Abstract Computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination. In this paper, we adopt a deep learning framework for the illumination estimation problem. The proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant color computation. Specifically, we trained the convolutional neural network to solve the problem by casting the color constancy problem as an illumination classification problem. We designed the deep learning architecture so that the output of the network can be directly used for computing the color of the illumination. Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets.
Recently, understanding the mechanism of the human visual system (HVS) has been found to be useful in building the statistical assumptions by mimicking the human built-in ability of color constancy @cite_19 @cite_1 . One limitation of these approaches is that we are still far from fully understanding the mechanism of the HVS. In this regard, we believe our deep learning approach which train HVS inspired complex model (CNN) with large data to simulate color information processing on the human brain could make a breakthrough for the problem of the computational color constancy because we do not need to know how it works specifically.
@cite_19 @cite_1
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The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of @math or @math . Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset. Computational color constancy aims to estimate the color of the light source. The performance of many vision tasks, such as object detection and scene understanding, may benefit from color constancy by estimating the correct object colors. Since traditional color constancy methods are based on specific assumptions, none of those methods can be used as a universal predictor. Further, shallow learning schemes are used for training-based color constancy approaches, suffering from limited learning capacity. In this paper, we propose a framework using Deep Neural Networks (DNNs) to obtain an accurate light source estimator to achieve color constancy. We formulate color constancy as a DNN-based regression approach to estimate the color of the light source. The model is trained using datasets of more than a million images. Experiments show that the proposed algorithm outperforms the state-of-the-art by 9 . Especially in cross dataset validation, reducing the median angular error by 35 . Further, in our implementation, the algorithm operates at more than @math fps during Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood human visual system (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model. Color equalization algorithms exhibit a variety of behaviors described in two differing types of models: Gray World and White Patch. These two models are considered alternatives to each other in methods of color correction. They are the basis for two human visual adaptation mechanisms: Lightness Constancy and Color Constancy. The Gray World approach is typical of the Lightness Constancy adaptation because it centers the histogram dynamic, working the same way as the exposure control on a camera. Alternatively, the White Patch approach is typical of the Color Constancy adaptation, searching for the lightest patch to use as a white reference similar to how the human visual system does. The Retinex algorithm basically belongs to the White Patch family due to its reset mechanism. Searching for a way to merge these two approaches, we have developed a new chromatic correction algorithm, called Automatic Color Equalization (ACE), which is able to perform Color Constancy even if based on Gray World approach. It maintains the main Retinex idea that the color sensation derives from the comparison of the spectral lightness values across the image. We tested different performance measures on ACE, Retinex and other equalization algorithms. The results of this comparison are presented.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. This letter proposes a new kind of image quality philosophy—Modulate Quality based on Fixation Points (MQFP) based on Human Visual System (HVS) model. Dissimilar to the former HVS-based quality assessment, the new measure emphasizes particularly on modeling the jumping phenomenon of human sight instead of modeling the visual perception of human. In other words, to model the HVS using fixation points and stay-frequency instead of Contrast Sensitive Function (CSF) etc. which models the visual perception of HVS. The experiment on various frequency-distortion images indicates that the new measure is correlated with the subjective judgment more than the former HVS-based measure and is a robust measure. With the wide applications of saliency information in visual signal processing, many saliency detection methods have been proposed. However, some key characteristics of the human visual system (HVS) are still neglected in building these saliency detection models. In this paper, we propose a new saliency detection model based on the human visual sensitivity and the amplitude spectrum of quaternion Fourier transform (QFT). We use the amplitude spectrum of QFT to represent the color, intensity, and orientation distributions for image patches. The saliency value for each image patch is calculated by not only the differences between the QFT amplitude spectrum of this patch and other patches in the whole image, but also the visual impacts for these differences determined by the human visual sensitivity. The experiment results show that the proposed saliency detection model outperforms the state-of-the-art detection models. In addition, we apply our proposed model in the application of image retargeting and achieve better performance over the conventional algorithms. Illumination estimation is the process of determining the chromaticity of the illumination in an imaged scene in order to remove undesirable color casts through white-balancing. While computational color constancy is a well-studied topic in computer vision, it remains challenging due to the ill-posed nature of the problem. One class of techniques relies on low-level statistical information in the image color distribution and works under various assumptions (e.g. Grey-World, White-Patch, etc). These methods have an advantage that they are simple and fast, but often do not perform well. More recent state-of-the-art methods employ learning-based techniques that produce better results, but often rely on complex features and have long evaluation and training times. In this paper, we present a learning-based method based on four simple color features and show how to use this with an ensemble of regression trees to estimate the illumination. We demonstrate that our approach is not only faster than existing learning-based methods in terms of both evaluation and training time, but also gives the best results reported to date on modern color constancy data sets. If color appearance is to be a useful feature in identifying an object, then color appearance must remain roughly constant when the object is viewed in different contexts. People maintain approximate color constancy despite variation in the color of nearby objects and despite variation in the spectral power distribution of the ambient light. Land's retinex algorithm is a model of human color constancy. We analyze the retinex algorithm and discuss its general properties. We show that the algorithm is too sensitive to changes in the color of nearby objects to serve as an adequate model of human color constancy. Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms. In many multi-media applications it is desirable to separate the influence of the illumination sources and imaging equipment from the properties of the depicted scene. The ability of the human visual system to solve this task in many situations is known as color constancy. Technical applications of these methods include automatic color correction and illumination independent search in image databases. Many conventional computational color constancy methods assume that the effect of an illumination change can be described by a matrix multiplication with a diagonal matrix. In this paper we introduce a color normalization algorithm which computes the unique color transformation matrix which normalizes a given set of moments computed from the color distribution of an image. This normalization procedure is a generalization of the channel independent color constancy methods since general matrix transformations are considered. We compare the performance of this new normalization method with conventional color constancy methods. The experiments show that diagonal transformation matrices provide a better illumination compensation. This shows that the color moments also contain significant information about the color distributions of the objects in the image which is independent of the illumination characteristics. In another set of experiments we use the unique transformation matrix as a descriptor of the set of moments which describe the global color distribution in the image. Combining the matrices computed from two such images describes the color differences between them. We then use this as a tool for color dependent search in image databases. This matrix based color search is computationally less demanding than histogram based color search tools. Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for color constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient. The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions such as the grey-world and white patch assumptions. The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal, researchers try to simulate the Human Visual System (HVS). Visual attention is a main feature of the HVS, but few studies have been done on using it in image quality assessment. In this work, we investigate the use of the visual attention information in their final pooling step. The rationale of this choice is that an artefact is likely more annoying in a salient region than in other areas. To shed light on this point, a quality assessment campaign has been conducted during which eye movements have been recorded. The results show that applying the visual attention to image quality assessment is not trivial, even with the ground truth. Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency. Exemplar-based learning or, equally, nearest neighbor methods have recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. In computer vision, these types of technique have been successful in several problems such as scene recognition, shape matching, image parsing, character recognition, and object detection. Applying the concept of exemplar-based learning to the problem of color constancy seems odd at first glance since, in the first place, similar nearest neighbor images are not usually affected by precisely similar illuminants and, in the second place, gathering a dataset consisting of all possible real-world images, including indoor and outdoor scenes and for all possible illuminant colors and intensities, is indeed impossible. In this paper, we instead focus on surfaces in the image and address the color constancy problem by unsupervised learning of an appropriate model for each training surface in training images. We find nearest neighbor models for each surface in a test image and estimate its illumination based on comparing the statistics of pixels belonging to nearest neighbor surfaces and the target surface. The final illumination estimation results from combining these estimated illuminants over surfaces to generate a unique estimate. We show that it performs very well, for standard datasets, compared to current color constancy algorithms, including when learning based on one image dataset is applied to tests from a different dataset. The proposed method has the advantage of overcoming multi-illuminant situations, which is not possible for most current methods since they assume the color of the illuminant is constant all over the image. We show a technique to overcome the multiple illuminant situation using the proposed method and test our technique on images with two distinct sources of illumination using a multiple-illuminant color constancy dataset. The concept proposed here is a completely new approach to the color constancy problem and provides a simple learning-based framework. The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality. Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center surround retinex to a multiscale version that achieves simultaneous dynamic range compression color consistency lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour. In this work we design an adaptive color constancy algorithm that, exploiting the skin regions found in faces, is able to estimate and correct the scene illumination. The algorithm automatically switches from global to spatially varying color correction on the basis of the illuminant estimations on the different faces detected in the image. An extensive comparison with both global and local color constancy algorithms is carried out to validate the effectiveness of the proposed algorithm in terms of both statistical and perceptual significance on a large heterogeneous data set of RAW images containing faces. Traditional watermarking schemes are sensitive to geometric distortions, in which synchronisation for recovering embedded information is a challenging task because of the disorder caused by rotation, scaling or translation (RST). The existing RST-resistant watermarking methods still have limitations with respect to robustness, capacity or fidelity. In this study, the authors address several major problems in RST-invariant watermarking. The first point is how to take advantage of the high RST resilience of scale-invariant feature transform (SIFT) features, which show good performance in terms of RST-resistant pattern recognition. Since many keypoint-based watermarking methods do not discuss cropping attacks, the second issue discussed in this study is how to resist cropping using a human visual system (HVS), which also helps us to eliminate computational complexity. The third issue is the investigation of an HVS-based watermarking strategy for extracting only feature points in the human attentive area. Lastly, a variable-length watermark synchronisation algorithm using dynamic programming is proposed. Experimental results show that the proposed algorithms are practical and show superior performance in comparison with many existing works in terms of watermark capacity, watermark transparency, and the resistance to RST attacks. In this paper, a CAPTCHA is presented based on the masking characteristics of the Human Visual System (HVS). Knowing that noise can be masked by high activity regions and showing that edges can be masked by noise for a human observer while still being detected by machines, the suggested CAPTCHA is composed of English alphabets that are picked randomly and written with a combination of texture and edges with added noise such as to deceive the machine by randomly changing the visibility of characters for humans. The proposed CAPTCHA is highly legible and robust to brute-force attacks and sophisticated Object Character Recognition (OCR) segmentation algorithms.