference Test: Normative data for Spanish-speaking pediatric population.

Science.gov (United States)

Rivera, D; Morlett-Paredes, A; Peñalver Guia, A I; Irías Escher, M J; Soto-Añari, M; Aguayo Arelis, A; Rute-Pérez, S; Rodríguez-Lorenzana, A; Rodríguez-Agudelo, Y; Albaladejo-Blázquez, N; García de la Cadena, C; Ibáñez-Alfonso, J A; Rodriguez-Irizarry, W; García-Guerrero, C E; Delgado-Mejía, I D; Padilla-López, A; Vergara-Moragues, E; Barrios Nevado, M D; Saracostti Schwartzman, M; Arango-Lasprilla, J C

2017-01-01

To generate normative data for the Stroop Word-Color Interference test in Spanish-speaking pediatric populations. The sample consisted of 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Each participant was administered the Stroop Word-Color Interference test as part of a larger neuropsychological battery. The Stroop Word, Stroop Color, Stroop Word-Color, and Stroop Interference scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the analyses. The final multiple linear regression models showed main effects for age on all scores, except on Stroop Interference for Guatemala, such that scores increased linearly as a function of age. Age2 affected Stroop Word scores for all countries, Stroop Color scores for Ecuador, Mexico, Peru, and Spain; Stroop Word-Color scores for Ecuador, Mexico, and Paraguay; and Stroop Interference scores for Cuba, Guatemala, and Spain. MLPE affected Stroop Word scores for Chile, Mexico, and Puerto Rico; Stroop Color scores for Mexico, Puerto Rico, and Spain; Stroop Word-Color scores for Ecuador, Guatemala, Mexico, Puerto Rico and Spain; and Stroop-Interference scores for Ecuador, Mexico, and Spain. Sex affected Stroop Word scores for Spain, Stroop Color scores for Mexico, and Stroop Interference for Honduras. This is the largest Spanish-speaking pediatric normative study in the world, and it will allow neuropsychologists from these countries to have a more accurate approach to interpret the Stroop Word-Color Interference test in pediatric populations.

Scalable, ultra-resistant structural colors based on network metamaterials

KAUST Repository

Galinski, Henning

2017-05-05

Structural colors have drawn wide attention for their potential as a future printing technology for various applications, ranging from biomimetic tissues to adaptive camouflage materials. However, an efficient approach to realize robust colors with a scalable fabrication technique is still lacking, hampering the realization of practical applications with this platform. Here, we develop a new approach based on large-scale network metamaterials that combine dealloyed subwavelength structures at the nanoscale with lossless, ultra-thin dielectric coatings. By using theory and experiments, we show how subwavelength dielectric coatings control a mechanism of resonant light coupling with epsilon-near-zero regions generated in the metallic network, generating the formation of saturated structural colors that cover a wide portion of the spectrum. Ellipsometry measurements support the efficient observation of these colors, even at angles of 70°. The network-like architecture of these nanomaterials allows for high mechanical resistance, which is quantified in a series of nano-scratch tests. With such remarkable properties, these metastructures represent a robust design technology for real-world, large-scale commercial applications.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

Science.gov (United States)

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Carded Tow Real-Time Color Assessment: A Spectral Camera-Based System

Directory of Open Access Journals (Sweden)

Rocco Furferi

2016-08-01

Full Text Available One of the most important parameters to be controlled during the production of textile yarns obtained by mixing pre-colored fibers, is the color correspondence between the manufactured yarn and a given reference, usually provided by a designer or a customer. Obtaining yarns from raw pre-colored fibers is a complex manufacturing process entailing a number of steps such as laboratory sampling, color recipe corrections, blowing, carding and spinning. Carding process is the one devoted to transform a “fuzzy mass” of tufted fibers into a regular mass of untwisted fibers, named “tow”. During this process, unfortunately, the correspondence between the color of the tow and the target one cannot be assured, thus leading to yarns whose color differs from the one used for reference. To solve this issue, the main aim of this work is to provide a system able to perform a spectral camera-based real-time measurement of a carded tow, to assess its color correspondence with a reference carded fabric and, at the same time, to monitor the overall quality of the tow during the carding process. Tested against a number of differently colored carded fabrics, the proposed system proved its effectiveness in reliably assessing color correspondence in real-time.

Classification of right-hand grasp movement based on EMOTIV Epoc+

Science.gov (United States)

Tobing, T. A. M. L.; Prawito, Wijaya, S. K.

2017-07-01

Combinations of BCT elements for right-hand grasp movement have been obtained, providing the average value of their classification accuracy. The aim of this study is to find a suitable combination for best classification accuracy of right-hand grasp movement based on EEG headset, EMOTIV Epoc+. There are three movement classifications: grasping hand, relax, and opening hand. These classifications take advantage of Event-Related Desynchronization (ERD) phenomenon that makes it possible to differ relaxation, imagery, and movement state from each other. The combinations of elements are the usage of Independent Component Analysis (ICA), spectrum analysis by Fast Fourier Transform (FFT), maximum mu and beta power with their frequency as features, and also classifier Probabilistic Neural Network (PNN) and Radial Basis Function (RBF). The average values of classification accuracy are ± 83% for training and ± 57% for testing. To have a better understanding of the signal quality recorded by EMOTIV Epoc+, the result of classification accuracy of left or right-hand grasping movement EEG signal (provided by Physionet) also be given, i.e.± 85% for training and ± 70% for testing. The comparison of accuracy value from each combination, experiment condition, and external EEG data are provided for the purpose of value analysis of classification accuracy.

New decision support tool for acute lymphoblastic leukemia classification

Science.gov (United States)

Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

2012-03-01

In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

Color digital halftoning taking colorimetric color reproduction into account

Science.gov (United States)

Haneishi, Hideaki; Suzuki, Toshiaki; Shimoyama, Nobukatsu; Miyake, Yoichi

1996-01-01

Taking colorimetric color reproduction into account, the conventional error diffusion method is modified for color digital half-toning. Assuming that the input to a bilevel color printer is given in CIE-XYZ tristimulus values or CIE-LAB values instead of the more conventional RGB or YMC values, two modified versions based on vector operation in (1) the XYZ color space and (2) the LAB color space were tested. Experimental results show that the modified methods, especially the method using the LAB color space, resulted in better color reproduction performance than the conventional methods. Spatial artifacts that appear in the modified methods are presented and analyzed. It is also shown that the modified method (2) with a thresholding technique achieves a good spatial image quality.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

Science.gov (United States)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Content-based quality evaluation of color images: overview and proposals

Science.gov (United States)

Tremeau, Alain; Richard, Noel; Colantoni, Philippe; Fernandez-Maloigne, Christine

2003-12-01

The automatic prediction of perceived quality from image data in general, and the assessment of particular image characteristics or attributes that may need improvement in particular, becomes an increasingly important part of intelligent imaging systems. The purpose of this paper is to propose to the color imaging community in general to develop a software package available on internet to help the user to select among all these approaches which is better appropriated to a given application. The ultimate goal of this project is to propose, next to implement, an open and unified color imaging system to set up a favourable context for the evaluation and analysis of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.

AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

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CHANDRASEKAR RAVI

2017-06-01

Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

Screening tests for hazard classification of complex waste materials – Selection of methods

International Nuclear Information System (INIS)

Weltens, R.; Vanermen, G.; Tirez, K.; Robbens, J.; Deprez, K.; Michiels, L.

2012-01-01

In this study we describe the development of an alternative methodology for hazard characterization of waste materials. Such an alternative methodology for hazard assessment of complex waste materials is urgently needed, because the lack of a validated instrument leads to arbitrary hazard classification of such complex waste materials. False classification can lead to human and environmental health risks and also has important financial consequences for the waste owner. The Hazardous Waste Directive (HWD) describes the methodology for hazard classification of waste materials. For mirror entries the HWD classification is based upon the hazardous properties (H1–15) of the waste which can be assessed from the hazardous properties of individual identified waste compounds or – if not all compounds are identified – from test results of hazard assessment tests performed on the waste material itself. For the latter the HWD recommends toxicity tests that were initially designed for risk assessment of chemicals in consumer products (pharmaceuticals, cosmetics, biocides, food, etc.). These tests (often using mammals) are not designed nor suitable for the hazard characterization of waste materials. With the present study we want to contribute to the development of an alternative and transparent test strategy for hazard assessment of complex wastes that is in line with the HWD principles for waste classification. It is necessary to cope with this important shortcoming in hazardous waste classification and to demonstrate that alternative methods are available that can be used for hazard assessment of waste materials. Next, by describing the pros and cons of the available methods, and by identifying the needs for additional or further development of test methods, we hope to stimulate research efforts and development in this direction. In this paper we describe promising techniques and argument on the test selection for the pilot study that we have performed on different

The Functional Classification and Field Test Performance in Wheelchair Basketball Players.

Science.gov (United States)

Gil, Susana María; Yanci, Javier; Otero, Montserrat; Olasagasti, Jurgi; Badiola, Aduna; Bidaurrazaga-Letona, Iraia; Iturricastillo, Aitor; Granados, Cristina

2015-06-27

Wheelchair basketball players are classified in four classes based on the International Wheelchair Basketball Federation (IWBF) system of competition. Thus, the aim of the study was to ascertain if the IWBF classification, the type of injury and the wheelchair experience were related to different performance field-based tests. Thirteen basketball players undertook anthropometric measurements and performance tests (hand dynamometry, 5 m and 20 m sprints, 5 m and 20 m sprints with a ball, a T-test, a Pick-up test, a modified 10 m Yo-Yo intermittent recovery test, a maximal pass and a medicine ball throw). The IWBF class was correlated (pstaff and coaches of the teams when assessing performance of wheelchair basketball players.

Composite Techniques Based Color Image Compression

Directory of Open Access Journals (Sweden)

Zainab Ibrahim Abood

2017-03-01

Full Text Available Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S, composite wavelet technique (W and composite multi-wavelet technique (M. For the high energy sub-band of the 3rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet transform (MMM in M technique which has the highest values of energy (En and compression ratio (CR and least values of bit per pixel (bpp, time (T and rate distortion R(D. Also the values of the compression parameters of the color image are nearly the same as the average values of the compression parameters of the three bands of the same image.

Cogent Confabulation based Expert System for Segmentation and Classification of Natural Landscape Images

Directory of Open Access Journals (Sweden)

BRAOVIC, M.

2017-05-01

Full Text Available Ever since there has been an increase in the number of automatic wildfire monitoring and surveillance systems in the last few years, natural landscape images have been of great importance. In this paper we propose an expert system for fast segmentation and classification of regions on natural landscape images that is suitable for real-time applications. We focus primarily on Mediterranean landscape images since the Mediterranean area and areas with similar climate are the ones most associated with high wildfire risk. The proposed expert system is based on cogent confabulation theory and knowledge bases that contain information about local and global features, optimal color spaces suitable for classification of certain regions, and context of each class. The obtained results indicate that the proposed expert system significantly outperforms well-known classifiers that it was compared against in both accuracy and speed, and that it is effective and efficient for real-time applications. Additionally, we present a FESB MLID dataset on which we conducted our research and that we made publicly available.

[Aging of silorane- and methacrylate-based composite resins: effects on color and translucency].

Science.gov (United States)

Liu, Chang; Pan, Jie; Lin, Hong; Shen, Song

2015-10-01

To evaluate the color stability and translucency of silorane-based low shrinkage composite after in vitro aging procedures of thermal cycling and water storage respectively, and to compare with those of conventional methacrylate-based posterior composite. Three light-cured composite resins, dimethacrylate-based composite A (Filtek™ Z350), B (Filtek™ P60) and silorane-based composite C (Filtek™ P90), were tested in this study. Ten specimens (10 mm in diameter, 1 mm in height) of each composite were prepared. The ten specimens in each group were then divided into two subgroups (n = 5). One subgroup underwent thermal cycling [(5.0 ± 0.5)~(55.0 ± 1.0) °C, 10 000 cycles] and the other was stored in 37 C° distilled water for 180 days. With a spectrophotometer, the CIE L * a * b * parameters of the specimens were tested before and after artificial aging against white, medium grey and black backgrounds, respectively. △E, TP and △TP were calculated and data were analyzed using independent-samples t test and partial analysis (P composite showed color alteration above the clinically acceptable levels (△E > 3.3), and also showed higher △E with a statistically significant difference in comparison with the other composites (B and C) (P composite C showed more alteration compared with composite B (P composite underwent greater alteration with regard to color stability and translucency.

Mean colors and effective temperatures of K and M dwarfs

International Nuclear Information System (INIS)

Wing, R.F.

1983-01-01

A new compilation of the mean colors of K3 - M6 dwarfs obtained largely from the photometry of Johnson (1965) by sorting the stars he observed according to their new spectral classifications from the eight-color photometry is given in table form. For each color index, a plot was made of color vs. spectral type and a smooth curve was drawn through the data. With the new spectral types, these curves are well determined, the author claims. He recommends the use of the new tabulation of mean colors whenever classifications on the MK system are employed, in view of the substantial systematic differences with Johnson's. (G.J.P.)

Hyperspectral imaging using a color camera and its application for pathogen detection

Science.gov (United States)

Yoon, Seung-Chul; Shin, Tae-Sung; Heitschmidt, Gerald W.; Lawrence, Kurt C.; Park, Bosoon; Gamble, Gary

2015-02-01

This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image

The motion of color-charged particles as a means of testing the non-Abelian dark matter model

OpenAIRE

Dzhunushaliev, V.; Folomeev, V.; Protsenko, N.

2018-01-01

A possibility is discussed for experimental testing of the dark matter model supported by a classic non-Abelian SU(3) gauge (Yang-Mills) field. Our approach is based on the analysis of the motion of color-charged particles on the background of color electric and magnetic fields using the Wong equations. Estimating the magnitudes of the color fields near the edge of a galaxy, we employ them in obtaining the general analytic solutions to the Wong equations. Using the latter, we calculate the ma...

Water Detection Based on Color Variation

Science.gov (United States)

Rankin, Arturo L.

2012-01-01

This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

Age group classification and gender detection based on forced expiratory spirometry.

Science.gov (United States)

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

Science.gov (United States)

Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

2018-04-01

Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

Joint Probability-Based Neuronal Spike Train Classification

Directory of Open Access Journals (Sweden)

Yan Chen

2009-01-01

Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

HIV Testing Patterns among Urban YMSM of Color

Science.gov (United States)

Leonard, Noelle R.; Rajan, Sonali; Gwadz, Marya V.; Aregbesola, Temi

2014-01-01

The heightened level of risk for HIV infection among Black and Latino young men who have sex with men (YMSM) is driven by multilevel influences. Using cross-sectional data, we examined HIV testing patterns among urban YMSM of color in a high-HIV seroprevalence area (ages 16 to 21 years). Self-reported frequency of testing was high, with 42% of…

Classification of high resolution imagery based on fusion of multiscale texture features

International Nuclear Information System (INIS)

Liu, Jinxiu; Liu, Huiping; Lv, Ying; Xue, Xiaojuan

2014-01-01

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification

Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification.

Science.gov (United States)

Lati, Ran N; Filin, Sagi; Aly, Radi; Lande, Tal; Levin, Ilan; Eizenberg, Hanan

2014-07-01

Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models. © 2013 Society of Chemical Industry.

Human visual system-based color image steganography using the contourlet transform

Science.gov (United States)

Abdul, W.; Carré, P.; Gaborit, P.

