Opponent Color Space Research Articles

Cartoon and texture decomposition for color image in opponent color space

The Meyer model has been successfully applied to decompose cartoon component and texture component for the gray scale image, where the total variation (TV) norm and the G-norm are respectively modeled to capture the cartoon component and the texture component in an energy minimization method. In this paper, we extend this model to the color image in the opponent color space, which is closer to human perception than the RGB space. It is important to extend the TV norm and the G-norm correspondingly because the color image is viewed as a vector-valued vector. We introduce the definition of the L1 norm and L∞ norm for the vector-valued vector and accordingly define the TV norm and the G-norm for the color image. In order to handle the numerical difficulty caused by the non-differentiability of the TV norm and G-norm, the dual formulations are used to represent these norm. Then the decomposition problem is reformulated into a minimax problem. A first-order primal-dual algorithm is readily applied to compute the saddle point of the minimax problem. Numerical results are shown the performance of the proposed model.

Transformations from cone responses to opponent color spaces

AbstractWe have described a number of transformations by mapping the cone signal vector , formed by , to opponent space , formed by achromatic signal , red‐green signal and yellow‐blue signal . Two new transformations, and , are proposed, based on the CIE 2006 spectral luminous efficiency function, , and Hurvich's opponent spectral red‐green and yellow‐blue responses, and , without and with constraints, respectively. is better than in terms of best fit of and , and both transformations improved previously proposed ones. Finally, we discuss the neutral conditions in cone space using each of the new and some of the extant transformations, as well as choosing scaling factors so that the neutral conditions in cone space are satisfied.

Research on blur discrimination thresholds of three-Channel for color image

On the basis of modern theory of color vision, the blur discrimination threshold of color image with three channels (luminance, red-green and the yellow-blue channel) in the opponent color space (DKL space) is studied through visual experiments in this paper. The visual evaluation experiment is designed by the psychophysical method based on Quest algorithm. Finding the blur discrimination threshold of color image according to the DKL space is of great significance for helping us understand color perception of image. It also has a wide range of applications in areas such as color image compression during color image transmission. Two (flowers and artificial object) typical images in the McGill standard color image database were chosen as the experimental images. After converting the color space of the image from RGB to DKL space, Gaussian low-pass filtering is used to establish the fuzzy image database. Quest algorithm which is a psychological physical method is used to design the visual evaluation experiment. Eleven volunteers with normal color vision were invited to participate in the visual evaluation experiment for each of the three channels of the two images. Our main conclusions are: (1) Human eyes are more sensitive to luminance information than color information. (2) The larger the contrast of the image and the smaller the uniformity of the image, the more sensitive the human eye is to changes in image blur.

Color Threshold Functions: Application of Contrast Sensitivity Functions in Standard and High Dynamic Range Color Spaces

Contrast sensitivity functions (CSFs) describe the smallest visible contrast across a range of stimulus and viewing parameters. CSFs are useful for imaging and video applications, as contrast thresholds describe the maximum of color reproduction error that is invisible to the human observer. However, existing CSFs are limited. First, they are typically only defined for achromatic contrast. Second, even when they are defined for chromatic contrast, the thresholds are described along the cardinal dimensions of linear opponent color spaces, and therefore are difficult to relate to the dimensions of more commonly used color spaces, such as sRGB or CIE L*a*b*. Here, we adapt a recently proposed CSF to what we call color threshold functions (CTFs), which describe thresholds for color differences in more commonly used color spaces. We include color spaces with standard dynamic range gamut (sRGB, YCbCr, CIE L*a*b*, CIE L*u*v*) and high dynamic range gamut (PQ-RGB, PQ-YCbCr and ICTCP). Using CTFs, we analyze these color spaces in terms of coding efficiency and contrast threshold uniformity.

Image Retrieval Using the Fused Perceptual Color Histogram.

Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.

