International Conference on Communication, Information and Computing Technology (ICCICT-15) 12-13 May, 2015
ISBN: 978-93-83006-07-6
COLOR CONSTANCY ALGORITHMS: SIGNIFICANT STUDY Ms. Gurpreet Kaur1, Ms. Pooja2
[email protected],
[email protected] CT Group Of Institutions, Punjab Technical University. Abstract— Color constancy is just as the main visual perception system which allows visitors to recognize color in a diversity of conditions, and to see some uniformity in the color. Different color constancy algorithms are utilized like gray world, white patch, gray world 1st order derivative, gray world 2nd order derivative. This paper focuses on utilizing the gray world 2nd order derivative because this algorithm is based on 8 neighbors. The key objectives of this paper are to study the present work with color constancy algorithms and find the limitations of the edge based color constancy using 2nd order derivative and suggest suitable solution for the same.
color constancy can be an ill-posed problem and other techniques are proposed to cope with it. Color constancy is the ability to observe a comparatively stable color for an object even under changing illumination. Most computer methods are pixel-based, correcting a photo in order that its statistics satisfy assumptions like the average intensity of the scene under neutral light is world scene.
Keywords— Color constancy, Edge based hypothesis, Gray world and Chromaticity neutralization.
I. INTRODUCTION
Fig 1.Image under different illuminations II. AREAS OF COLOR CONSTANCY
COLOR is borrowed at the time of three mechanisms, i.e., the reflectance of the object, the affectability of cones, as well as the illuminant spectra. Of such mechanisms, the illuminant spectrum is the smallest amount of constant. The dissimilar aspects depend about the illumination changes, as an example daytime (daylight, noon, and evening) or inside/outside situations. Therefore, made from an object depends about the illumination to which we seeing it, is the main difficulty for computer vision. The color constancy asset solves this difficulty by the human visual system. This skill allows humans to spot made from an object individually of made from on the illuminant. Color constancy is the capacity to name colors of objects independent of made from of the light source.
a) Image Retrieval: Color based images has many applications and satisfactory results have been established by many research. Retrieval based on object colors must taken into account the factors that influence formation of color images: illumination conditions, sensor spectral senilities and surface reflectance. b) Image Classification: Color constancy can be classified into two categories as indoor and outdoor classification. We considered indoor/outdoor classification because the images of these classes present dissimilar content and are usually taken under different illumination conditions. It has been experimental studied that indoor scenes shows greater variability than outdoor scenes. c) Color Object Recognition: As outdoor classification is a complicated problem. But Bayesian color constancy Obtaining color constancy is of significance many different approach helps to a color image of an outdoor scene. Thus computer vision applications, like image retrieval, image helps in recognizing the colored objects. categorization, color object identification and object tracking. d) Object Tracking: Tracking objects in environments under Color Constancy can be a recognizable proven fact that non-uniform lighting condition is mainly challenging as the describes the human capability to estimate the best color of an view in spite of made from of illumination of their scene. practical appearance may change in space and time. We Being an image is formation of the light that falls in this area hold on to color constancy principles to find out the as well as the reflectance properties on the scene, achieving appearance variation induced by non-uniform illumination and we use this information to perform location-dependent color corrections to boost tracking performance.