2010-01-01

We present a steganographic scheme based on the contourlet transform which uses the contrast sensitivity function (CSF) to control the force of insertion of the hidden information in a perceptually uniform color space. The CIELAB color space is used as it is well suited for steganographic applications because any change in the CIELAB color space has a corresponding effect on the human visual system as is very important for steganographic schemes to be undetectable by the human visual system (HVS). The perceptual decomposition of the contourlet transform gives it a natural advantage over other decompositions as it can be molded with respect to the human perception of different frequencies in an image. The evaluation of the imperceptibility of the steganographic scheme with respect to the color perception of the HVS is done using standard methods such as the structural similarity (SSIM) and CIEDE2000. The robustness of the inserted watermark is tested against JPEG compression.

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

Science.gov (United States)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Influence of Surrounding Colors in the Illuminant-Color Mode on Color Constancy

Directory of Open Access Journals (Sweden)

Kazuho Fukuda

2011-05-01

Full Text Available On color constancy, we showed that brighter surrounding colors had greater influence than dim colors (Uchikawa, Kitazawa, MacLeod, Fukuda, 2010 APCV. Increasing luminance of a stimulus causes the change in appearance from the surface-color to the illuminant-color mode. However it is unknown whether the visual system considers such color appearance mode of surrounding colors to achieve color constancy. We investigated the influence of surrounding colors that appeared illuminant on color constancy. The stimulus was composed of a central test stimulus and surrounding six colors: bright and dim red, green and blue. The observers adjusted the chromaticity of the test stimulus to be appeared as an achromatic surface. The luminance balance of three bright surrounding colors was equalized with that of the optimal colors in three illuminant conditions, then, the luminance of one of the three bright colors was varied in the range beyond the critical luminance of color appearance mode transition. The results showed that increasing luminance of a bright surrounding color shifted the observers' achromatic setting toward its chromaticity, but this effect diminished for the surrounding color in the illuminant-color mode. These results suggest that the visual system considers color appearance mode of surrounding colors to accomplish color constancy.

Region-Based Color Image Indexing and Retrieval

DEFF Research Database (Denmark)

Kompatsiaris, Ioannis; Triantafyllou, Evangelia; Strintzis, Michael G.

2001-01-01

In this paper a region-based color image indexing and retrieval algorithm is presented. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of the regions. A new color distance is also defined for this algorithm. Based on ....... Experimental results demonstrate the performance of the algorithm. The development of an intelligent image content-based search engine for the World Wide Web is also presented, as a direct application of the presented algorithm....

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Science.gov (United States)

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Reconstructing Carotenoid-Based and Structural Coloration in Fossil Skin.

Science.gov (United States)

McNamara, Maria E; Orr, Patrick J; Kearns, Stuart L; Alcalá, Luis; Anadón, Pere; Peñalver, Enrique

2016-04-25

Evidence of original coloration in fossils provides insights into the visual communication strategies used by ancient animals and the functional evolution of coloration over time [1-7]. Hitherto, all reconstructions of the colors of reptile integument and the plumage of fossil birds and feathered dinosaurs have been of melanin-based coloration [1-6]. Extant animals also use other mechanisms for producing color [8], but these have not been identified in fossils. Here we report the first examples of carotenoid-based coloration in the fossil record, and of structural coloration in fossil integument. The fossil skin, from a 10 million-year-old colubrid snake from the Late Miocene Libros Lagerstätte (Teruel, Spain) [9, 10], preserves dermal pigment cells (chromatophores)-xanthophores, iridophores, and melanophores-in calcium phosphate. Comparison with chromatophore abundance and position in extant reptiles [11-15] indicates that the fossil snake was pale-colored in ventral regions; dorsal and lateral regions were green with brown-black and yellow-green transverse blotches. Such coloration most likely functioned in substrate matching and intraspecific signaling. Skin replicated in authigenic minerals is not uncommon in exceptionally preserved fossils [16, 17], and dermal pigment cells generate coloration in numerous reptile, amphibian, and fish taxa today [18]. Our discovery thus represents a new means by which to reconstruct the original coloration of exceptionally preserved fossil vertebrates. Copyright © 2016 Elsevier Ltd. All rights reserved.

Grading of Iris Color with an Extended Photographic Reference Set

Directory of Open Access Journals (Sweden)

Luuk Franssen

2008-01-01

Conclusion: The new method is promising to be more accurate than existing iris color classification systems in clinical situations where objective colorimetry-based systems are not available. The method may be useful to assess iris translucency and fundus reflectance as sources of variation in retinal straylight.

Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

NARCIS (Netherlands)

Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

2013-01-01

The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured

Vessel-guided airway segmentation based on voxel classification

DEFF Research Database (Denmark)

Lo, Pechin Chien Pau; Sporring, Jon; Ashraf, Haseem

2008-01-01

This paper presents a method for improving airway tree segmentation using vessel orientation information. We use the fact that an airway branch is always accompanied by an artery, with both structures having similar orientations. This work is based on a  voxel classification airway segmentation...... method proposed previously. The probability of a voxel belonging to the airway, from the voxel classification method, is augmented with an orientation similarity measure as a criterion for region growing. The orientation similarity measure of a voxel indicates how similar is the orientation...... of the surroundings of a voxel, estimated based on a tube model, is to that of a neighboring vessel. The proposed method is tested on 20 CT images from different subjects selected randomly from a lung cancer screening study. Length of the airway branches from the results of the proposed method are significantly...

Color Shift Modeling of Light-Emitting Diode Lamps in Step-Loaded Stress Testing

OpenAIRE

Cai, Miao; Yang, Daoguo; Huang, J.; Zhang, Maofen; Chen, Xianping; Liang, Caihang; Koh, S.W.; Zhang, G.Q.

2017-01-01

The color coordinate shift of light-emitting diode (LED) lamps is investigated by running three stress-loaded testing methods, namely step-up stress accelerated degradation testing, step-down stress accelerated degradation testing, and constant stress accelerated degradation testing. A power model is proposed as the statistical model of the color shift (CS) process of LED products. Consequently, a CS mechanism constant is obtained for detecting the consistency of CS mechanisms among various s...

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

Science.gov (United States)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Multi-band transmission color filters for multi-color white LEDs based visible light communication

Science.gov (United States)

Wang, Qixia; Zhu, Zhendong; Gu, Huarong; Chen, Mengzhu; Tan, Qiaofeng

2017-11-01

Light-emitting diodes (LEDs) based visible light communication (VLC) can provide license-free bands, high data rates, and high security levels, which is a promising technique that will be extensively applied in future. Multi-band transmission color filters with enough peak transmittance and suitable bandwidth play a pivotal role for boosting signal-noise-ratio in VLC systems. In this paper, multi-band transmission color filters with bandwidth of dozens nanometers are designed by a simple analytical method. Experiment results of one-dimensional (1D) and two-dimensional (2D) tri-band color filters demonstrate the effectiveness of the multi-band transmission color filters and the corresponding analytical method.

Identifying multiple influential spreaders in term of the distance-based coloring

Energy Technology Data Exchange (ETDEWEB)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Liu, Jian-Guo, E-mail: liujg004@ustc.edu.cn [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433 (China)

2016-02-22

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Identifying multiple influential spreaders in term of the distance-based coloring

International Nuclear Information System (INIS)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-01-01

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Dissimilarity-based classification of anatomical tree structures

DEFF Research Database (Denmark)

Sørensen, Lauge; Lo, Pechin Chien Pau; Dirksen, Asger

2011-01-01

A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment betw...

Dissimilarity-based classification of anatomical tree structures

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs; Lo, Pechin Chien Pau; Dirksen, Asger

2011-01-01

A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment...

Effect of different solutions on color stability of acrylic resin-based dentures

Directory of Open Access Journals (Sweden)

Marcelo Coelho Goiato

2014-01-01

Full Text Available The aim of this study was to evaluate the effect of thermocycling and immersion in mouthwash or beverage solutions on the color stability of four different acrylic resin-based dentures (Onda Cryl, OC; QC20, QC; Classico, CL; and Lucitone, LU. The factors evaluated were type of acrylic resin, immersion time, and solution (mouthwash or beverage. A total of 224 denture samples were fabricated. For each type of resin, eight samples were immersed in mouthwashes (Plax-Colgate, PC; Listerine, LI; and Oral-B, OB, beverages (coffee, CP; cola, C; and wine, W, and artificial saliva (AS; control. The color change (DE was evaluated before (baseline and after thermocycling (T1, and after immersion in solution for 1 h (T2, 3 h (T3, 24 h (T4, 48 h (T5, and 96 h (T6. The CIE Lab system was used to determine the color changes. The thermocycling test was performed for 5000 cycles. Data were submitted to three-way repeated-measures analysis of variance and Tukey's test (p < 0.05. When the samples were immersed in each mouthwash, all assessed factors, associated or not, significantly influenced the color change values, except there was no association between the mouthwash and acrylic resin. Similarly, when the samples were immersed in each beverage, all studied factors influenced the color change values. In general, regardless of the solution, LU exhibited the greatest DE values in the period from T1 to T5; and QC presented the greatest DE values at T6. Thus, thermocycling and immersion in the various solutions influenced the color stability of acrylic resins and QC showed the greatest color alteration.

Effect of different solutions on color stability of acrylic resin-based dentures.

Science.gov (United States)

Goiato, Marcelo Coelho; Nóbrega, Adhara Smith; dos Santos, Daniela Micheline; Andreotti, Agda Marobo; Moreno, Amália

2014-01-01

The aim of this study was to evaluate the effect of thermocycling and immersion in mouthwash or beverage solutions on the color stability of four different acrylic resin-based dentures (Onda Cryl, OC; QC20, QC; Classico, CL; and Lucitone, LU). The factors evaluated were type of acrylic resin, immersion time, and solution (mouthwash or beverage). A total of 224 denture samples were fabricated. For each type of resin, eight samples were immersed in mouthwashes (Plax-Colgate, PC; Listerine, LI; and Oral-B, OB), beverages (coffee, CP; cola, C; and wine, W), and artificial saliva (AS; control). The color change (DE) was evaluated before (baseline) and after thermocycling (T1), and after immersion in solution for 1 h (T2), 3 h (T3), 24 h (T4), 48 h (T5), and 96 h (T6). The CIE Lab system was used to determine the color changes. The thermocycling test was performed for 5000 cycles. Data were submitted to three-way repeated-measures analysis of variance and Tukey's test (p<0.05). When the samples were immersed in each mouthwash, all assessed factors, associated or not, significantly influenced the color change values, except there was no association between the mouthwash and acrylic resin. Similarly, when the samples were immersed in each beverage, all studied factors influenced the color change values. In general, regardless of the solution, LU exhibited the greatest DE values in the period from T1 to T5; and QC presented the greatest DE values at T6. Thus, thermocycling and immersion in the various solutions influenced the color stability of acrylic resins and QC showed the greatest color alteration.

Music Retrieval Based on the Relation between Color Association and Lyrics

Science.gov (United States)

Nakamur, Tetsuaki; Utsumi, Akira; Sakamoto, Maki

Various methods for music retrieval have been proposed. Recently, many researchers are tackling developing methods based on the relationship between music and feelings. In our previous psychological study, we found that there was a significant correlation between colors evoked from songs and colors evoked only from lyrics, and showed that the music retrieval system using lyrics could be developed. In this paper, we focus on the relationship among music, lyrics and colors, and propose a music retrieval method using colors as queries and analyzing lyrics. This method estimates colors evoked from songs by analyzing lyrics of the songs. On the first step of our method, words associated with colors are extracted from lyrics. We assumed two types of methods to extract words associated with colors. In the one of two methods, the words are extracted based on the result of a psychological experiment. In the other method, in addition to the words extracted based on the result of the psychological experiment, the words from corpora for the Latent Semantic Analysis are extracted. On the second step, colors evoked from the extracted words are compounded, and the compounded colors are regarded as those evoked from the song. On the last step, colors as queries are compared with colors estimated from lyrics, and the list of songs is presented based on similarities. We evaluated the two methods described above and found that the method based on the psychological experiment and corpora performed better than the method only based on the psychological experiment. As a result, we showed that the method using colors as queries and analyzing lyrics is effective for music retrieval.

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

Children's Book Color Preferences as Related to Their Favorite Color.

Science.gov (United States)

Locke, Jill L.

Because young children disregard writing on the spine of a book, researchers chose to run a test on color preferences in books. In a library situation young children see most books from a spine-out angle; thus when allowed to select a book by themselves, the first characteristics noticed are size and color. This study is based on the hypothesis…

Protanopia (red color-blindness) in medaka: a simple system for producing color-blind fish and testing their spectral sensitivity.

Science.gov (United States)

Homma, Noriko; Harada, Yumi; Uchikawa, Tamaki; Kamei, Yasuhiro; Fukamachi, Shoji

2017-02-06

Color perception is important for fish to survive and reproduce in nature. Visual pigments in the retinal photoreceptor cells are responsible for receiving light stimuli, but the function of the pigments in vivo has not been directly investigated in many animals due to the lack of color-blind lines and appropriate color-perception tests. In this study, we established a system for producing color-blind fish and testing their spectral sensitivity. First, we disrupted long-wavelength-sensitive (LWS) opsins of medaka (Oryzias latipes) using the CRISPR/Cas9 system to make red-color-blind lines. Single guide RNAs were designed using the consensus sequences between the paralogous LWSa and LWSb genes to simultaneously introduce double-frameshift mutations. Next, we developed a non-invasive and no-prior-learning test for spectral sensitivity by applying an optomotor response (OMR) test under an Okazaki Large Spectrograph (OLS), termed the O-O test. We constructed an electrical-rotary cylinder with black/white stripes, into which a glass aquarium containing one or more fish was placed under various monochromatic light conditions. The medaka were irradiated by the OLS every 10 nm, from wavelengths of 700 nm to 900 nm, and OMR was evaluated under each condition. We confirmed that the lws - medaka were indeed insensitive to red light (protanopia). While the control fish responded to wavelengths of up to 830 nm (λ = 830 nm), the lws - mutants responded up to λ = 740 nm; however, this difference was not observed after adaptation to dark: both the control and lws - fish could respond up to λ = 820 ~ 830 nm. These results suggest that the lws - mutants lost photopic red-cone vision, but retained scotopic rod vision. Considering that the peak absorption spectra (λ max ) of medaka LWSs are about 560 nm, but the light-adapted control medaka could respond behaviorally to light at λ = 830 nm, red-cone vision could cover an unexpectedly wide range of

Development of paper-based color test-strip for drug detection in aquatic environment: Application to oxytetracycline.

Science.gov (United States)

Gomes, Helena I A S; Sales, M Goreti F

2015-03-15

The wide use of antibiotics in aquaculture has led to the emergence of resistant microbial species. It should be avoided/minimized by controlling the amount of drug employed in fish farming. For this purpose, the present work proposes test-strip papers aiming at the detection/semi-quantitative determination of organic drugs by visual comparison of color changes, in a similar analytical procedure to that of pH monitoring by universal pH paper. This is done by establishing suitable chemical changes upon cellulose, attributing the paper the ability to react with the organic drug and to produce a color change. Quantitative data is also enabled by taking a picture and applying a suitable mathematical treatment to the color coordinates given by the HSL system used by windows. As proof of concept, this approach was applied to oxytetracycline (OXY), one of the antibiotics frequently used in aquaculture. A bottom-up modification of paper was established, starting by the reaction of the glucose moieties on the paper with 3-triethoxysilylpropylamine (APTES). The so-formed amine layer allowed binding to a metal ion by coordination chemistry, while the metal ion reacted after with the drug to produce a colored compound. The most suitable metals to carry out such modification were selected by bulk studies, and the several stages of the paper modification were optimized to produce an intense color change against the concentration of the drug. The paper strips were applied to the analysis of spiked environmental water, allowing a quantitative determination for OXY concentrations as low as 30ng/mL. In general, this work provided a simple, method to screen and discriminate tetracycline drugs, in aquaculture, being a promising tool for local, quick and cheap monitoring of drugs. Copyright © 2014 Elsevier B.V. All rights reserved.