Color illusions also deceive CNNs for low-level vision tasks: Analysis and implications

The study of visual illusions has proven to be a very useful approach in vision science. In this work we start by showing that, while convolutional neural networks (CNNs) trained for low-level visual tasks in natural images may be deceived by brightness and color illusions, some network illusions can be inconsistent with the perception of humans. Next, we analyze where these similarities and differences may come from. On one hand, the proposed linear eigenanalysis explains the overall similarities: in simple CNNs trained for tasks like denoising or deblurring, the linear version of the network has center-surround receptive fields, and global transfer functions are very similar to the human achromatic and chromatic contrast sensitivity functions in human-like opponent color spaces. These similarities are consistent with the long-standing hypothesis that considers low-level visual illusions as a by-product of the optimization to natural environments. Specifically, here human-like features emerge from error minimization. On the other hand, the observed differences must be due to the behavior of the human visual system not explained by the linear approximation. However, our study also shows that more ‘flexible’ network architectures, with more layers and a higher degree of nonlinearity, may actually have a worse capability of reproducing visual illusions. This implies, in line with other works in the vision science literature, a word of caution on using CNNs to study human vision: on top of the intrinsic limitations of the L + NL formulation of artificial networks to model vision, the nonlinear behavior of flexible architectures may easily be markedly different from that of the visual system.

Fabric defect detection based on information entropy and frequency domain saliency

The automatic detection of defects is an important part of the fabric production process. However, existing methods of detecting defects in fabrics with periodic patterns lack adaptability and perform poorly in detection. In this paper, we propose an unsupervised fabric defect detection method based on the human visual attention mechanism. The method introduces two-dimensional entropy which can reflect the spatial distribution characteristics of images based on one-dimensional entropy, according to the relationship between information entropy and image texture. The image is reconstructed into a quaternion matrix by combining two-dimensional entropy and three feature maps that characterize the opponent color space representation of the input image. The hypercomplex Fourier transform is then used to transform the quaternion image matrix into the frequency domain. We propose a new method for local tuning of amplitude spectrum, thereby suppressing the background pattern while retaining the defect region. Finally, the inverse transform is performed to obtain a saliency map. Through experimental comparisons and a series of numerical evaluations, we demonstrate that the proposed method has a better detection effect compared to state-of-the-art methods in fabric defect detection.

Image retrieval based on gradient-structures histogram

Color perception and orientation selection are very important mechanisms of the human brain that have close relationships with feature extraction and representation. However, extracting low-level features by mimicking these mechanisms remains challenging. To address this problem, we present the gradient-structures histogram as a novel method of content-based image retrieval (CBIR). Its main highlights are: (1) a novel and easy-to-calculate local structure detector, the gradient-structures, which simulates the orientation selection mechanism based on the opponent-color space and connects it with low-level features, (2) a novel discriminative representation method that describes color, intensity and orientation features. It is convenient, as it does not require weight coefficients for color, intensity and orientation. (3) The proposed representation method has the advantages of being histogram-based and having the power to discriminate spatial layout, color and edge cues. The proposed method provides efficient CBIR performance, as demonstrated by comparative experiments in which it significantly outperformed some state-of-the-art methods, including the Bow method, local binary pattern histogram, perceptual uniform descriptor, color volume histogram, color difference histogram, some improved LBP methods and the Tree2Vector method in terms of precision/recall and AUC metrics.

Visual object tracking with discriminative correlation filtering and hybrid color feature

The technology of visual object tracking based on correlation filter has good accuracy and efficiency. However, it is still necessary to be study further on the appearance model of the target, the scale variation of the target and so on. This paper proposes a tracking algorithm based on discriminative correlation filtering and a hybrid color feature. The hybrid color feature is composed of two parts, which are compressed color name features and Histogram of Oriented Gradient features based on opponent color space. These two parts features above are extracted from the target patch, respectively. For the first part, color-name features are extracted from a target patch firstly, and then block-based compressed color-name features are extracted according to these color-name features. For the second part, opponent color features are extracted from the target patch firstly, and then HOG features are extracted from these opponent color features. At the basis of the hybrid color feature, two different discriminative correlation filters are used to estimate the translation and the scale of the target, respectively. Finally, extensive experiments show that the tracking algorithm with the hybrid color features of this paper outperforming several state-of-the-art tracking algorithms.

