Color image segmentation: advances and prospects

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Feature selection in machine learning: A new perspective

The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.

/pdf/computer-vision-based-fabric-defect-detection-a-survey-27mr8b2pf3.pdf

Computer-Vision-Based Fabric Defect Detection: A Survey

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

/pdf/computer-aided-tumor-detection-in-endoscopic-video-using-4n5nqty54s.pdf

Computer-aided tumor detection in endoscopic video using color wavelet features

Color image segmentation by pixel classification in an adapted hybrid color space: application to soccer image analysis

Color Spaces and Image Segmentation

In this paper, we present a new approach for color texture classification by use of Haralick features extracted from co-occurrence matrices computed from local binary pattern (LBP) images These LBP images, which are different from the color LBP initially proposed by Maenpaa and Pietikainen, are extracted from color texture images, which are coded in 28 different color spaces An iterative procedure then selects among the extracted features, those which discriminate the textures, in order to build a low dimensional feature space Experimental results, achieved with the BarkTex database, show the interest of this method with which a satisfying rate of well-classified images (856%) is obtained, with a 10-dimensional feature space

Haralick feature extraction from LBP images for color texture classification

Mono-CCD color cameras acquire only one color component at each pixel by means of their color filter array (CFA) covering the CCD sensor. To obtain a color image, a procedure - called demosaicing - is then necessary to estimate the other two missing color components at each pixel. This chapter deals with the quality of color images generated in such a way. We attempt to determine which demosaicing method provides the best results according to several comparison criteria, particularly for subsequent needs of color image analyses like edge detection.

/pdf/comparison-of-color-demosaicing-methods-l2576lwwwm.pdf

Comparison of color demosaicing methods

https://hal.archives-ouvertes.fr/hal-00732484/document

Ludovic Macaire

Papers

Color image segmentation by pixel classification in an adapted hybrid color space: application to soccer image analysis

Color Spaces and Image Segmentation

Haralick feature extraction from LBP images for color texture classification

Comparison of color demosaicing methods

Constraint scores for semi-supervised feature selection: A comparative study