7 CFR 51.1436 - Color classifications.

Science.gov (United States)

2010-01-01

... grade. (1) “Light” means that the kernel is mostly golden color or lighter, with not more than 25 percent of the surface darker than golden, and none of the surface darker than light brown. (2) “Light amber” means that the kernel has more than 25 percent of its surface light brown, but not more than 25...

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

Science.gov (United States)

Medina, José M; Díaz, José A; Vukusic, Pete

2015-04-20

Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

Performances on Symbol Digit Modalities Test, Color Trails Test, and modified Stroop test in a healthy, elderly Danish sample

DEFF Research Database (Denmark)

Vogel, Asmus; Stokholm, Jette; Jørgensen, Kasper

2013-01-01

This study presents Danish data for the Symbol Digit Modalities Test (SDMT), Color Trails Test (CTT), and a modified Stroop test from 100 subjects aged 60-87 years. Among the included demographic variables, age had the highest impact on test performances. Thus, the study presents separate data...

Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

OpenAIRE

Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

2017-01-01

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

Event-Based Color Segmentation With a High Dynamic Range Sensor

Directory of Open Access Journals (Sweden)

Alexandre Marcireau

2018-04-01

Full Text Available This paper introduces a color asynchronous neuromorphic event-based camera and a methodology to process color output from the device to perform color segmentation and tracking at the native temporal resolution of the sensor (down to one microsecond. Our color vision sensor prototype is a combination of three Asynchronous Time-based Image Sensors, sensitive to absolute color information. We devise a color processing algorithm leveraging this information. It is designed to be computationally cheap, thus showing how low level processing benefits from asynchronous acquisition and high temporal resolution data. The resulting color segmentation and tracking performance is assessed both with an indoor controlled scene and two outdoor uncontrolled scenes. The tracking's mean error to the ground truth for the objects of the outdoor scenes ranges from two to twenty pixels.

Spectral colors capture and reproduction based on digital camera

Science.gov (United States)

Chen, Defen; Huang, Qingmei; Li, Wei; Lu, Yang

2018-01-01

The purpose of this work is to develop a method for the accurate reproduction of the spectral colors captured by digital camera. The spectral colors being the purest color in any hue, are difficult to reproduce without distortion on digital devices. In this paper, we attempt to achieve accurate hue reproduction of the spectral colors by focusing on two steps of color correction: the capture of the spectral colors and the color characterization of digital camera. Hence it determines the relationship among the spectral color wavelength, the RGB color space of the digital camera device and the CIEXYZ color space. This study also provides a basis for further studies related to the color spectral reproduction on digital devices. In this paper, methods such as wavelength calibration of the spectral colors and digital camera characterization were utilized. The spectrum was obtained through the grating spectroscopy system. A photo of a clear and reliable primary spectrum was taken by adjusting the relative parameters of the digital camera, from which the RGB values of color spectrum was extracted in 1040 equally-divided locations. Calculated using grating equation and measured by the spectrophotometer, two wavelength values were obtained from each location. The polynomial fitting method for the camera characterization was used to achieve color correction. After wavelength calibration, the maximum error between the two sets of wavelengths is 4.38nm. According to the polynomial fitting method, the average color difference of test samples is 3.76. This has satisfied the application needs of the spectral colors in digital devices such as display and transmission.

Tongue Images Classification Based on Constrained High Dispersal Network

Directory of Open Access Journals (Sweden)

Dan Meng

2017-01-01

Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

Cloud field classification based on textural features

Science.gov (United States)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes

Modeling human color categorization: Color discrimination and color memory

OpenAIRE

Heskes, T.; van den Broek, Egon; Lucas, P.; Hendriks, Maria A.; Vuurpijl, L.G.; Puts, M.J.H.; Wiegerinck, W.

2003-01-01

Color matching in Content-Based Image Retrieval is done using a color space and measuring distances between colors. Such an approach yields non-intuitive results for the user. We introduce color categories (or focal colors), determine that they are valid, and use them in two experiments. The experiments conducted prove the difference between color categorization by the cognitive processes color discrimination and color memory. In addition, they yield a Color Look-Up Table, which can improve c...

Automatic parquet block sorting using real-time spectral classification

Science.gov (United States)

Astrom, Anders; Astrand, Erik; Johansson, Magnus

1999-03-01

This paper presents a real-time spectral classification system based on the PGP spectrograph and a smart image sensor. The PGP is a spectrograph which extracts the spectral information from a scene and projects the information on an image sensor, which is a method often referred to as Imaging Spectroscopy. The classification is based on linear models and categorizes a number of pixels along a line. Previous systems adopting this method have used standard sensors, which often resulted in poor performance. The new system, however, is based on a patented near-sensor classification method, which exploits analogue features on the smart image sensor. The method reduces the enormous amount of data to be processed at an early stage, thus making true real-time spectral classification possible. The system has been evaluated on hardwood parquet boards showing very good results. The color defects considered in the experiments were blue stain, white sapwood, yellow decay and red decay. In addition to these four defect classes, a reference class was used to indicate correct surface color. The system calculates a statistical measure for each parquet block, giving the pixel defect percentage. The patented method makes it possible to run at very high speeds with a high spectral discrimination ability. Using a powerful illuminator, the system can run with a line frequency exceeding 2000 line/s. This opens up the possibility to maintain high production speed and still measure with good resolution.

Human wound photogrammetry with low-cost hardware based on automatic calibration of geometry and color

Science.gov (United States)

Jose, Abin; Haak, Daniel; Jonas, Stephan; Brandenburg, Vincent; Deserno, Thomas M.

2015-03-01

Photographic documentation and image-based wound assessment is frequently performed in medical diagnostics, patient care, and clinical research. To support quantitative assessment, photographic imaging is based on expensive and high-quality hardware and still needs appropriate registration and calibration. Using inexpensive consumer hardware such as smartphone-integrated cameras, calibration of geometry, color, and contrast is challenging. Some methods involve color calibration using a reference pattern such as a standard color card, which is located manually in the photographs. In this paper, we adopt the lattice detection algorithm by Park et al. from real world to medicine. At first, the algorithm extracts and clusters feature points according to their local intensity patterns. Groups of similar points are fed into a selection process, which tests for suitability as a lattice grid. The group which describes the largest probability of the meshes of a lattice is selected and from it a template for an initial lattice cell is extracted. Then, a Markov random field is modeled. Using the mean-shift belief propagation, the detection of the 2D lattice is solved iteratively as a spatial tracking problem. Least-squares geometric calibration of projective distortions and non-linear color calibration in RGB space is supported by 35 corner points of 24 color patches, respectively. The method is tested on 37 photographs taken from the German Calciphylaxis registry, where non-standardized photographic documentation is collected nationwide from all contributing trial sites. In all images, the reference card location is correctly identified. At least, 28 out of 35 lattice points were detected, outperforming the SIFT-based approach previously applied. Based on these coordinates, robust geometry and color registration is performed making the photographs comparable for quantitative analysis.

Stamp Detection in Color Document Images

DEFF Research Database (Denmark)

Micenkova, Barbora; van Beusekom, Joost

2011-01-01

, moreover, it can be imprinted with a variable quality and rotation. Previous methods were restricted to detection of stamps of particular shapes or colors. The method presented in the paper includes segmentation of the image by color clustering and subsequent classification of candidate solutions...... by geometrical and color-related features. The approach allows for differentiation of stamps from other color objects in the document such as logos or texts. For the purpose of evaluation, a data set of 400 document images has been collected, annotated and made public. With the proposed method, recall of 83...

Validity of the Worth 4 Dot Test in Patients with Red-Green Color Vision Defect.

Science.gov (United States)

Bak, Eunoo; Yang, Hee Kyung; Hwang, Jeong-Min

2017-05-01

The Worth four dot test uses red and green glasses for binocular dissociation, and although it has been believed that patients with red-green color vision defects cannot accurately perform the Worth four dot test, this has not been validated. Therefore, the purpose of this study was to demonstrate the validity of the Worth four dot test in patients with congenital red-green color vision defects who have normal or abnormal binocular vision. A retrospective review of medical records was performed on 30 consecutive congenital red-green color vision defect patients who underwent the Worth four dot test. The type of color vision anomaly was determined by the Hardy Rand and Rittler (HRR) pseudoisochromatic plate test, Ishihara color test, anomaloscope, and/or the 100 hue test. All patients underwent a complete ophthalmologic examination. Binocular sensory status was evaluated with the Worth four dot test and Randot stereotest. The results were interpreted according to the presence of strabismus or amblyopia. Among the 30 patients, 24 had normal visual acuity without strabismus nor amblyopia and 6 patients had strabismus and/or amblyopia. The 24 patients without strabismus nor amblyopia all showed binocular fusional responses by seeing four dots of the Worth four dot test. Meanwhile, the six patients with strabismus or amblyopia showed various results of fusion, suppression, and diplopia. Congenital red-green color vision defect patients of different types and variable degree of binocularity could successfully perform the Worth four dot test. They showed reliable results that were in accordance with their estimated binocular sensory status.

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features.

Science.gov (United States)

Drover, Dylan; Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-06-07

Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best "classifier model-feature selector" combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew's Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model-feature selector combinations.

Determination of the impact of RGB points cloud attribute quality on color-based segmentation process

Directory of Open Access Journals (Sweden)

Bartłomiej Kraszewski

2015-06-01

Full Text Available The article presents the results of research on the effect that radiometric quality of point cloud RGB attributes have on color-based segmentation. In the research, a point cloud with a resolution of 5 mm, received from FAROARO Photon 120 scanner, described the fragment of an office’s room and color images were taken by various digital cameras. The images were acquired by SLR Nikon D3X, and SLR Canon D200 integrated with the laser scanner, compact camera Panasonic TZ-30 and a mobile phone digital camera. Color information from images was spatially related to point cloud in FAROARO Scene software. The color-based segmentation of testing data was performed with the use of a developed application named “RGB Segmentation”. The application was based on public Point Cloud Libraries (PCL and allowed to extract subsets of points fulfilling the criteria of segmentation from the source point cloud using region growing method.Using the developed application, the segmentation of four tested point clouds containing different RGB attributes from various images was performed. Evaluation of segmentation process was performed based on comparison of segments acquired using the developed application and extracted manually by an operator. The following items were compared: the number of obtained segments, the number of correctly identified objects and the correctness of segmentation process. The best correctness of segmentation and most identified objects were obtained using the data with RGB attribute from Nikon D3X images. Based on the results it was found that quality of RGB attributes of point cloud had impact only on the number of identified objects. In case of correctness of the segmentation, as well as its error no apparent relationship between the quality of color information and the result of the process was found.[b]Keywords[/b]: terrestrial laser scanning, color-based segmentation, RGB attribute, region growing method, digital images, points cloud

Modeling human color categorization: Color discrimination and color memory

NARCIS (Netherlands)

Heskes, T.; van den Broek, Egon; Lucas, P.; Hendriks, Maria A.; Vuurpijl, L.G.; Puts, M.J.H.; Wiegerinck, W.

2003-01-01

Color matching in Content-Based Image Retrieval is done using a color space and measuring distances between colors. Such an approach yields non-intuitive results for the user. We introduce color categories (or focal colors), determine that they are valid, and use them in two experiments. The

Color categories and color appearance

Science.gov (United States)

Webster, Michael A.; Kay, Paul

2011-01-01

We examined categorical effects in color appearance in two tasks, which in part differed in the extent to which color naming was explicitly required for the response. In one, we measured the effects of color differences on perceptual grouping for hues that spanned the blue–green boundary, to test whether chromatic differences across the boundary were perceptually exaggerated. This task did not require overt judgments of the perceived colors, and the tendency to group showed only a weak and inconsistent categorical bias. In a second case, we analyzed results from two prior studies of hue scaling of chromatic stimuli (De Valois, De Valois, Switkes, & Mahon, 1997; Malkoc, Kay, & Webster, 2005), to test whether color appearance changed more rapidly around the blue–green boundary. In this task observers directly judge the perceived color of the stimuli and these judgments tended to show much stronger categorical effects. The differences between these tasks could arise either because different signals mediate color grouping and color appearance, or because linguistic categories might differentially intrude on the response to color and/or on the perception of color. Our results suggest that the interaction between language and color processing may be highly dependent on the specific task and cognitive demands and strategies of the observer, and also highlight pronounced individual differences in the tendency to exhibit categorical responses. PMID:22176751

Luminance contours can gate afterimage colors and "real" colors.

Science.gov (United States)

Anstis, Stuart; Vergeer, Mark; Van Lier, Rob

2012-09-06

It has long been known that colored images may elicit afterimages in complementary colors. We have already shown (Van Lier, Vergeer, & Anstis, 2009) that one and the same adapting image may result in different afterimage colors, depending on the test contours presented after the colored image. The color of the afterimage depends on two adapting colors, those both inside and outside the test. Here, we further explore this phenomenon and show that the color-contour interactions shown for afterimage colors also occur for "real" colors. We argue that similar mechanisms apply for both types of stimulation.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation

Energy Technology Data Exchange (ETDEWEB)

Cong, Rui, E-mail: congrui2684@163.com; Li, Jing, E-mail: lijing@sj-hospital.org; Guo, Song, E-mail: 21751735@qq.com

2017-02-15

Highlights: • Qualitative SWE classification proposed here was significantly better than quantitative SWE parameters. • Qualitative classification proposed here was better than the classification proposed before. • Qualitative classification proposed here could obtain higher specificity without a loss of sensitivity. - Abstract: Objectives: To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. Methods: From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. Results: With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P < 0.05). When applying Qual1

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation

International Nuclear Information System (INIS)

Cong, Rui; Li, Jing; Guo, Song

2017-01-01

Highlights: • Qualitative SWE classification proposed here was significantly better than quantitative SWE parameters. • Qualitative classification proposed here was better than the classification proposed before. • Qualitative classification proposed here could obtain higher specificity without a loss of sensitivity. - Abstract: Objectives: To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. Methods: From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. Results: With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P < 0.05). When applying Qual1

A texton-based approach for the classification of lung parenchyma in CT images

DEFF Research Database (Denmark)

Gangeh, Mehrdad J.; Sørensen, Lauge; Shaker, Saher B.

2010-01-01

In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated...... regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance...

Calculation of color difference and measurement of the spectrum of aerosol based on human visual system

Science.gov (United States)

Dai, Mengyan; Liu, Jianghai; Cui, Jianlin; Chen, Chunsheng; Jia, Peng

2017-10-01

In order to solve the problem of the quantitative test of spectrum and color of aerosol, the measurement method of spectrum of aerosol based on human visual system was proposed. The spectrum characteristics and color parameters of three different aerosols were tested, and the color differences were calculated according to the CIE1976-L*a*b* color difference formula. Three tested powders (No 1# No 2# and No 3# ) were dispersed in a plexglass box and turned into aerosol. The powder sample was released by an injector with different dosages in each experiment. The spectrum and color of aerosol were measured by the PRO 6500 Fiber Optic Spectrometer. The experimental results showed that the extinction performance of aerosol became stronger and stronger with the increase of concentration of aerosol. While the chromaticity value differences of aerosols in the experiment were so small, luminance was verified to be the main influence factor of human eye visual perception and contributed most in the three factors of the color difference calculation. The extinction effect of No 3# aerosol was the strongest of all and caused the biggest change of luminance and color difference which would arouse the strongest human visual perception. According to the sensation level of chromatic color by Chinese, recognition color difference would be produced when the dosage of No 1# powder was more than 0.10 gram, the dosage of No 2# powder was more than 0.15 gram, and the dosage of No 3# powder was more than 0.05 gram.

AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

Directory of Open Access Journals (Sweden)

H. Ding

2016-06-01

Full Text Available Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM. In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

Science.gov (United States)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

TEXT CLASSIFICATION USING NAIVE BAYES UPDATEABLE ALGORITHM IN SBMPTN TEST QUESTIONS

Directory of Open Access Journals (Sweden)

Ristu Saptono

2017-01-01

Full Text Available Document classification is a growing interest in the research of text mining. Classification can be done based on the topics, languages, and so on. This study was conducted to determine how Naive Bayes Updateable performs in classifying the SBMPTN exam questions based on its theme. Increment model of one classification algorithm often used in text classification Naive Bayes classifier has the ability to learn from new data introduces with the system even after the classifier has been produced with the existing data. Naive Bayes Classifier classifies the exam questions based on the theme of the field of study by analyzing keywords that appear on the exam questions. One of feature selection method DF-Thresholding is implemented for improving the classification performance. Evaluation of the classification with Naive Bayes classifier algorithm produces 84,61% accuracy.

Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Science.gov (United States)

Ardha Aryaguna, Prama; Danoedoro, Projo

2016-11-01

Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, that's why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest.

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Directory of Open Access Journals (Sweden)

Xinzheng Zhang

2016-09-01

Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

A Fuzzy Logic-Based Video Subtitle and Caption Coloring System

Directory of Open Access Journals (Sweden)

Mohsen Davoudi

2012-01-01

Full Text Available An approach has been proposed for automatic adaptive subtitle coloring using fuzzy logic-based algorithm. This system changes the color of the video subtitle/caption to “pleasant” color according to color harmony and the visual perception of the image background colors. In the fuzzy analyzer unit, using RGB histograms of background image, the R, G, and B values for the color of the subtitle/caption are computed using fixed fuzzy IF-THEN rules fully driven from the color harmony theories to satisfy complementary color and subtitle-background color harmony conditions. A real-time hardware structure has been proposed for implementation of the front-end processing unit as well as the fuzzy analyzer unit.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.

Science.gov (United States)

Wang, Lei; Pedersen, Peder C; Agu, Emmanuel; Strong, Diane M; Tulu, Bengisu

2017-09-01

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

How "implicit" are implicit color effects in memory?

Science.gov (United States)

Zimmer, Hubert D; Steiner, Astrid; Ecker, Ullrich K H

2002-01-01

Processing colored pictures of objects results in a preference to choose the former color for a specific object in a subsequent color choice test (Wippich & Mecklenbräuker, 1998). We tested whether this implicit memory effect is independent of performances in episodic color recollection (recognition). In the study phase of Experiment 1, the color of line drawings was either named or its appropriateness was judged. We found only weak implicit memory effects for categorical color information. In Experiment 2, silhouettes were colored by subjects during the study phase. Performances in both the implicit and the explicit test were good. Selections of "old" colors in the implicit test, though, were almost completely confined to items for which the color was also remembered explicitly. In Experiment 3, we applied the opposition technique in order to check whether we could find any implicit effects regarding items for which no explicit color recollection was possible. This was not the case. We therefore draw the conclusion that implicit color preference effects are not independent of explicit recollection, and that they are probably based on the same episodic memory traces that are used in explicit tests.

A Fuzzy Color-Based Approach for Understanding Animated Movies Content in the Indexing Task

Directory of Open Access Journals (Sweden)

Vasile Buzuloiu

2008-04-01

Full Text Available This paper proposes a method for detecting and analyzing the color techniques used in the animated movies. Each animated movie uses a specific color palette which makes its color distribution one major feature in analyzing the movie content. The color palette is specially tuned by the author in order to convey certain feelings or to express artistic concepts. Deriving semantic or symbolic information from the color concepts or the visual impression induced by the movie should be an ideal way of accessing its content in a content-based retrieval system. The proposed approach is carried out in two steps. The first processing step is the low-level analysis. The movie color content gets represented with several global statistical parameters computed from the movie global weighted color histogram. The second step is the symbolic representation of the movie content. The numerical parameters obtained from the first step are converted into meaningful linguistic concepts through a fuzzy system. They concern mainly the predominant hues of the movie, some of Itten’s color contrasts and harmony schemes, color relationships and color richness. We use the proposed linguistic concepts to link to given animated movies according to their color techniques. In order to make the retrieval task easier, we also propose to represent color properties in a graphical manner which is similar to the color gamut representation. Several tests have been conducted on an animated movie database.

Decoding color responses in human visual cortex

International Nuclear Information System (INIS)

Kuriki, Ichiro; Matsumiya, Kazumichi; Shioiri, Satoshi; Nakamura, Shingo; Sun, Pei; Ueno, Kenichi; Tanaka, Keiji; Cheng, Kang

2011-01-01

Color percept is a subjective experience and, in general, it is impossible for other people to tell someone's color percept. The present study demonstrated that the simple image-classification analysis of brain activity obtained by a functional magnetic resonance imaging (fMRI) technique enables to tell which of four colors the subject is looking at. Our results also imply that color information is coded by the responses of hue-selective neurons in human brain, not by the combinations of red-green and blue-yellow hue components. (author)

Validity of FAA-approved color vision tests for class II and class III aeromedical screening.

Science.gov (United States)

1993-09-01

All clinical color vision tests currently used in the medical examination of pilots were studied regarding validity for prediction of performance on practical tests of ability to discriminate the aviation signal colors, red, green, and white given un...

Experimental Study on Color Durability of Color Asphalt Pavement

Science.gov (United States)

Ning, Shi; Huan, Su

2017-06-01

Aiming at the poor Color durability and the lack of research on Color asphalt pavement, spraying an anti-tire trace seal resin emulsion on the surface, a Color durable asphalt pavement was proposed. After long-term rolling and long-term aging test, the Color durability was evaluated by RGB function in Photoshop and trace residue rate formula. Test results proved that the Evaluation method was simple and effective. After long-term rolling, the Color of the road surface tends to a constant value. Spraying the emulsion on the road surface can resist tire traces. After long-term aging test, the resistance to tire traces was increased by 26.6% compared with the conventional type, while the former was 44.1% higher than the latter without long-term aging. The Color durable asphalt pavement can effectively improve the ability of Color asphalt pavement to resist tire traces, and significantly improve the Color durability of Color asphalt pavement.

Inventory classification based on decoupling points

Directory of Open Access Journals (Sweden)

Joakim Wikner

2015-01-01

Full Text Available The ideal state of continuous one-piece flow may never be achieved. Still the logistics manager can improve the flow by carefully positioning inventory to buffer against variations. Strategies such as lean, postponement, mass customization, and outsourcing all rely on strategic positioning of decoupling points to separate forecast-driven from customer-order-driven flows. Planning and scheduling of the flow are also based on classification of decoupling points as master scheduled or not. A comprehensive classification scheme for these types of decoupling points is introduced. The approach rests on identification of flows as being either demand based or supply based. The demand or supply is then combined with exogenous factors, classified as independent, or endogenous factors, classified as dependent. As a result, eight types of strategic as well as tactical decoupling points are identified resulting in a process-based framework for inventory classification that can be used for flow design.

Hunter versus CIE color measurement systems for analysis of milk-based beverages.

Science.gov (United States)

Cheng, Ni; Barbano, David M; Drake, Mary Anne

2018-06-01

The objective of our work was to determine the differences in sensitivity of Hunter and International Commission on Illumination (CIE) methods at 2 different viewer angles (2 and 10°) for measurement of whiteness, red/green, and blue/yellow color of milk-based beverages over a range of composition. Sixty combinations of milk-based beverages were formulated (2 replicates) with a range of fat level from 0.2 to 2%, true protein level from 3 to 5%, and casein as a percent of true protein from 5 to 80% to provide a wide range of milk-based beverage color. In addition, commercial skim, 1 and 2% fat high-temperature, short-time pasteurized fluid milks were analyzed. All beverage formulations were HTST pasteurized and cooled to 4°C before analysis. Color measurement viewer angle (2 vs. 10°) had very little effect on objective color measures of milk-based beverages with a wide range of composition for either the Hunter or CIE color measurement system. Temperature (4, 20, and 50°C) of color measurement had a large effect on the results of color measurement in both the Hunter and CIE measurement systems. The effect of milk beverage temperature on color measurement results was the largest for skim milk and the least for 2% fat milk. This highlights the need for proper control of beverage serving temperature for sensory panel analysis of milk-based beverages with very low fat content and for control of milk temperature when doing objective color analysis for quality control in manufacture of milk-based beverages. The Hunter system of color measurement was more sensitive to differences in whiteness among milk-based beverages than the CIE system, whereas the CIE system was much more sensitive to differences in yellowness among milk-based beverages. There was little difference between the Hunter and CIE system in sensitivity to green/red color of milk-based beverages. In defining milk-based beverage product specifications for objective color measures for dairy product

Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

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Man Ding

2010-01-01

Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

A new classification scheme of plastic wastes based upon recycling labels

Energy Technology Data Exchange (ETDEWEB)

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Ergin, Semih, E-mail: sergin@ogu.edu.tr [Electrical Electronics Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işık, Şahin, E-mail: sahini@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işıklı, İdil, E-mail: idil.isikli@bilecik.edu.tr [Electrical Electronics Engineering Dept., Bilecik University, 11210 Bilecik (Turkey)

2015-01-15

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

A new classification scheme of plastic wastes based upon recycling labels

International Nuclear Information System (INIS)

Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, İdil

2015-01-01

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Sentiment classification technology based on Markov logic networks

Science.gov (United States)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Standard test method for measurement of light reflectance value and small color differences between pieces of ceramic tile

CERN Document Server

American Society for Testing and Materials. Philadelphia

2007-01-01

1.1 This test method covers the measurement of Light Reflectance Value (LRV) and visually small color difference between pieces of glazed or unglazed ceramic tile, using any spectrophotometer that meets the requirements specified in the test method. LRV and the magnitude and direction of the color difference are expressed numerically, with sufficient accuracy for use in product specification. 1.2 LRV may be measured for either solid-colored tile or tile having a multicolored, speckled, or textured surface. For tile that are not solid-colored, an average reading should be obtained from multiple measurements taken in a pattern representative of the overall sample as described in 9.2 of this test method. Small color difference between tiles should only be measured for solid-color tiles. Small color difference between tile that have a multicolored, speckled, or textured surface, are not valid. 1.3 For solid colored tile, a comparison of the test specimen and reference specimen should be made under incandescent, f...

Two-out-of-two color matching based visual cryptography schemes.

Science.gov (United States)

Machizaud, Jacques; Fournel, Thierry

2012-09-24

Visual cryptography which consists in sharing a secret message between transparencies has been extended to color prints. In this paper, we propose a new visual cryptography scheme based on color matching. The stacked printed media reveal a uniformly colored message decoded by the human visual system. In contrast with the previous color visual cryptography schemes, the proposed one enables to share images without pixel expansion and to detect a forgery as the color of the message is kept secret. In order to correctly print the colors on the media and to increase the security of the scheme, we use spectral models developed for color reproduction describing printed colors from an optical point of view.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

Science.gov (United States)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Mechanism-based drug exposure classification in pharmacoepidemiological studies

NARCIS (Netherlands)

Verdel, B.M.

2010-01-01

Mechanism-based classification of drug exposure in pharmacoepidemiological studies In pharmacoepidemiology and pharmacovigilance, the relation between drug exposure and clinical outcomes is crucial. Exposure classification in pharmacoepidemiological studies is traditionally based on

EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

Directory of Open Access Journals (Sweden)

S. Arivazhagan

2011-11-01

Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Broader color gamut of color-modulating optical coating display based on indium tin oxide and phase change materials.

Science.gov (United States)

Ni, Zhigang; Mou, Shenghong; Zhou, Tong; Cheng, Zhiyuan

2018-05-01

A color-modulating optical coating display based on phase change materials (PCM) and indium tin oxide (ITO) is fabricated and analyzed. We demonstrate that altering the thickness of top-ITO in this PCM-based display device can effectively change color. The significant role of the top-ITO layer in the thin-film interference in this multilayer system is confirmed by experiment as well as simulation. The ternary-color modulation of devices with only 5 nano thin layer of phase change material is achieved. Furthermore, simulation work demonstrates that a stirringly broader color gamut can be obtained by introducing the control of the top-ITO thickness.

Color Categories and Color Appearance

Science.gov (United States)

Webster, Michael A.; Kay, Paul

2012-01-01

We examined categorical effects in color appearance in two tasks, which in part differed in the extent to which color naming was explicitly required for the response. In one, we measured the effects of color differences on perceptual grouping for hues that spanned the blue-green boundary, to test whether chromatic differences across the boundary…

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels

Science.gov (United States)

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels.

Science.gov (United States)

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

Real reproduction and evaluation of color based on BRDF method

Science.gov (United States)

Qin, Feng; Yang, Weiping; Yang, Jia; Li, Hongning; Luo, Yanlin; Long, Hongli

2013-12-01

It is difficult to reproduce the original color of targets really in different illuminating environment using the traditional methods. So a function which can reconstruct the characteristics of reflection about every point on the surface of target is required urgently to improve the authenticity of color reproduction, which known as the Bidirectional Reflectance Distribution Function(BRDF). A method of color reproduction based on the BRDF measurement is introduced in this paper. Radiometry is combined with the colorimetric theories to measure the irradiance and radiance of GretagMacbeth 24 ColorChecker by using PR-715 Radiation Spectrophotometer of PHOTO RESEARCH, Inc, USA. The BRDF and BRF (Bidirectional Reflectance Factor) values of every color piece corresponding to the reference area are calculated according to irradiance and radiance, thus color tristimulus values of 24 ColorChecker are reconstructed. The results reconstructed by BRDF method are compared with values calculated by the reflectance using PR-715, at last, the chromaticity coordinates in color space and color difference between each other are analyzed. The experimental result shows average color difference and sample standard deviation between the method proposed in this paper and traditional reconstruction method depended on reflectance are 2.567 and 1.3049 respectively. The conclusion indicates that the method of color reproduction based on BRDF has the more obvious advantages to describe the color information of object than the reflectance in hemisphere space through the theoretical and experimental analysis. This method proposed in this paper is effective and feasible during the research of reproducing the chromaticity.

A low-cost real color picker based on Arduino.

Science.gov (United States)

Agudo, Juan Enrique; Pardo, Pedro J; Sánchez, Héctor; Pérez, Ángel Luis; Suero, María Isabel

2014-07-07

Color measurements have traditionally been linked to expensive and difficult to handle equipment. The set of mathematical transformations that are needed to transfer a color that we observe in any object that doesn't emit its own light (which is usually called a color-object) so that it can be displayed on a computer screen or printed on paper is not at all trivial. This usually requires a thorough knowledge of color spaces, colorimetric transformations and color management systems. The TCS3414CS color sensor (I2C Sensor Color Grove), a system for capturing, processing and color management that allows the colors of any non-self-luminous object using a low-cost hardware based on Arduino, is presented in this paper. Specific software has been developed in Matlab and a study of the linearity of chromatic channels and accuracy of color measurements for this device has been undertaken. All used scripts (Arduino and Matlab) are attached as supplementary material. The results show acceptable accuracy values that, although obviously do not reach the levels obtained with the other scientific instruments, for the price difference they present a good low cost option.

Assessment of the hemispheric lateralization of grapheme-color synesthesia with Stroop-type tests.