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International Conference on Communication, Information and Computing Technology (ICCICT-15) 12-13 May, 2015
III. TECHNIQUES USED IN COLOR CONSTANCY Gray World - Gray-World [3] is famous color constancy method which assumes that the regular reflectance of surfaces in the world is achromatic. This assumption is however violated in real world and variations may exist. The variations are random and autonomous; the standard would converge to mean value. Color balancing algorithms can use this assumption by forcing its images to possess a common average gray value due to its R, G, and B components. In the case a graphic is taken by an electronic digital camera by means of a particular illumination environment, the effect from the individual lighting cast is easy to eliminate by enforcing the gray world assumption within the image. As a result of approximation, along with from the image is really a lot far better an original scene. White Patch - White Patch method [3] attempts to place the objects which have been actually white, from the scene; It assumes the white pixels are the brightest (I = R+G+B).White Patch approach is the conventional Color Constancy adaptation, trying to search the lightest patch for a white reference comparable to how the human visual system does. In White Patch highest value within the image is white. The White-Patch algorithm assumes that the maximum response in an image is caused by a perfect reflectance. Gray Edge 1st Order Derivative- In gray Edge 1st order derivative [4] 4-neighbouring pixels are considered. The primary derivative-based edge recognition operator try to find image edges by computing the graphic gradient values, like Roberts operator, Sobel operator, Prewitt operator. Gray Edge 2nd Order Derivative-The 8-neighbouring pixels [4] are considered. Unlike 4-connected, there is a distinction in 8-connected, more information for image correction is available. Gray Edge using 1st order derivative has not proved to become capable because each pixel considers its 4neighbouring pixels. So, in this process not all of the pixels are accessible for color correction. IV. LITERATURE SURVEY Madi et al.(2014) [5] examined that the color of a displayed image by a projector can be indistinguishable by options that come with it, the ambient light, the projection screen, and also observer. It really has raised the condition to correct the image over the display to remove these effects and to assurance a constancy of large appearances. The models for controlling the appearance of the displayed image, has been projected. The argument with respect to the goal applications, the computational color constancy may be specific at different steps of formation scheme of sensed image with a human. Projectors become a little more propagate and incorporated in multiple areas. However, the quality of the displayed image
ISBN: 978-93-83006-07-6
may be adversely afflicted with it used along with the real viewing conditions. Mark S. Drew (2013) has proposed Exemplar-based knowledge or, equally, nearest neighbor methods has presently gained affection from researchers in numerous computer science domains because of the popularity of abundant quantities of approachable data and storage capacity. In computer vision, these sorts of method in lots of problems such as view recognition, shape matching, image parsing, character recognition and object detection has doing well. The idea of exemplar-based finding out how to solves the difficulty of color constancy seems unusual at quick look since, from the beginning, associated nearest neighbor images aren't usually impacted by precisely similar illuminants and, inside the second place, gathering a dataset composed of all possible realworld images, including indoor and outdoor scenes for all possible illuminant colors and intensities, is indeed impossible, has applied. As an alternative center on surfaces inside the image and address the color constancy difficulty by unconfirmed learning of an suitable model for each and every training surface in training images. The closest neighbour models for each and every surface in a screening test image and estimate its illumination determined by comparing the statistics of pixels belonging to nearest neighbor surfaces as well as target surface, was founded. Hyunchan Ahn et.al (2013) proposed that the extensive range of options for color constancy if one wish to generate the power of visual system which identifies the intrinsic colors of objects under different illuminants. The ways in line with the low-level enumeration are trusted customer happiness short computational complex and sufficient results with sufficient parameters. All things considered, none of the methods exploit color channel correction evidently to increase made from constancy. By great deal of explanation, the tendencies reported by diffusion values and finally have a saturation weighting function into the existing methods, has founded. The power of made from constancy is essential for illuminant, surface reflectance, and camera sensitivity function, made from property of each pixel while in the image varies fine illuminant changes, then may cause the performance degradation of computer vision applications. Bleier et al. (2011) [18] presented the strategies for color constancy to estimate the non-uniform illumination. The proposed method is based on different color