P‐32: Investigation of the Color Mura Mechanism and Simulation Models

The aim of this work is to investigate the mechanism of color moire/mura which appear in the display products with prism or ITO touch sensor stacked structure. We construct matlab simulation models to execute the image processing in the opponent color space by adding the perception contrast sensitivity (CSF), and the simulation intuitive color mura images match the actual phenomenon.

The Study of Spatial Frequency Channels for Human Visual System

Similar to auditory perception of sound system, color perception of the human visual system also presents a multi-frequency channel property. In order to study the multi-frequency channel mechanism of how the human visual system processes color information, the paper proposed a psychophysical experiment to measure the contrast sensitivities based on 17 color samples of 16 spatial frequencies on CIELAB opponent color space. Correlation analysis was carried out on the psychophysical experiment data, and the results show obvious linear correlations of observations for different spatial frequencies of different observers, which indicates that a linear model can be used to model how human visual system processes spatial frequency information. The results of solving the model based on the experiment data of color samples show that 9 spatial frequency tuning curves can exist in human visual system with each lightness, R–G and Y–B color channel and each channel can be represented by 3 tuning curves, which reflect the “center-around” form of the human visual receptive field. It is concluded that there are 9 spatial frequency channels in human vision system. The low frequency tuning curve of a narrow-frequency bandwidth shows the characteristics of lower level receptive field for human vision system, the medium frequency tuning curve shows a low pass property of the change of medium frequent colors and the high frequency tuning curve of a width-frequency bandwidth, which has a feedback effect on the low and medium frequency channels and shows the characteristics of higher level receptive field for human vision system, which represents the discrimination of details.

Towards a blind image quality evaluator using multi-scale second-order statistics

Natural image statistics have proved to be effective indicators in measuring quality degradations. Most of the current statistics-based Image Quality Assessment (IQA) metrics aim at utilizing features derived from first-order models. However, second-order statistics are also of great value in image quality prediction, which are not yet fully studied. In this paper, a Blind image Quality Evaluator based on Multi-scale Second-order Statistics (BQEMSS) is proposed. The distorted image is first transformed into an opponent color space, and then quality-aware features are extracted in multiple scales from the joint distribution of adjacent sub-band coefficients in the wavelet domain and the histogram of Gaussian derivative pattern in the spatial domain respectively. To quantify the statistical regularities between sub-band coefficients, three types of image dependencies are explored, including spatially adjacent dependency, sub-band orientation dependency and sub-band scale dependency. In the final step, features are stacked to form a feature vector and a regression module is employed to map the feature vectors into quality scores. Extensive experiments on several public image quality databases demonstrate that BQEMSS is superior over the relevant state-of-the-art general-purpose blind IQA models.

Detection of streaks caused by dust in the sheetfed scanners

Dust in the scanner may cause vertical streaks in the scanned image since it reflects some part of the incident light. In this paper, we propose a method for detecting streaks in the scanned images that are a direct result of dust on the scanner glass. This lets customers resolve the issue without calling the maintenance. The solution includes denoising, conversion to opponent color space, calculation of ΔE', calculation of features, and classification. We denoise the image in order to remove halftones in case the image was halftoned. Opponent color space lets us look separately at luminance channel and chrominance channels. We have developed three features that use the data in luminance and chrominance channels. Eventually, we will use these features to detect streaks, and distinguish them from content.

53-2: Wide-Color-Gamut and High-Dynamic-Range Color-Image Encoding Scheme based on CIECAM02

We propose a new image-encoding scheme for a wide-color-gamut/high-dynamic-range display. The proposed scheme consists of two stages: color conversion from input XYZ to an opponent color space (based on CIECAM02), and high-dynamic-range encoding. A sub-sampling test shows that the proposed scheme performs better than previous models.

Image copy-move forgery detection using SURF in opponent color space

Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost, they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.