Directory of Open Access Journals (Sweden)

Mathieu J Ruiz

Full Text Available Grapheme-color synesthesia, the idiosyncratic, arbitrary association of colors to letters or numbers, develops in childhood once reading is mastered. Because language processing is strongly left-lateralized in most individuals, we hypothesized that grapheme-color synesthesia could be left-lateralized as well. We used synesthetic versions of the Stroop test with colored letters and numbers presented either in the right or the left visual field of thirty-four synesthetes. Interference by synesthetic colors was stronger for stimuli in the right hemifield (first experiment, color naming task. Synesthetes were also faster in the right hemifield when naming the synesthetic color of graphemes (second experiment. Overall, the lateralization effect was 7 ms (the 95% confidence interval was [1.5 12] ms, a delay compatible with an additional callosal transfer for stimuli presented in the left hemifield. Though weak, this effect suggests that the association of synesthetic colors to graphemes may be preferentially processed in the left hemisphere. We speculate that this left-lateralization could be a landmark of synesthetic grapheme-color associations, if not found for color associations learnt by non-synesthete adults.

[Acquired disorders of color vision].

Science.gov (United States)

Lascu, Lidia; Balaş, Mihaela

2002-01-01

This article is a general view of acquired disorders of color vision. The revision of the best known methods and of the etiopathogenic classification is not very important in ophthalmology but on the other hand, the detection of the blue defect advertise and associated ocular pathology. There is a major interest in serious diseases as multiple sclerosis, AIDS, diabetes melitus, when the first ocular sign can be a defect in the color vision.

Color-weak compensation using local affine isometry based on discrimination threshold matching

OpenAIRE

Mochizuki, Rika; Kojima, Takanori; Lenz, Reiner; Chao, Jinhui

2015-01-01

We develop algorithms for color-weak compensation and color-weak simulation based on Riemannian geometry models of color spaces. The objective function introduced measures the match of color discrimination thresholds of average normal observers and a color-weak observer. The developed matching process makes use of local affine maps between color spaces of color-normal and color-weak observers. The method can be used to generate displays of images that provide color-normal and color-weak obser...

G0-WISHART Distribution Based Classification from Polarimetric SAR Images

Science.gov (United States)

Hu, G. C.; Zhao, Q. H.

2017-09-01

Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

Color Image Quality Assessment Based on CIEDE2000

Directory of Open Access Journals (Sweden)

Yang Yang

2012-01-01

Full Text Available Combining the color difference formula of CIEDE2000 and the printing industry standard for visual verification, we present an objective color image quality assessment method correlated with subjective vision perception. An objective score conformed to subjective perception (OSCSP Q was proposed to directly reflect the subjective visual perception. In addition, we present a general method to calibrate correction factors of color difference formula under real experimental conditions. Our experiment results show that the present DE2000-based metric can be consistent with human visual system in general application environment.

Visual wetness perception based on image color statistics.

Science.gov (United States)

Sawayama, Masataka; Adelson, Edward H; Nishida, Shin'ya

2017-05-01

Color vision provides humans and animals with the abilities to discriminate colors based on the wavelength composition of light and to determine the location and identity of objects of interest in cluttered scenes (e.g., ripe fruit among foliage). However, we argue that color vision can inform us about much more than color alone. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color can help us recognize complex material qualities. Here we show that human vision uses color statistics of an image for the perception of an ecologically important surface condition (i.e., wetness). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tended to make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation, is consistent with actual optical changes produced by surface wetting. Furthermore, we found that the wetness enhancing transformation operator was more effective for the images with many colors (large hue entropy) than for those with few colors (small hue entropy). The hue entropy may be used to separate surface wetness from other surface states having similar optical properties. While surface wetness and surface color might seem to be independent, there are higher order color statistics that can influence wetness judgments, in accord with the ecological statistics. The present findings indicate that the visual system uses color image statistics in an elegant way to help estimate the complex physical status of a scene.

A Sieving ANN for Emotion-Based Movie Clip Classification

Science.gov (United States)

Watanapa, Saowaluk C.; Thipakorn, Bundit; Charoenkitkarn, Nipon

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

Parts-based stereoscopic image assessment by learning binocular manifold color visual properties

Science.gov (United States)

Xu, Haiyong; Yu, Mei; Luo, Ting; Zhang, Yun; Jiang, Gangyi

2016-11-01

Existing stereoscopic image quality assessment (SIQA) methods are mostly based on the luminance information, in which color information is not sufficiently considered. Actually, color is part of the important factors that affect human visual perception, and nonnegative matrix factorization (NMF) and manifold learning are in line with human visual perception. We propose an SIQA method based on learning binocular manifold color visual properties. To be more specific, in the training phase, a feature detector is created based on NMF with manifold regularization by considering color information, which not only allows parts-based manifold representation of an image, but also manifests localized color visual properties. In the quality estimation phase, visually important regions are selected by considering different human visual attention, and feature vectors are extracted by using the feature detector. Then the feature similarity index is calculated and the parts-based manifold color feature energy (PMCFE) for each view is defined based on the color feature vectors. The final quality score is obtained by considering a binocular combination based on PMCFE. The experimental results on LIVE I and LIVE Π 3-D IQA databases demonstrate that the proposed method can achieve much higher consistency with subjective evaluations than the state-of-the-art SIQA methods.

Scanner-based macroscopic color variation estimation

Science.gov (United States)

Kuo, Chunghui; Lai, Di; Zeise, Eric

2006-01-01

Flatbed scanners have been adopted successfully in the measurement of microscopic image artifacts, such as granularity and mottle, in print samples because of their capability of providing full color, high resolution images. Accurate macroscopic color measurement relies on the use of colorimeters or spectrophotometers to provide a surrogate for human vision. The very different color response characteristics of flatbed scanners from any standard colorimetric response limits the utility of a flatbed scanner as a macroscopic color measuring device. This metamerism constraint can be significantly relaxed if our objective is mainly to quantify the color variations within a printed page or between pages where a small bias in measured colors can be tolerated as long as the color distributions relative to the individual mean values is similar. Two scenarios when converting color from the device RGB color space to a standardized color space such as CIELab are studied in this paper, blind and semi-blind color transformation, depending on the availability of the black channel information. We will show that both approaches offer satisfactory results in quantifying macroscopic color variation across pages while the semi-blind color transformation further provides fairly accurate color prediction capability.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

Science.gov (United States)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

[Study of spectrum drifting of primary colors and its impact on color rendering properties].

Science.gov (United States)

Cui, Xiao-yan; Zhang, Xiao-dong

2012-08-01

LEDs are currently used widely to display text, graphics and images in large screens. With red, green and blue LEDs as three primary colors, color rendition will be realized through color mixing. However, LEDs' spectrum will produce drifts with the changes in the temperature environment. With the changes in the driving current simulating changes in the temperature, the three primary color LEDs' spectral drifts were tested, and the drift characteristics of the three primary colors were obtained respectively. Based on the typical characteristics of the LEDs and the differences between LEDs with different colors in composition and molecular structure, the paper analyzed the reason for the spectrum drifts and the drift characteristics of different color LEDs, and proposed the equations of spectrum drifts. Putting the experimental data into the spectrum drift equations, the paper analyzed the impacts of primary colors on the mixed color, pointed out a way to reduce the chromatic aberration, and provided the theory for engineering application of color LEDs.

Standard test method for color and color difference of whitewares by abriged spectrophotometry

CERN Document Server

American Society for Testing and Materials. Philadelphia

2001-01-01

1.1 This test method describes the instrumental measurement of the reflection properties and color of ceramic glazes and other whitewares by the use of a spectrophotometer or spectrocolorimeter with a hemispherical optical measuring system, such as an integrating sphere. 1.2 The test method is suitable for use with most specimens having an exterior flat surface large enough to cover the spectrophotometer sample port. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Development of multi-color scintillator based X-ray image intensifier

International Nuclear Information System (INIS)

Nittoh, Koichi; Konagai, Chikara; Noji, Takashi

2004-01-01

A multi-color scintillator based high-sensitive, wide dynamic range and long-life X-ray image intensifier has been developed. An europium activated Y 2 O 2 S scintillator, emitting red, green and blue photons of different intensities, is utilized as the output fluorescent screen of the intensifier. By combining this image intensifier with a suitably tuned high sensitive color CCD camera, it is possible for a sensitivity of the red color component to become six times higher than that of the conventional image intensifier. Simultaneous emission of a moderate green color and a weak blue color covers different sensitivity regions. This widens the dynamic range, by nearly two orders of ten. With this image intensifier, it is possible to image simultaneously complex objects containing various different X-ray transmission from paper, water or plastic to heavy metals. This high sensitivity intensifier, operated at lower X-ray exposure, causes less degradation of scintillator materials and less colorization of output screen glass, and thus helps achieve a longer lifetime. This color scintillator based image intensifier is being introduced for X-ray inspection in various fields

Assessment of Selective Attention with CSCWT (Computerized Stroop Color-Word Test) among Children and Adults

Science.gov (United States)

Afsaneh, Zarghi; Alireza, Zali; Mehdi, Tehranidost; Farzad, Ashrafi; Reza, Zarindast Mohammad; Mehdi, Moazzezi; Mojtaba, Khodadadi Seyed

2012-01-01

The SCWT (Stroop Color-Word Test) is a quick and frequently used measure for assessing selective attention and cognitive flexibility. This study determines age, sex and education level influence on attention and cognitive flexibility by CSCWT (Computerized Stroop Color-Word Test) among healthy Iranian children and adults. There were 78 healthy…

Cholesteric Liquid Crystal Based Reflex Color Reflective Displays

Science.gov (United States)

Khan, Asad

2012-02-01

Bistable color cholesteric liquid crystal displays are unique LCDs that exhibit high reflectivity, good contrast, extremely low power operation, and are amenable to versatile roll-to-roll manufacturing. The display technology, now branded as Reflex has been in commercialized products since 1996. It has been the subject of extensive research and development globally by a variety of parties in both academic and industrial settings. Today, the display technology is in volume production for applications such as dedicated eWriters (Boogie Board), full color electronic skins (eSkin), and displays for smart cards. The flexibility comes from polymerization induced phase separation using unique materials unparalleled in any other display technology. The blend of monomers, polymers, cross linkers, and other components along with nematic liquid crystals and chiral dopants is created and processed in such ways so as to enable highly efficient manufactrable displays using ultra thin plastic substrates -- often as thin as 50μm. Other significant aspects include full color by stacking or spatial separation, night vision capability, ultra high resolution, as well as active matrix capabilities. Of particular note is the stacking approach of Reflex based displays to show full color. This approach for reflective color displays is unique to this technology. Owing to high transparency in wavelength bands outside the selective reflection band, three primarily color layers can be stacked on top of each other and reflect without interfering with other layers. This highly surprising architecture enables the highest reflectivity of any other reflective electronic color display technology. The optics, architecture, electro-topics, and process techniques will be discussed. This presentation will focus on the physics of the core technology and color, it's evolution from rigid glass based displays to flexible displays, development of products from the paradigm shifting concepts to consumer

Color-Space-Based Visual-MIMO for V2X Communication

OpenAIRE

Jai-Eun Kim; Ji-Won Kim; Youngil Park; Ki-Doo Kim

2016-01-01

In this paper, we analyze the applicability of color-space-based, color-independent visual-MIMO for V2X. We aim to achieve a visual-MIMO scheme that can maintain the original color and brightness while performing seamless communication. We consider two scenarios of GCM based visual-MIMO for V2X. One is a multipath transmission using visual-MIMO networking and the other is multi-node V2X communication. In the scenario of multipath transmission, we analyze the channel capacity numerically and w...

CONTROLLED CONDENSATION IN K-NN AND ITS APPLICATION FOR REAL TIME COLOR IDENTIFICATION

Directory of Open Access Journals (Sweden)

Carmen Villar Patiño

2017-04-01

Full Text Available k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

Science.gov (United States)

Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

2017-06-01

In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

Color sensor and neural processor on one chip

Science.gov (United States)

Fiesler, Emile; Campbell, Shannon R.; Kempem, Lother; Duong, Tuan A.

1998-10-01

Low-cost, compact, and robust color sensor that can operate in real-time under various environmental conditions can benefit many applications, including quality control, chemical sensing, food production, medical diagnostics, energy conservation, monitoring of hazardous waste, and recycling. Unfortunately, existing color sensor are either bulky and expensive or do not provide the required speed and accuracy. In this publication we describe the design of an accurate real-time color classification sensor, together with preprocessing and a subsequent neural network processor integrated on a single complementary metal oxide semiconductor (CMOS) integrated circuit. This one-chip sensor and information processor will be low in cost, robust, and mass-producible using standard commercial CMOS processes. The performance of the chip and the feasibility of its manufacturing is proven through computer simulations based on CMOS hardware parameters. Comparisons with competing methodologies show a significantly higher performance for our device.

Adaptive testing for making unidimensional and multidimensional classification decisions

NARCIS (Netherlands)

van Groen, M.M.

2014-01-01

Computerized adaptive tests (CATs) were originally developed to obtain an efficient estimate of the examinee’s ability, but they can also be used to classify the examinee into one of two or more levels (e.g. master/non-master). These computerized classification tests have the advantage that they can

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

Science.gov (United States)

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

Directory of Open Access Journals (Sweden)

Stefan Dech

2012-09-01

Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Color Shaded-Relief and Surface-Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

Science.gov (United States)

Mars, John L.; Garrity, Christopher P.; Houseknecht, David W.; Amoroso, Lee; Meares, Donald C.

2007-01-01

Introduction The northeastern part of the National Petroleum Reserve in Alaska (NPRA) has become an area of active petroleum exploration during the past five years. Recent leasing and exploration drilling in the NPRA requires the U.S. Bureau of Land Management (BLM) to manage and monitor a variety of surface activities that include seismic surveying, exploration drilling, oil-field development drilling, construction of oil-production facilities, and construction of pipelines and access roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require rapid compilation and analysis of data pertaining to surface and subsurface geology, hydrology, and biology. In addition, BLM must monitor these activities and assess their impacts on the natural environment. Timely and accurate completion of these land-management tasks requires elevation, hydrologic, geologic, petroleum-activity, and cadastral data, all integrated in digital formats at a higher resolution than is currently available in nondigital (paper) formats. To support these land-management tasks, a series of maps was generated from remotely sensed data in an area of high petroleum-industry activity (fig. 1). The maps cover an area from approximately latitude 70?00' N. to 70?30' N. and from longitude 151?00' W. to 153?10' W. The area includes the Alpine oil field in the east, the Husky Inigok exploration well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a proposed pipeline to carry oil from discovery wells in NPRA to the Alpine oil field. This map area is referred to as the 'Fish Creek area' after a creek that flows through the region. The map series includes (1) a color shaded-relief map based on 5-m-resolution data (sheet 1), (2) a surface-classification map based on 30-m-resolution data (sheet 2), and (3) a 5-m-resolution shaded relief-surface classification map that combines the shaded

Hypnotic color blindness and performance on the Stroop test.

Science.gov (United States)

Mallard, D; Bryant, R A

2001-10-01

A suggestion for hypnotic color blindness was investigated by administering a reverse Stroop color-naming task. Prior to the suggestion for color blindness, participants learned associations between color names and shapes. Following the color blindness suggestion, participants were required to name the shapes when they appeared in colors that were either congruent or incongruent with the learned associations. The 18 high hypnotizable participants who passed the suggestion were slower to name (a) shapes in which the color name was incongruent with the color in which it was printed, (b) "unseen" rather than "seen" shapes, and (c) color-incongruent shapes that were printed in the color in which they were "color-blind." These patterns are discussed in terms of potential cognitive and social mechanisms that may mediate responses to hypnotic color blindness.

A classification model of Hyperion image base on SAM combined decision tree

Science.gov (United States)

Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

2009-10-01

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

WHY COLOR-FLAVOR LOCKING IS JUST LIKE CHIRAL SYMMETRY BREAKING

International Nuclear Information System (INIS)

PISARSKI, R.D.; RISCHKE, D.H.