constancy ways to estimate the one illuminate. In this method, image is splitted into number of smaller pixels and each color correction approach is applied to every super pixel. Then these guesstimates are combined into a single illuminant color. The error on this estimate was much better than the errors under single illumination estimation approach. As a result, the result made from non-uniform illumination gives better results as compared to single color illumination. Gijsenij et al. (2012) [9] has discussed the Color constancy algorithms are mostly designed for depending on the simplifying supposition the spectral arrangement of a lightweight source is uniform across views. However, the simple truth is, this supposition is normally violated due to the presence of multiple light sources. The approach may be adequate to increase existing methods to more cautious 691
International Conference on Communication, Information and Computing Technology (ICCICT-15) 12-13 May, 2015
scenarios where the uniform light-source assumption is just too big restrictive. It's got shown that patch-based illuminant judgment will be as authentic as global illuminant judgment when light source is (approximately) uniform. The color of a lightweight source contains a significant affect object colors inside the view. So, the same camera, take similar object however in dissimilar illumination, may modify to use calculated color values. This color alteration may propose objectionable effects in digital images. Furthermore, it may negatively disturb the performance laptop or computer vision techniques for various applications including object recognition, tracking, and surveillance. With regards to color constancy is usually to correct the issue of the illuminant color, either by computing consistent features, or by converting the input image in a way that the connection among color of light source are removed. Chakrabarti et al. (2012) [10] offers an effectual maximal likelihood access for one perhaps the color constancy problem: removing from images made from cast caused by the spectral partition with the dominant scene illuminant. The statistical model for any spatial partition of colors in white balanced images is progressing out after which employing this model to infer illumination parameters as those being almost certainly under this model. The true secret estimation is always that by utilizing spatial band-pass filters to color images one unveils color distributions that are uni-modal, adequate, and well illustrated by a basic parametric form. The estimated shade of a fabric depends about the spectral and spatial distributions of that neighboring illumination. Therefore, sort use color as the continuing signal for identification, for some reason complete these exterior factors and infer a color descriptor which includes fixed despite adjustments to light. The aptitude to make this assumption termed color constancy has exhibited by a person's visual system to assertive degree, there do understand benefits to building it into machines. Javier et al.(2012) [11] has founded color illustration that happen to be constant to illuminant changes continues to an open condition in computer vision. Previously, most accesses has according to physical constraints or statistical assumptions borrowed in the scene, because very short consideration is paid to the impact that selected illuminants on one more color image, has represented. Javier et al. has proposed perceptual constraints that happen to be computed for the corrected images. The category hypothesis, which weights the pair of feasible illuminants according on their capacity to plan the corrected image onto specific colors, has defined. These colors as the normal color categories associated with essential a linguistic word, who has psychophysically deliberated. These color categories program usual color statistics, along with relevance across different cultures is indicated by the fact that the regular color name, has received. Gijsenij et al. (2012 [12] has discussed the color correction using edge based approaches to look at the illuminant efficiently. However, the several edge types live in real-world images, like material, shadow, and highlight edges. These various edge types have an isolated relation to the accuracy from the illuminant evaluation. Therefore, an analysis continues to be provided a number of edge types to the outcomes of edge-based color constancy methods happen to be
ISBN: 978-93-83006-07-6
introduced. First, an edge-based classification continues to be presented classifying edge types based on his or her photometric assets. From the evaluation, many experts have derived that specular and shadow edge types are more significant than material edges for evaluation from the illuminant. Bianco and Schettini (2012) [13] has investigated how illuminant evaluation can be exploiting color details bought from the faces automatically detected from the image. The way is based on two clarifications: first, skin colors be likely produce a group in color space, creating it a sign to estimate the illuminant from the scene; second, many photographic images are portraits or contain people, has projected. The objective of computational color correction to assess the actual color within an acquired view disregards its light source. As yet, it is an ill-posed problem, since its results lacks uniqueness and stability, various solutions available, each