A novel local mesh color texture pattern for image retrieval system

This manuscript contributes a new feature descriptor local mesh color texture pattern (LMCTP) by merging color and local spatial information for image retrieval system. The intended method combines the various features from a mixture of color spaces, the local mesh patterns of the input image. LMCTP descriptor discriminates information between the spatial color textural patterns of multi-spectral channels within the local region. It determines the relationship between surrounding neighbors of the opponent color channel. Beside this, a merged color space, I1QCb, is constructed by combining the I1 part of the I1I2I3 color space and the chromatic component images, Cb and Q, of the YCbCr and YIQ color spaces correspondingly so as to exploit the effect of joint color texture feature. The I1QCb color space, whose component of images having matching characteristics, increases the distinctive power for retrieval system. Then opponent color spaces I1Q, QCb and I1Cb are utilized for the capturing of LMCTP features. The feasibility of the proposed framework is experiments on benchmark databases such as MIT VisTex database (DB1), Corel 1000 database (DB2). Experimental study shows that the proposed pattern gives average retrieval precision of 99.8 for DB1 and 76.5 for DB2, respectively.

A Spectral-based Color Vision Deficiency Model Compatible with Dichromacy and Anomalous Trichromacy

This paper proposes a spectral-based CVD (Color Vision Deficiency) model compatible with both of dichromacy and anomalous trichromacy. The spectral projection model based on Matrix-R extracts the lost spectra as a difference in the fundamentals between the normal and the color deficient. The lost spectra are re-used for image daltonization by an optimal spectral shift to maximize the spectral visibility or minimize the visual gap from the normal. The model rationally improves the scene visibility after daltonization. The proposed algorithm is designed based on the original key ideas of 1) Acquisition of fundamental C*LMS (spectra visible to the normal) from sRGB camera image by a pseudo-inverse projection without expensive spectral image. 2) Foundation of projection matrix-RCVD onto dichromatic and anomalous trichromatic spectral spaces by combining the cone responses in the table by DeMarco&Pokorny& Smith. 3) Extraction of fundamental C*CVD (spectra visible to the dichromat or anomalous trichromat) by operating the matrix-RCVD on the fundamental C*LMS. 4) Introduction of complete OCS (Opponent-Color Space) to keep the perfect achromatic grayness in the opponent-color stage. 5) Estimation of lost spectra ΔC*CVD as the difference between visible spectra C*LMS to the normal and C*CVD to the dichromacy or anomalous trichromacy. 6) Color blindness correction (daltonization) by reviving the lost spectra ΔC*CVD with the optimal spectral shift into the visible waveband.

A NEW COLOR CORRECTION METHOD FOR UNDERWATER IMAGING

Abstract. Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.

Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN.

In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods.

Performance evaluation of new colour histogram-based interest point detectors

Interest point detection is an active area in computer vision due to its importance in many applications. Measuring the pixel-wise difference between image pixel intensities is the mechanism of most detectors that have been proposed in literature. Recently, interest point detectors were proposed that incorporated the histogram representation instead of image pixel intensity. In this paper, research that extends histogram-based interest point detectors is introduced. Four colour-space representations were used to construct new detectors: HSV, Opponent, Transformed and Ohta colour spaces. Several experiments were performed to evaluate the new colour histogram-based detectors and compare them with previous detectors. First, the proposed detectors were evaluated in an image-matching task. Then, we studied and evaluated the performance of some of the local image descriptors that were extracted from the interest points and regions detected by the proposed detectors. Finally, the four top-ranked descriptors in the descriptor evaluation experiments were used to evaluate the new colour histogram-based detectors in an image-classification task using different object and scene image datasets. The experimental results demonstrate that our new detectors possess an increased ability to distinguish and more robust in regards to image matching, particularly with respect to textured scene images that involve transformations, such as illumination, viewpoint and blur changes. Furthermore, the descriptor performance may change depending on the detector and data set type. The image-classification results demonstrate that the proposed detectors exhibit higher classification accuracy for certain descriptors and data sets than the other detectors.