2000-01-01

The authors review how a classification into representations of color and flavor can be used to understand the possible patterns of symmetry breaking for color superconductivity in dense quark matter. In particular, the authors show how for three flavors, color-flavor locking is precisely analogous to the usual pattern of chiral symmetry breaking in the QCD vacuum

Negative Assortative Mating Based on Body Coloration in the Freshwater Platyfish (Poecillidae: Xiphophorus maculatus

Directory of Open Access Journals (Sweden)

Tyler E. Frankel

2017-04-01

Full Text Available The ability of individuals within a population to survive and thrive is highly dependent upon the maintenance of genetic variation and phenotypic diversity, thereby ensuring adaptation to dynamic environments. A fundamental method of maintaining such variation is through a negative assortative mating strategy, in which individuals would be expected to reproductively select members of the opposite sex that exhibit dissimilar phenotypes. Employing three uniform body color morphs, red, yellow and blue, of the platyfish (Xiphophorus maculatus, this study was designed to investigate whether X. maculatus females would preferentially be attracted to males exhibiting an alternative color, thereby enabling an examination of the effect of male body coloration on mate choice by adult females. Mate choice was determined based on the initial preference of each female, as well as the amount of time females spent associating with each male. Initial preferences were analyzed using a binomial distribution test, and overall preference data using Wilcoxon signed rank tests. Red females initially selected for dissimilar colored males, and spent a significantly larger amount of time associating with blue and yellow males, as did yellow females with red and blue males. Blue females initially selected and spent a significantly larger amount of time associating with red males but, interestingly, showed no selective preference between blue and yellow males. In these experimental trials, the overall strong mate selection exhibited by female platyfish for males of dissimilar coloration is suggestive of a negative assortative mating strategy and provides evidence for the maintenance of color polymorphism in nature populations.

Railroad signal color and orientation : effects of color blindness and criteria for color vision field tests

Science.gov (United States)

2015-03-01

This report concerns two issues: 1) whether color vision is necessary for locomotive crews who work on railroads where the signal system is either completely redundant with regard to signal color and signal orientation or the signal system only uses ...

Color Shift Modeling of Light-Emitting Diode Lamps in Step-Loaded Stress Testing

NARCIS (Netherlands)

Cai, Miao; Yang, Daoguo; Huang, J.; Zhang, Maofen; Chen, Xianping; Liang, Caihang; Koh, S.W.; Zhang, G.Q.

2017-01-01

The color coordinate shift of light-emitting diode (LED) lamps is investigated by running three stress-loaded testing methods, namely step-up stress accelerated degradation testing, step-down stress accelerated degradation testing, and constant stress accelerated degradation testing. A power

Interactive bibliographical database on color

Science.gov (United States)

Caivano, Jose L.

2002-06-01

The paper describes the methodology and results of a project under development, aimed at the elaboration of an interactive bibliographical database on color in all fields of application: philosophy, psychology, semiotics, education, anthropology, physical and natural sciences, biology, medicine, technology, industry, architecture and design, arts, linguistics, geography, history. The project is initially based upon an already developed bibliography, published in different journals, updated in various opportunities, and now available at the Internet, with more than 2,000 entries. The interactive database will amplify that bibliography, incorporating hyperlinks and contents (indexes, abstracts, keywords, introductions, or eventually the complete document), and devising mechanisms for information retrieval. The sources to be included are: books, doctoral dissertations, multimedia publications, reference works. The main arrangement will be chronological, but the design of the database will allow rearrangements or selections by different fields: subject, Decimal Classification System, author, language, country, publisher, etc. A further project is to develop another database, including color-specialized journals or newsletters, and articles on color published in international journals, arranged in this case by journal name and date of publication, but allowing also rearrangements or selections by author, subject and keywords.

Unconscious Familiarity-based Color-Form Binding: Evidence from Visual Extinction.

Science.gov (United States)

Rappaport, Sarah J; Riddoch, M Jane; Chechlacz, Magda; Humphreys, Glyn W

2016-03-01

There is good evidence that early visual processing involves the coding of different features in independent brain regions. A major question, then, is how we see the world in an integrated manner, in which the different features are "bound" together. A standard account of this has been that feature binding depends on attention to the stimulus, which enables only the relevant features to be linked together [Treisman, A., & Gelade, G. A feature-integration theory of attention. Cognitive Psychology, 12, 97-136, 1980]. Here we test this influential idea by examining whether, in patients showing visual extinction, the processing of otherwise unconscious (extinguished) stimuli is modulated by presenting objects in their correct (familiar) color. Correctly colored objects showed reduced extinction when they had a learned color, and this color matched across the ipsi- and contralesional items (red strawberry + red tomato). In contrast, there was no reduction in extinction under the same conditions when the stimuli were colored incorrectly (blue strawberry + blue tomato; Experiment 1). The result was not due to the speeded identification of a correctly colored ipsilesional item, as there was no benefit from having correctly colored objects in different colors (red strawberry + yellow lemon; Experiment 2). There was also no benefit to extinction from presenting the correct colors in the background of each item (Experiment 3). The data suggest that learned color-form binding can reduce extinction even when color is irrelevant for the task. The result is consistent with preattentive binding of color and shape for familiar stimuli.

A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

Science.gov (United States)

Grassmann, Felix; Mengelkamp, Judith; Brandl, Caroline; Harsch, Sebastian; Zimmermann, Martina E; Linkohr, Birgit; Peters, Annette; Heid, Iris M; Palm, Christoph; Weber, Bernhard H F

2018-04-10

Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%-92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm

Modernization of Physical Appearance and Solution Color Tests Using Quantitative Tristimulus Colorimetry: Advantages, Harmonization, and Validation Strategies.

Science.gov (United States)

Pack, Brian W; Montgomery, Laura L; Hetrick, Evan M

2015-10-01

Color measurements, including physical appearance, are important yet often misunderstood and underappreciated aspects of a control strategy for drug substances and drug products. From a patient safety perspective, color can be an important control point for detecting contamination, impurities, and degradation products, with human visual acuity often more sensitive for colored impurities than instrumental techniques such as HPLC. Physical appearance tests and solution color tests can also serve an important role in ensuring that appropriate steps are taken such that clinical trials do not become unblinded when the active material is compared with another product or a placebo. Despite the importance of color tests, compendial visual tests are not harmonized across the major pharmacopoeias, which results in ambiguous specifications of little value, difficult communication of true sample color, and significant extra work required for global registration. Some pharmacopoeias have not yet recognized or adopted technical advances in the instrumental measurement of color and appearance, whereas others begin to acknowledge the advantage of instrumental colorimetry, yet leave implementation of the technology ambiguous. This commentary will highlight the above-mentioned inconsistencies, provide an avenue toward harmonization and modernization, and outline a scientifically sound approach for implementing quantitative technologies for improved measurement, communication, and control of color and appearance for both solutions and solids. Importantly, this manuscript, for the first time, outlines a color method validation approach that is consistent with the International Conference on Harmonization's guidance on the topic of method validation. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

A fuzzy art neural network based color image processing and ...

African Journals Online (AJOL)

To improve the learning process from the input data, a new learning rule was suggested. In this paper, a new method is proposed to deal with the RGB color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of ...

Color contrasting in radioscopy systems

International Nuclear Information System (INIS)

Lopaev, V.P.; Pavlov, S.V.; Nazarenko, V.G.

1979-01-01

Transformation principles for achromatic radioscopy control systems to color ones have been considered. Described is the developed ''Gamma 1'' roentgen-TV facility with color contrasting, which is based on the principle of analog conversion of brightness signal to a hue. By means of color channels amplifiers realized are the special amplitude characteristics, permitting in comparison with the common method of analogous transformation to obtain the greater number of hues within the identical range of brightnesses of image under investigation due to introducing purple colors. The investigation of amplitude resolution capability of color contrasting device has shown, that in the case of color contrasting of image the amplitude resolution is 1.7-1.8 time higher than in the case of achromatic one. Defectoscopic sensitivity during the testing of 5-20 mm thick steel products in the process of experimental-production tests turned out to be 1.1-1.3 time higher when using color contrasting of radioscopic image. Realization simplicity, high resolution, noise stability and wide functional possibilities of the facility show the prospects for its using during the quality control of welded joints in products of power engineering

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

Science.gov (United States)

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

The generalization ability of online SVM classification based on Markov sampling.

Science.gov (United States)

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

Directory of Open Access Journals (Sweden)

Anne-Hélène Rêve-Lamarche

2017-12-01

Full Text Available Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between in-situ information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more toward phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated with specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current in-situ ecological knowledge.

Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT

Science.gov (United States)

Madokoro, H.; Yamanashi, A.; Sato, K.

2013-08-01

This paper presents an unsupervised scene classification method for actualizing semantic recognition of indoor scenes. Background and foreground features are respectively extracted using Gist and color scale-invariant feature transform (SIFT) as feature representations based on context. We used hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm with low calculation costs. Our method creates bags of features for voting visual words created from both feature descriptors to a two-dimensional histogram. Moreover, our method generates labels as candidates of categories for time-series images while maintaining stability and plasticity together. Automatic labeling of category maps can be realized using labels created using adaptive resonance theory (ART) as teaching signals for counter propagation networks (CPNs). We evaluated our method for semantic scene classification using KTH's image database for robot localization (KTH-IDOL), which is popularly used for robot localization and navigation. The mean classification accuracies of Gist, gray SIFT, one class support vector machines (OC-SVM), position-invariant robust features (PIRF), and our method are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The result of our method is 15.8% higher than that of PIRF. Moreover, we applied our method for fine classification using our original mobile robot. We obtained mean classification accuracy of 83.2% for six zones.

Soil classification based on cone penetration test (CPT) data in Western Central Java

Science.gov (United States)

Apriyono, Arwan; Yanto, Santoso, Purwanto Bekti; Sumiyanto

2018-03-01

This study presents a modified friction ratio range for soil classification i.e. gravel, sand, silt & clay and peat, using CPT data in Western Central Java. The CPT data was obtained solely from Soil Mechanic Laboratory of Jenderal Soedirman University that covers more than 300 sites within the study area. About 197 data were produced from data filtering process. IDW method was employed to interpolated friction ratio values in a regular grid point for soil classification map generation. Soil classification map was generated and presented using QGIS software. In addition, soil classification map with respect to modified friction ratio range was validated using 10% of total measurements. The result shows that silt and clay dominate soil type in the study area, which is in agreement with two popular methods namely Begemann and Vos. However, the modified friction ratio range produces 85% similarity with laboratory measurements whereby Begemann and Vos method yields 70% similarity. In addition, modified friction ratio range can effectively distinguish fine and coarse grains, thus useful for soil classification and subsequently for landslide analysis. Therefore, modified friction ratio range proposed in this study can be used to identify soil type for mountainous tropical region.

Color-Space-Based Visual-MIMO for V2X Communication.

Science.gov (United States)

Kim, Jai-Eun; Kim, Ji-Won; Park, Youngil; Kim, Ki-Doo

2016-04-23

In this paper, we analyze the applicability of color-space-based, color-independent visual-MIMO for V2X. We aim to achieve a visual-MIMO scheme that can maintain the original color and brightness while performing seamless communication. We consider two scenarios of GCM based visual-MIMO for V2X. One is a multipath transmission using visual-MIMO networking and the other is multi-node V2X communication. In the scenario of multipath transmission, we analyze the channel capacity numerically and we illustrate the significance of networking information such as distance, reference color (symbol), and multiplexing-diversity mode transitions. In addition, in the V2X scenario of multiple access, we may achieve the simultaneous multiple access communication without node interferences by dividing the communication area using image processing. Finally, through numerical simulation, we show the superior SER performance of the visual-MIMO scheme compared with LED-PD communication and show the numerical result of the GCM based visual-MIMO channel capacity versus distance.

CMEIAS color segmentation: an improved computing technology to process color images for quantitative microbial ecology studies at single-cell resolution.

Science.gov (United States)

Gross, Colin A; Reddy, Chandan K; Dazzo, Frank B

2010-02-01

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system's uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/ . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

Science.gov (United States)

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all Pbreast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

Inkjet color-printer control interface

Science.gov (United States)

Kistler, R.; Kriegler, F. J.; Marshall, R. E.

1977-01-01

Special purpose interface permits computer-driven control of inkjet printers. Inkjet printers are answer to problem of high-speed peripheral output devices for computer systems. Control interface was developed to provide high-resolution color-classification maps quickly and economically from multispectral data.

Luminance contours can gate afterimage colors and 'real' colors

NARCIS (Netherlands)

Anstis, S.; Vergeer, M.L.T.; Lier, R.J. van

2012-01-01

It has long been known that colored images may elicit afterimages in complementary colors. We have already shown (Van Lier, Vergeer, & Anstis, 2009) that one and the same adapting image may result in different afterimage colors, depending on the test contours presented after the colored image. The

Multi-label literature classification based on the Gene Ontology graph

Directory of Open Access Journals (Sweden)

Lu Xinghua

2008-12-01

Full Text Available Abstract Background The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. Results In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Conclusion Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

Directory of Open Access Journals (Sweden)

Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

A drone detection with aircraft classification based on a camera array

Science.gov (United States)

Liu, Hao; Qu, Fangchao; Liu, Yingjian; Zhao, Wei; Chen, Yitong

2018-03-01

In recent years, because of the rapid popularity of drones, many people have begun to operate drones, bringing a range of security issues to sensitive areas such as airports and military locus. It is one of the important ways to solve these problems by realizing fine-grained classification and providing the fast and accurate detection of different models of drone. The main challenges of fine-grained classification are that: (1) there are various types of drones, and the models are more complex and diverse. (2) the recognition test is fast and accurate, in addition, the existing methods are not efficient. In this paper, we propose a fine-grained drone detection system based on the high resolution camera array. The system can quickly and accurately recognize the detection of fine grained drone based on hd camera.

A review of supervised object-based land-cover image classification

Science.gov (United States)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

Science.gov (United States)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

Eleven Colors That Are Almost Never Confused

Science.gov (United States)

Boynton, Robert M.

1989-08-01

1.1. Three functions of color vision. Setting aside the complex psychological effects of color, related to esthetics, fashion, and mood, three relatively basic functions of color vision, which can be examined scientifically, are discernable. (1) With the eye in a given state of adaptation, color vision allows the perception of signals that otherwise would be below threshold, and therefore lost to perception. Evidence for this comes from a variety of two-color threshold experiments. (2) Visible contours can be maintained by color differences alone, regardless of the relative radiances of the two parts of the field whose junction defines the border. For achromatic vision, contour disappears at the isoluminant point. (3) Color specifies what seems to be an absolute property of a surface, one that enhances its recognizability and allows a clearer separation and classification of non-contiguous elements in the visual field.

TESTING STELLAR POPULATION SYNTHESIS MODELS WITH SLOAN DIGITAL SKY SURVEY COLORS OF M31's GLOBULAR CLUSTERS

International Nuclear Information System (INIS)

Peacock, Mark B.; Zepf, Stephen E.; Maccarone, Thomas J.; Kundu, Arunav

2011-01-01

Accurate stellar population synthesis models are vital in understanding the properties and formation histories of galaxies. In order to calibrate and test the reliability of these models, they are often compared with observations of star clusters. However, relatively little work has compared these models in the ugriz filters, despite the recent widespread use of this filter set. In this paper, we compare the integrated colors of globular clusters in the Sloan Digital Sky Survey (SDSS) with those predicted from commonly used simple stellar population (SSP) models. The colors are based on SDSS observations of M31's clusters and provide the largest population of star clusters with accurate photometry available from the survey. As such, it is a unique sample with which to compare SSP models with SDSS observations. From this work, we identify a significant offset between the SSP models and the clusters' g - r colors, with the models predicting colors which are too red by g - r ∼ 0.1. This finding is consistent with previous observations of luminous red galaxies in the SDSS, which show a similar discrepancy. The identification of this offset in globular clusters suggests that it is very unlikely to be due to a minority population of young stars. The recently updated SSP model of Maraston and Stroembaeck better represents the observed g - r colors. This model is based on the empirical MILES stellar library, rather than theoretical libraries, suggesting an explanation for the g - r discrepancy.