determined by various assumptions. If suppositions aren't in accordance with the algorithm's estimation with the actual illuminant can be quite inadequate. Eduardo Monari (2012) [14] has discussed the re-identification of person roughest task, nonetheless an unanswered problem for a number of applications in video surveillance. The accesses apply colors as key attributes for object explanation, which are in fact essential indication for re-identification. However, the shades captured by camera suffer from unidentified and varying global and local light situation within the view. Thus, color constancy is major requirement of strong color based person re-identification. Eduardo Monari provides the most recent access for automated estimation and compensation of local illumination within the scene. The accesses permit for conduct of multiple light sources within the sight, also to compensate backlight illumination concurrently, has proposed. Negrete et al. (2012) [15] has discussed that image enhancement issues addressed by analyzing the result of both well-known color constancy algorithms in grouping with gamma correction. Color constancy has power to identify the best colors, independently with the illuminant within the image. Human vision contains a natural power to correct the color connection between light sources. However, the mechanism that is certainly interested in this potential is not yet fully understood. Those effects utilizing the algorithms separately and mixed with, has studied. Algorithms classified into three types: gamut-based methods, learning methods, and statistical methods. Moreover, in line with the illumination Type the algorithms groups into: uniform and non uniform. Notwithstanding this categorization, many of the color constancy corrections, the dynamic range inside the image is expanded. V. LIMITATIONS OF EXISTING ALGORITHMS The survey has shown that still much improvement is required in the color constancy algorithms. It has been found that the most of the existing research has following limitations:a) The 2nd order derivative based edge based color constancy has the capability to considerably improve the effect of 692
International Conference on Communication, Information and Computing Technology (ICCICT-15) 12-13 May, 2015
color source, but it may causes some Gaussian noise and degrade the effect of brightness in the image. So to overcome this difficulty, we can use histogram stretching. b) Effect of the Human visual system is also ignored. Because the alteration done by the color constancy is based upon the measured light source; which can be efficient some time or may produce poor results in certain cases. c) Most of the existing research has taken the results on the available data sets; not much work is done by taking real time color source affected images. VI. COMPARISON OF VARIOUS COLOR CONSTANCY TECHNIQUES Author (s) Hyunchan Ahn et al.[7]
Gijsenij et al. [9]
Theo Gevers et al.[12]
Year
Technique
Analysis
2013
Saturation Weighting.
It will give more satisfactory results on combining with lowlevel statistics methods. Patch-based illuminant estimation can be as accurate as global illuminant estimation when the light source is Uniform. Different edges are considered based on their photometric properties. Material edges shows less valuable results. Natural image statistics are used to identify the most important characteristics of color images. The proposed
2012
2012
Patch Based Illumination Estimation.
Photometric Edge Weighting.
Gijsenij et al. [16]
2011
Natural Image Statistics.
Tara Akhavan et
2010
A new
al. [17]
ISBN: 978-93-83006-07-6
combining learning method i.e. Neural Network.
method works in the RGB Color space which is so easy to work and achieved 95% accuracy.
VII. PROPOSED METHODOLOGY In our proposed work, we will add fuzzy membership with saturation weighting technique in order to improve the results. In first step input image is obtained. After that we apply saturation weighting technique along with fuzzy logic. But if in some cases, we lose the edges of the image in that case we apply edge preserving filter as a post processing. Then final image is obtained and evaluate them on the basis of performance metrics. Input Color Image
Apply Saturation Weighting Based Color Constancy Using Fuzzy Membership
Apply Edge Preserving Filtering
Final Image
Evaluate Performance Metrics
VIII. CONCLUSION This work has reviewed some well-known color constancy algorithms. By conducting the review it has been shown that the 2nd order based gray edge algorithm provides quite good results than other algorithms. It can be shown that the most of the algorithms are point out point based but only gray edge hypothesis based algorithms derive from first order and second order derivations helping to make edge based color constancy unique. But it may degrade the effect of brightness in the image. So some work needs to be done to improve and enhance the quality of the image. In near future, we shall extend this work to use image filtering and image enhancement techniques. Fuzzy logic can also helps to achieve the quality of the image, even when the image get blurred. However, parallel programming may also be used to enhance the speed of the available algorithms. 693
International Conference on Communication, Information and Computing Technology (ICCICT-15) 12-13 May, 2015
ISBN: 978-93-83006-07-6
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