Modelling the IRAS colors of galaxies

International Nuclear Information System (INIS)

Helou, G.

1987-01-01

A physical interpretation is proposed for the color-color diagram of galaxies which are powered only by star formation. The colors of each galaxy result from the combination of two components: cirrus-like emission from the neutral disk, and warmer emission from regions directly involved in on-going star formation. This approach to modelling the emission is based on dust properties, but independent evidence for it is found in the relation between the color sequence and the luminosity sequence. Implications of data and interpretations are discussed and possible tests mentioned for the model

Performance analysis of multi-primary color display based on OLEDs/PLEDs

Science.gov (United States)

Xiong, Yan; Deng, Fei; Xu, Shan; Gao, Shufang

2017-09-01

A multi-primary color display, such as the six-primary color format, is a solution in expanding the color gamut of a full-color flat panel display. The performance of a multi-primary color display based on organic/polymer light-emitting diodes was analyzed in this study using the fitting curves of the characteristics of devices (i.e., current density, voltage, luminance). A white emitter was introduced into a six-primary color format to form a seven-primary color format that contributes to energy saving, and the ratio of power efficiency of a seven-primary color display to that of a six-primary color display would increase from 1.027 to 1.061 by using emitting diodes with different electroluminescent efficiencies. Different color matching schemes of the seven-primary color format display were compared in a uniform color space, and the scheme of the color reproduction did not significantly affect the display performance. Although seven- and six-primary color format displays benefit a full-color display with higher quality, they are less efficient than three-primary (i.e., red (R), green (G), and blue (B), RGB) and four-primary (i.e., RGB+white, RGBW) color format displays. For the seven-primary color formats considered in this study, the advantages of white-primary-added display with efficiently developed light-emitting devices were more evident than the format without a white primary.

Image color reduction method for color-defective observers using a color palette composed of 20 particular colors

Science.gov (United States)

Sakamoto, Takashi

2015-01-01

This study describes a color enhancement method that uses a color palette especially designed for protan and deutan defects, commonly known as red-green color blindness. The proposed color reduction method is based on a simple color mapping. Complicated computation and image processing are not required by using the proposed method, and the method can replace protan and deutan confusion (p/d-confusion) colors with protan and deutan safe (p/d-safe) colors. Color palettes for protan and deutan defects proposed by previous studies are composed of few p/d-safe colors. Thus, the colors contained in these palettes are insufficient for replacing colors in photographs. Recently, Ito et al. proposed a p/dsafe color palette composed of 20 particular colors. The author demonstrated that their p/d-safe color palette could be applied to image color reduction in photographs as a means to replace p/d-confusion colors. This study describes the results of the proposed color reduction in photographs that include typical p/d-confusion colors, which can be replaced. After the reduction process is completed, color-defective observers can distinguish these confusion colors.

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

Science.gov (United States)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

Structure-based classification and ontology in chemistry

Directory of Open Access Journals (Sweden)

Hastings Janna

2012-04-01

Full Text Available Abstract Background Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures, while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies. Results We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches. Conclusion Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational

Contextual segment-based classification of airborne laser scanner data

NARCIS (Netherlands)

Vosselman, George; Coenen, Maximilian; Rottensteiner, Franz

2017-01-01

Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset

Color Memory

OpenAIRE

Pate, Monica; Raclariu, Ana-Maria; Strominger, Andrew

2017-01-01

A transient color flux across null infinity in classical Yang-Mills theory is considered. It is shown that a pair of test `quarks' initially in a color singlet generically acquire net color as a result of the flux. A nonlinear formula is derived for the relative color rotation of the quarks. For weak color flux the formula linearizes to the Fourier transform of the soft gluon theorem. This color memory effect is the Yang-Mills analog of the gravitational memory effect.

Design of structurally colored surfaces based on scalar diffraction theory

DEFF Research Database (Denmark)

Johansen, Villads Egede; Andkjær, Jacob Anders; Sigmund, Ole

2014-01-01

In this paper we investigate the possibility of controlling the color and appearance of surfaces simply by modifying the height profile of the surface on a nanoscale level. The applications for such methods are numerous: new design possibilities for high-end products, color engraving on any highly...... reflective surface, paint-free text and coloration, UV-resistant coloring, etc. In this initial study, the main focus is on finding a systematic way to obtain these results. For now the simulation and optimization is based on a simple scalar diffraction theory model. From the results, several design issues...

Color-Space-Based Visual-MIMO for V2X Communication

Directory of Open Access Journals (Sweden)

Jai-Eun Kim

2016-04-01

Full Text Available In this paper, we analyze the applicability of color-space-based, color-independent visual-MIMO for V2X. We aim to achieve a visual-MIMO scheme that can maintain the original color and brightness while performing seamless communication. We consider two scenarios of GCM based visual-MIMO for V2X. One is a multipath transmission using visual-MIMO networking and the other is multi-node V2X communication. In the scenario of multipath transmission, we analyze the channel capacity numerically and we illustrate the significance of networking information such as distance, reference color (symbol, and multiplexing-diversity mode transitions. In addition, in the V2X scenario of multiple access, we may achieve the simultaneous multiple access communication without node interferences by dividing the communication area using image processing. Finally, through numerical simulation, we show the superior SER performance of the visual-MIMO scheme compared with LED-PD communication and show the numerical result of the GCM based visual-MIMO channel capacity versus distance.

Comparing writing style feature-based classification methods for estimating user reputations in social media.

Science.gov (United States)

Suh, Jong Hwan

2016-01-01

In recent years, the anonymous nature of the Internet has made it difficult to detect manipulated user reputations in social media, as well as to ensure the qualities of users and their posts. To deal with this, this study designs and examines an automatic approach that adopts writing style features to estimate user reputations in social media. Under varying ways of defining Good and Bad classes of user reputations based on the collected data, it evaluates the classification performance of the state-of-art methods: four writing style features, i.e. lexical, syntactic, structural, and content-specific, and eight classification techniques, i.e. four base learners-C4.5, Neural Network (NN), Support Vector Machine (SVM), and Naïve Bayes (NB)-and four Random Subspace (RS) ensemble methods based on the four base learners. When South Korea's Web forum, Daum Agora, was selected as a test bed, the experimental results show that the configuration of the full feature set containing content-specific features and RS-SVM combining RS and SVM gives the best accuracy for classification if the test bed poster reputations are segmented strictly into Good and Bad classes by portfolio approach. Pairwise t tests on accuracy confirm two expectations coming from the literature reviews: first, the feature set adding content-specific features outperform the others; second, ensemble learning methods are more viable than base learners. Moreover, among the four ways on defining the classes of user reputations, i.e. like, dislike, sum, and portfolio, the results show that the portfolio approach gives the highest accuracy.

Superpixel-based classification of gastric chromoendoscopy images

Science.gov (United States)

Boschetto, Davide; Grisan, Enrico

2017-03-01

Chromoendoscopy (CH) is a gastroenterology imaging modality that involves the staining of tissues with methylene blue, which reacts with the internal walls of the gastrointestinal tract, improving the visual contrast in mucosal surfaces and thus enhancing a doctor's ability to screen precancerous lesions or early cancer. This technique helps identify areas that can be targeted for biopsy or treatment and in this work we will focus on gastric cancer detection. Gastric chromoendoscopy for cancer detection has several taxonomies available, one of which classifies CH images into three classes (normal, metaplasia, dysplasia) based on color, shape and regularity of pit patterns. Computer-assisted diagnosis is desirable to help us improve the reliability of the tissue classification and abnormalities detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. We propose the exploitation of a first unsupervised segmentation via superpixel, which groups pixels into perceptually meaningful atomic regions, used to replace the rigid structure of the pixel grid. For each superpixel, a set of features is extracted and then fed to a random forest based classifier, which computes a model used to predict the class of each superpixel. The average general accuracy of our model is 92.05% in the pixel domain (86.62% in the superpixel domain), while detection accuracies on the normal and abnormal class are respectively 85.71% and 95%. Eventually, the whole image class can be predicted image through a majority vote on each superpixel's predicted class.

Visual color matching system based on RGB LED light source

Science.gov (United States)

Sun, Lei; Huang, Qingmei; Feng, Chen; Li, Wei; Wang, Chaofeng

2018-01-01

In order to study the property and performance of LED as RGB primary color light sources on color mixture in visual psychophysical experiments, and to find out the difference between LED light source and traditional light source, a visual color matching experiment system based on LED light sources as RGB primary colors has been built. By simulating traditional experiment of metameric color matching in CIE 1931 RGB color system, it can be used for visual color matching experiments to obtain a set of the spectral tristimulus values which we often call color-matching functions (CMFs). This system consists of three parts: a monochromatic light part using blazed grating, a light mixing part where the summation of 3 LED illuminations are to be visually matched with a monochromatic illumination, and a visual observation part. The three narrow band LEDs used have dominant wavelengths of 640 nm (red), 522 nm (green) and 458 nm (blue) respectively and their intensities can be controlled independently. After the calibration of wavelength and luminance of LED sources with a spectrophotometer, a series of visual color matching experiments have been carried out by 5 observers. The results are compared with those from CIE 1931 RGB color system, and have been used to compute an average locus for the spectral colors in the color triangle, with white at the center. It has been shown that the use of LED is feasible and has the advantages of easy control, good stability and low cost.

A patch-based convolutional neural network for remote sensing image classification.

Science.gov (United States)

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Color and experimental physics

International Nuclear Information System (INIS)

Chanowitz, M.S.

1975-01-01

After a brief review of the color hypothesis and the motivations for its introduction, the experimental tests arare discussed. It is assumed that colored states have not been produced at present energies and only experimental tests which apply below the color threshold, when color is a ''hidden symmetry,'' are discussed. Some of these tests offer the possibility of distinguishing between quark models with fractional and integral quark charges. (auth)

EMG finger movement classification based on ANFIS

Science.gov (United States)

Caesarendra, W.; Tjahjowidodo, T.; Nico, Y.; Wahyudati, S.; Nurhasanah, L.

2018-04-01

An increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.

Structural (UV) and carotenoid-based plumage coloration - signals for parental investment?

Science.gov (United States)

Lucass, Carsten; Iserbyt, Arne; Eens, Marcel; Müller, Wendt

2016-05-01

Parental care increases parental fitness through improved offspring condition and survival but comes at a cost for the caretaker(s). To increase life-time fitness, caring parents are, therefore, expected to adjust their reproductive investment to current environmental conditions and parental capacities. The latter is thought to be signaled via ornamental traits of the bearer. We here investigated whether pre- and/or posthatching investment of blue tit (Cyanistes caeruleus) parents was related to ornamental plumage traits (UV crown coloration and carotenoid-based plumage coloration) expressed by either the individual itself (i.e. "good parent hypothesis") or its partner (i.e. "differential allocation hypothesis"). Our results show that neither prehatching (that is clutch size and offspring begging intensity) nor posthatching parental investment (provisioning rate, offspring body condition at fledging) was related to an individual's UV crown coloration or to that of its partner. Similar observations were made for carotenoid-based plumage coloration, except for a consistent positive relationship between offspring begging intensity and maternal carotenoid-based plumage coloration. This sex-specific pattern likely reflects a maternal effect mediated via maternally derived egg substances, given that the relationship persisted when offspring were cross-fostered. This suggests that females adjust their offspring's phenotype toward own phenotype, which may facilitate in particular mother-offspring co-adaptation. Overall, our results contribute to the current state of evidence that structural or pigment-based plumage coloration of blue tits are inconsistently correlated with central life-history traits.

Chinese Sentence Classification Based on Convolutional Neural Network

Science.gov (United States)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

The colors of the alphabet: naturally-biased associations between shape and color.

Science.gov (United States)

Spector, Ferrinne; Maurer, Daphne

2011-04-01

Many letters of the alphabet are consistently mapped to specific colors in English-speaking adults, both in the general population and in individuals with grapheme-color synaesthesia who perceive letters in color. Here, across six experiments, we tested the ubiquity of the color/letter associations with typically developing toddlers, literate children, and adults. We found that pre-literate children associate O with white and X with black and discovered that they also associate I and ameboid nonsense shapes with white; Z and jagged nonsense shapes with black; and C with yellow; but do not make a number of other associations (B blue; Y yellow; A red; G green) seen in literate children and adults. The toddlers' mappings were based on the shape and not the sound of the letter. The results suggest that sensory cortical organization initially binds specific colors to some specific shapes and that learning to read can induce additional associations, likely through the influence of higher order networks as letters take on meaning.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Science.gov (United States)

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

IRAS colors of carbon stars - An optical spectroscopic test

International Nuclear Information System (INIS)

Cohen, M.; Wainscoat, R.J.; Walker, H.J.; Volk, K.; Schwartz, D.E.

1989-01-01

Optical spectra are obtained of 57 photographic counterparts to IRAS sources not previously studied spectroscopically, and expected on the basis of their IRAS colors to be M or C type stars. Confirmed carbon stars are found only in a restricted range of 12-25 index, and constitute a striking vertical sequence in the 12-25-60 micron color-color diagram. This sequence is in accord with evolutionary models for AGB stars that convert M into C stars by dredge-up, and follow loops in the color-color plane. Optically visible and optically invisible carbon stars occupy different color-color locations consistent with their representations of different evolutionary states in the life of relatively low-mass stars. 16 refs

A minimum spanning forest based classification method for dedicated breast CT images

International Nuclear Information System (INIS)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei

2015-01-01

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging

What is Color Blindness?

Science.gov (United States)

... Color Blindness? Who Is at Risk for Color Blindness? Color Blindness Causes Color Blindness Diagnosis and Treatment How Color Blindness Is Tested What Is Color Blindness? Leer en Español: ¿Qué es el daltonismo? Written ...

Preliminary Research on Grassland Fine-classification Based on MODIS

International Nuclear Information System (INIS)

Hu, Z W; Zhang, S; Yu, X Y; Wang, X S

2014-01-01

Grassland ecosystem is important for climatic regulation, maintaining the soil and water. Research on the grassland monitoring method could provide effective reference for grassland resource investigation. In this study, we used the vegetation index method for grassland classification. There are several types of climate in China. Therefore, we need to use China's Main Climate Zone Maps and divide the study region into four climate zones. Based on grassland classification system of the first nation-wide grass resource survey in China, we established a new grassland classification system which is only suitable for this research. We used MODIS images as the basic data resources, and use the expert classifier method to perform grassland classification. Based on the 1:1,000,000 Grassland Resource Map of China, we obtained the basic distribution of all the grassland types and selected 20 samples evenly distributed in each type, then used NDVI/EVI product to summarize different spectral features of different grassland types. Finally, we introduced other classification auxiliary data, such as elevation, accumulate temperature (AT), humidity index (HI) and rainfall. China's nation-wide grassland classification map is resulted by merging the grassland in different climate zone. The overall classification accuracy is 60.4%. The result indicated that expert classifier is proper for national wide grassland classification, but the classification accuracy need to be improved

An Authentication Technique Based on Classification

Institute of Scientific and Technical Information of China (English)

李钢; 杨杰

2004-01-01

We present a novel watermarking approach based on classification for authentication, in which a watermark is embedded into the host image. When the marked image is modified, the extracted watermark is also different to the original watermark, and different kinds of modification lead to different extracted watermarks. In this paper, different kinds of modification are considered as classes, and we used classification algorithm to recognize the modifications with high probability. Simulation results show that the proposed method is potential and effective.

Contourlet domain multiband deblurring based on color correlation for fluid lens cameras.

Science.gov (United States)

Tzeng, Jack; Liu, Chun-Chen; Nguyen, Truong Q

2010-10-01

Due to the novel fluid optics, unique image processing challenges are presented by the fluidic lens camera system. Developed for surgical applications, unique properties, such as no moving parts while zooming and better miniaturization than traditional glass optics, are advantages of the fluid lens. Despite these abilities, sharp color planes and blurred color planes are created by the nonuniform reaction of the liquid lens to different color wavelengths. Severe axial color aberrations are caused by this reaction. In order to deblur color images without estimating a point spread function, a contourlet filter bank system is proposed. Information from sharp color planes is used by this multiband deblurring method to improve blurred color planes. Compared to traditional Lucy-Richardson and Wiener deconvolution algorithms, significantly improved sharpness and reduced ghosting artifacts are produced by a previous wavelet-based method. Directional filtering is used by the proposed contourlet-based system to adjust to the contours of the image. An image is produced by the proposed method which has a similar level of sharpness to the previous wavelet-based method and has fewer ghosting artifacts. Conditions for when this algorithm will reduce the mean squared error are analyzed. While improving the blue color plane by using information from the green color plane is the primary focus of this paper, these methods could be adjusted to improve the red color plane. Many multiband systems such as global mapping, infrared imaging, and computer assisted surgery are natural extensions of this work. This information sharing algorithm is beneficial to any image set with high edge correlation. Improved results in the areas of deblurring, noise reduction, and resolution enhancement can be produced by the proposed algorithm.

Zirconia-based colors for ceramic glazes

International Nuclear Information System (INIS)

Eppler, R.A.

1977-01-01

The history of color development for use in ceramic glazes is outlined. The most significant modern development is based on zirconia and zircon. These materials have gained increasing acceptance in the industry since their introduction in the late 1950's and early 1960's, due to their superior stability during firing of the glaze

Image Transform Based on the Distribution of Representative Colors for Color Deficient

Science.gov (United States)

Ohata, Fukashi; Kudo, Hiroaki; Matsumoto, Tetsuya; Takeuchi, Yoshinori; Ohnishi, Noboru

This paper proposes the method to convert digital image containing distinguishing difficulty sets of colors into the image with high visibility. We set up four criteria, automatically processing by a computer, retaining continuity in color space, not making images into lower visible for people with normal color vision, and not making images not originally having distinguishing difficulty sets of colors into lower visible. We conducted the psychological experiment. We obtained the result that the visibility of a converted image had been improved at 60% for 40 images, and we confirmed the main criterion of the continuity in color space was kept.

Color profiles and stability of acylated and nonacylated anthocyanins as novel pigment sources in a lipstick model: A viable alternative to synthetic colorants.

Science.gov (United States)

Westfall, Alexandra; Giusti, Mónica

Cosmetics, such as lipstick, can affect an individual's perception of attractiveness and morale. Consumer concern with the safety of synthetic colorants has made the need for alternative natural color sources increasingly urgent. Our goal was to evaluate the feasibility of anthocyanin (ACN) extracts as colorants in lipstick formulations. Lipstick formulations were colored with ACN-rich materials. Accelerated environmental testing typical of the cosmetic industry were used: incubation at 20°, 37°, and 45°C for 12 weeks and temperature abuse cycles between 20°/37°C or -20°/20°C. Color (CIELab) and total monomeric ACN (pH-differential) changes were monitored to determine shelf stability of the product. All formulations exhibited acceptable color for lipsticks. Shelf stability was determined to exceed 2 year based on the accelerated testing conditions. Formulations containing cyanidin as their main ACN were the most stable (elderberry, purple corn, and purple sweet potato). ACNs could be used as suitable alternatives to synthetic colorants in lipid-based topical formulations.

Equivalence of the Color Trails Test and Trail Making Test in nonnative English-speakers.

Science.gov (United States)

Dugbartey, A T; Townes, B D; Mahurin, R K

2000-07-01

The Color Trails Test (CTT) has been described as a culture-fair test of visual attention, graphomotor sequencing, and effortful executive processing abilities relative to the Trail Making Test (TMT). In this study, the equivalence of the TMT and the CTT among a group of 64 bilingual Turkish university students was examined. No difference in performance on the CTT-1 and TMT Part A was found, suggesting functionally equivalent performance across both tasks. In contrast, the statistically significant differences in performance on CTT-2 and TMT Part B, as well as the interference indices for both tests, were interpreted as providing evidence for task nonequivalence of the CTT-2 and TMT Part B. Results have implications for both psychometric test development and clinical cultural neuropsychology.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

Directory of Open Access Journals (Sweden)

Jennifer Howcroft

Full Text Available Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521. Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Reliability of a treatment-based classification system for subgrouping people with low back pain.

Science.gov (United States)

Henry, Sharon M; Fritz, Julie M; Trombley, Andrea R; Bunn, Janice Y

2012-09-01

Observational, cross-sectional reliability study. To examine the interrater reliability of novice raters in their use of the treatment-based classification (TBC) system for low back pain and to explore the patterns of disagreement in classification errors. Although the interrater reliability of individual test items in the TBC system is moderate to good, some error persists in classification decision making. Understanding which classification errors are common could direct further refinement of the TBC system. Using previously recorded patient data (n = 24), 12 novice raters classified patients according to the TBC schema. These classification results were combined with those of 7 other raters, allowing examination of the overall agreement using the kappa statistic, as well as agreement/disagreement among pairwise comparisons in classification assignments. A chi-square test examined differences in percent agreement between the novice and more experienced raters and differences in classification distributions between these 2 groups of raters. Among 12 novice raters, there was 80.9% agreement in the pairs of classification (κ = 0.62; 95% confidence interval: 0.59, 0.65) and an overall 75.5% agreement (κ = 0.57; 95% confidence interval: 0.55, 0.69) for the combined data set. Raters were least likely to agree on a classification of stabilization (77.5% agreement). The overall percentage of pairwise classification judgments that disagreed was 24.5%, with the most common disagreement being between manipulation and stabilization (11.0%), followed by a mismatch between stabilization and specific exercise (8.2%). Additional refinement is needed to reduce rater disagreement that persists in the TBC decision-making algorithm, particularly in the stabilization category. J Orthop Sports Phys Ther 2012;42(9):797-805, Epub 7 June 2012. doi:10.2519/jospt.2012.4078.

Classification of schizophrenia patients based on resting-state functional network connectivity

Directory of Open Access Journals (Sweden)

Mohammad Reza Arbabshirani

2013-07-01

Full Text Available There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity features to classify schizophrenia.

NONLINEAR COLOR-METALLICITY RELATIONS OF GLOBULAR CLUSTERS. II. A TEST ON THE NONLINEARITY SCENARIO FOR COLOR BIMODALITY USING THE u-BAND COLORS: THE CASE OF M87 (NGC 4486)

International Nuclear Information System (INIS)

Yoon, Suk-Jin; Lee, Sang-Yoon; Kim, Hak-Sub; Cho, Jaeil; Chung, Chul; Sohn, Sangmo T.; Blakeslee, John P.

2011-01-01

The optical color distributions of globular clusters (GCs) in most large elliptical galaxies are bimodal. Based on the assumed linear relationship between GC colors and their metallicities, the bimodality has been taken as evidence of two GC subsystems with different metallicities in each galaxy and has led to a number of theories in the context of galaxy formation. More recent observations and modeling of GCs, however, suggests that the color-metallicity relations (CMRs) are inflected, and thus colors likely trace metallicities in a nonlinear manner. The nonlinearity could produce bimodal color distributions from a broad underlying metallicity spread, even if it is unimodal. Despite the far-reaching implications, whether CMRs are nonlinear and whether the nonlinearity indeed causes the color bimodality are still open questions. Given that the spectroscopic refinement of CMRs is still very challenging, we here propose a new photometric technique to probe the possible nonlinear nature of CMRs. In essence, a color distribution of GCs is a 'projected' distribution of their metallicities. Since the form of CMRs hinges on which color is used, the shape of color distributions varies depending significantly on the colors. Among other optical colors, the u-band related colors (e.g., u – g and u – z) are theoretically predicted to exhibit significantly less inflected CMRs than other preferred CMRs (e.g., for g – z). As a case study, we performed the Hubble Space Telescope (HST)/WFPC2 archival u-band photometry for the M87 (NGC 4486) GC system with confirmed color bimodality. We show that the u-band color distributions are significantly different from that of g – z and consistent with our model predictions. With more u-band measurements, this method will support or rule out the nonlinear CMR scenario for the origin of GC color bimodality with high confidence. The HST/WFC3 observations in F336W for nearby large elliptical galaxies are highly anticipated in this regard.

Efficient color correction method for smartphone camera-based health monitoring application.

Science.gov (United States)

Duc Dang; Chae Ho Cho; Daeik Kim; Oh Seok Kwon; Jo Woon Chong

2017-07-01

Smartphone health monitoring applications are recently highlighted due to the rapid development of hardware and software performance of smartphones. However, color characteristics of images captured by different smartphone models are dissimilar each other and this difference may give non-identical health monitoring results when the smartphone health monitoring applications monitor physiological information using their embedded smartphone cameras. In this paper, we investigate the differences in color properties of the captured images from different smartphone models and apply a color correction method to adjust dissimilar color values obtained from different smartphone cameras. Experimental results show that the color corrected images using the correction method provide much smaller color intensity errors compared to the images without correction. These results can be applied to enhance the consistency of smartphone camera-based health monitoring applications by reducing color intensity errors among the images obtained from different smartphones.

P1-15: Categorical Color Perception of LED Illuminant Color for Deuteranomals

Directory of Open Access Journals (Sweden)

Saeko Oishi

2012-10-01

Full Text Available Color information has great value in our everyday lives, but it is not mindful of people with color vision deficiency (CVD. We can choose several color names to categorize a lot of colors around us. Eleven color names (white, black, red, green, yellow, blue, brown, orange, pink, and gray are known as basic color categories, but people with CVD cannot necessarily describe colors as people who are color vision normal (CVN do. Previous studies showed that it was hard for people with CVD to discriminate illuminant color from object color, and their color perception changed largely depending on experimental conditions. In this study we investigated categorical color perception of illuminant color for deuteranomals, using a mixture of light which consists of a red, a green, and a blue LED as a test stimulus. We tested those stimuli with three luminance levels (180 cd/m2, 18 cd/m2, 1.8 cd/m2 and two visual angles (10 deg, 0.5 deg. Subjects were three deuteranomals and three people who are CVN. Our result showed that the categorical color of mild deuteranomals was similar to that of those who were CVN, but that of severe deuteranomals was not. Severe deuteranomals judged more low chromatic colors as achromatic colors than those who were CVN. The smaller visual angle or lower luminance level the test stimulus had, the more deuteranomals confused color. The results suggest that the effect of the Bezold-Brucke phenomenon is greater to deuteranomals than to those who are CVN. Furthermore, deuteranomals use not only chromatic information but also luminance information when they describe color.

Classification Of Cluster Area Forsatellite Image

Directory of Open Access Journals (Sweden)

Thwe Zin Phyo

2015-06-01

Full Text Available Abstract This paper describes area classification for Landsat7 satellite image. The main purpose of this system is to classify the area of each cluster contained in a satellite image. To classify this image firstly need to clusterthe satellite image into different land cover types. Clustering is an unsupervised learning method that aimsto classify an image into homogeneous regions. This system is implemented based on color features with K-means clustering unsupervised algorithm. This method does not need to train image before clustering.The clusters of satellite image are grouped into a set of three clusters for Landsat7 satellite image. For this work the combined band 432 from Landsat7 satellite is used as an input. Satellite imageMandalay area in 2001 is chosen to test the segmentation method. After clustering a specific range for three clustered images must be defined in order to obtain greenland water and urbanbalance.This system is implemented by using MATLAB programming language.

Color Rendering Index Thermal Stability Improvement of Glass-Based Phosphor-Converted White Light-Emitting Diodes for Solid-State Lighting

Directory of Open Access Journals (Sweden)

Chun-Chin Tsai

2014-01-01

Full Text Available High color rendering index performance has been required for phosphor-converted warm-white light-emitting diodes (PC-WWLEDs in lighting industry. The characteristics of low-temperature fabricated phosphor (yellow: Ce3+:YAG, green: Tb3+:YAG, and red: CaAlClSiN3:Eu2+ doped glass were presented for applications to high color rendering index warm-white-light-emitting diodes. Color coordinates (x, y = (0.36, 0.29, quantum yield (QY = 55.6%, color rending index (CRI = 85.3, and correlated color temperature (CCT = 3923 K were characterized. Glass-based PC-WWLEDs was found able to maintain good thermal stability for long-time high-temperature operation. QY decay, CRI remenance, and chromaticity shift were also analyzed for glass- and silicone-based high-power PC-WLEDs by thermal aging at 150°C and 250°C for industrial test standard’s aging time 1008 hours. Better than the silicone’s, thermal stability of glass-based PC-WLEDs has been improved. The resulted high color rendering index (CRI glass phosphor potentially can be used as a phosphor layer for high-performance and low-cost PC-WLEDs used in next-generation indoor solid-state lighting applications.

Classification of solid industrial waste based on ecotoxicology tests using Daphnia magna: an alternative

OpenAIRE

William Gerson Matias; Vanessa Guimarães Machado; Cátia Regina Silva de Carvalho-Pinto; Débora Monteiro Brentano; Letícia Flohr

2005-01-01

The adequate treatment and final disposal of solid industrial wastes depends on their classification into class I or II. This classification is proposed by NBR 10.004; however, it is complex and time-consuming. With a view to facilitating this classification, the use of assays with Daphnia magna is proposed. These assays make possible the identification of toxic chemicals in the leach, which denotes the presence of one of the characteristics described by NBR 10.004, the toxicity, which is a s...

Visual determinants of reduced performance on the Stroop color-word test in normal aging individuals.

Science.gov (United States)

van Boxtel, M P; ten Tusscher, M P; Metsemakers, J F; Willems, B; Jolles, J

2001-10-01

It is unknown to what extent the performance on the Stroop color-word test is affected by reduced visual function in older individuals. We tested the impact of common deficiencies in visual function (reduced distant and close acuity, reduced contrast sensitivity, and color weakness) on Stroop performance among 821 normal individuals aged 53 and older. After adjustment for age, sex, and educational level, low contrast sensitivity was associated with more time needed on card I (word naming), red/green color weakness with slower card 2 performance (color naming), and reduced distant acuity with slower performance on card 3 (interference). Half of the age-related variance in speed performance was shared with visual function. The actual impact of reduced visual function may be underestimated in this study when some of this age-related variance in Stroop performance is mediated by visual function decrements. It is suggested that reduced visual function has differential effects on Stroop performance which need to be accounted for when the Stroop test is used both in research and in clinical settings. Stroop performance measured from older individuals with unknown visual status should be interpreted with caution.

Study on activity measurement of Nostoc flagelliforme cells based on color identification

Science.gov (United States)

Wang, Yizhong; Su, Jianyu; Liu, Tiegen; Kong, Fanzhi; Jia, Shiru

2008-12-01

In order to measure the activities of Nostoc flagelliforme cells, a new method based on color identification was proposed in this paper. N. flagelliforme cells were colored with fluoreseein diaeetate. Then, an image of colored N. flagelliforme cells was taken, and changed from RGB model to HIS model. Its histogram of hue H was calculated, which was used as the input of a designed BP network. The output of the BP network was the description of measured activity of N. flagelliforme cells. After training, the activity of N. flagelliforme cells was identified by the BP network according to the histogram of H of their colored image. Experiments were conducted with satisfied results to show the feasibility and usefulness of activity measurement of N. flagelliforme cells based on color identification.