We study the problem of reconfiguring one list edge-coloring of a graph into another list edge-coloring by changing only one edge color assignment at a time, while at all times maintaining a list edge-coloring, given a list of allowed colors for each edge. Ito, Kamiski and Demaine gave a sufficient condition so that any list edge-coloring of a tree can be transformed into any other. In this paper, we give a new sufficient condition which improves the known one. Our sufficient condition is best possible in some sense. The proof is constructive, and yields a polynomial-time algorithm that finds a transformation between two given list edge-colorings of a tree with n vertices via O(n2) recoloring steps. We remark that the upper bound O(n2) on the number of recoloring steps is tight, because there is an infinite family of instances on paths that satisfy our sufficient condition and whose reconfiguration requires Ω(n2) recoloring steps.

Given a collection of k sets consisting of a total of n points in the plane, the distance from any point in the plane to each of the sets is defined to be the minimum among distances to each point in the set. The farthest-color Voronoi diagram is defined as a generalized Voronoi diagram of the k sets with respect to the distance functions for each of the k sets. The combinatorial complexity of the diagram is known to be Θ(kn) in the worst case. This paper initiates a study on farthest-color Voronoi diagrams having O(n) complexity. We introduce a realistic model, which defines a certain class of the diagrams with desirable geometric properties observed. We finally show that the farthest-color Voronoi diagrams under the model have linear complexity.

This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.

We investigated optical transmission characteristics of aluminum thin films with periodic hole arrays in sub-wavelength. We divided white light into several color spectra using a color filter based on the surface plasmon resonance (SPR) utilizing aluminum showing high plasma frequency. By optimizing a hole-array period, hole shape, polarization and index difference of two surface, transmittance of 30% and full-width at half-maximum of around 100 nm were achieved.

Recently, a novel approach to color image compression based on colorization has been presented. The conventional method for colorization-based image coding tends to lose the local oscillation of chrominance components that the original images had. A large number of color assignments is required to restore these oscillations. On the other hand, previous studies suggest that an oscillation of a chrominance component correlates with the oscillation of a corresponding luminance component. In this paper, we propose a new colorization-based image coding method that utilizes the local correlation between texture components of luminance and chrominance. These texture components are obtained by a total variation regularized energy minimization method. The local correlation relationships are approximated by linear functions, and their coefficients are extracted by an optimization method. This key idea enables us to represent the oscillations of chrominance components by using only a few pieces of information. Experimental results showed that our method can restore the local oscillation and code images more efficiently than the conventional method, JPEG, or JPEG2000 at a high compression rate.

The color appearance model gives us the proper brightness information and optimized display conditions for various viewing surroundings. However on conditions of low-level illumination or low background reflectivity, the performance of brightness estimation is relatively poor. Therefore, through our psychophysical experiments, we investigated the state of visual luminance adaptation for comparing single adaptations and mixed adaptations under a complex viewing field, and we also investigated background adaptation degrees and exponential nonlinearity factors for mixed adaptation models. It provides more accurate brightness predictions according to different adapting luminance, which is decided from object and background luminance.

An image appearance model called iCAM06 was designed for high dynamic range (HDR) image rendering. The dynamic range of an HDR image needs to be mapped on output devices, which is called tone compression or tone mapping. The iCAM06, the representative HDR rendering algorithm, uses tone compression for image reproduction on the low dynamic range of output devices. However, color saturation reduction occurs during its tone compression process. We propose a saturation correction method using the inverse compensation in order to recover the saturation reduction in the iCAM06. Experimental results show that the proposed method has better performance than the iCAM06 from the viewpoint of saturation accuracy and rendering preference.

This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.

This paper proposes a novel hallucination technique for color face image reconstruction in the RGB, YCbCr, HSV and CIELAB color systems. Our hallucination method depends on multilinear principal component analysis (MPCA) with a linear regression model. In the hallucination framework, many color face images are expressed in color spaces. These images can be naturally described as tensors or multilinear arrays. This novel hallucination technique can perform feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. In our experiments, we used facial images from the FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color face images. The experimental results show that a correlation between the color channel and the proposed hallucination method can reduce the complexity in the color face hallucination process.

P. Kirchberger proved that, for a finite subset X of Rd such that each point in X is painted with one of two colors, if every d+2 or fewer points in X can be separated along the colors, then all the points in X can be separated along the colors. In this paper, we show a more colorful theorem.

This paper introduces a practical color filter array (CFA) interpolation technique. Among the many technologies proposed in this field, the inter-color methods that exploit correlation between color planes generally outperform the intra-color approaches. We have found that the filtering direction, e.g., horizontal or vertical, is among the most decisive factors for the performance of the CFA interpolation. However, most of the state-of-the-art technologies are not flexible enough in determining the filtering direction. For example, filtering only in the upper direction is not usually supported. In this context, we propose an inter-color CFA interpolation using a local map called unified geometry map (UGM). In this method, the filtering direction is determined based on the similarity of the local map data. Thus, it provides more choices of the filtering directions, which enhances the probability of finding the most appropriate direction. It is confirmed through simulations that the proposal outperforms the state-of-the-art algorithms in terms of objective quality measures. In addition, the proposed scheme is as inexpensive as the conventional methods with regard to resource consumption.

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.

Let C be a set of colors, and let ω(c) be an integer cost assigned to a color c in C. An edge-coloring of a graph G is to color all the edges of G so that any two adjacent edges are colored with different colors in C. The cost ω(f) of an edge-coloring f of G is the sum of costs ω(f(e)) of colors f(e) assigned to all edges e in G. An edge-coloring f of G is optimal if ω(f) is minimum among all edge-colorings of G. In this paper, we show that the problem of finding an optimal edge-coloring of a tree T can be simply reduced in polynomial time to the minimum weight perfect matching problem for a new bipartite graph constructed from T. The reduction immediately yields an efficient simple algorithm to find an optimal edge-coloring of T in time O(n1.5Δlog(nNω)), where n is the number of vertices in T, Δ is the maximum degree of T, and Nω is the maximum absolute cost |ω(c)| of colors c in C. We then show that our result can be extended for multitrees.

As a basic tool for deriving sparse representation of a color image from its atomic-decomposition with a redundant dictionary, the authors have recently proposed a new kind of shrinkage technique, viz. color shrinkage, which utilizes inter-channel color dependence directly in the three primary color space. Among various schemes of color shrinkage, this paper particularly presents the soft color-shrinkage and the hard color-shrinkage, natural extensions of the classic soft-shrinkage and the classic hard-shrinkage respectively, and shows their advantages over the existing shrinkage approaches where the classic shrinkage techniques are applied after a color transformation such as the opponent color transformation. Moreover, this paper presents the applications of our color-shrinkage schemes to color-image processing in the redundant tight-frame transform domain, and shows their superiority over the existing shrinkage approaches.

A new recoloring method to improve visibility of indiscriminable colors for protanopes or deuteranopes is proposed. In the proposed method, yellow-blue components of a color image perceived by protanopes/deuteranopes are adequately modified. Moreover, the gamut mapping is considered to obtain proper output color values in this method.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

This paper proposes a new auto flesh tone balance algorithm for the picture that is taken for people. In this paper, the basis of auto white balance (AWB) is human face in photo. For experiment, the transfer characteristic of camera image sensor is analyzed and the camera output RGB is calculated by measuring the average face chromaticity under standard illuminant. For the face region taken under unknown illuminant, the proposed algorithm makes RGB output rate of face region become its rate of standard face color. For this, it adjusts the R and B channel and performs the chromaticity correction. Algorithm is applied to the light skin color (average face color) in Macbeth color chart and average color of various face colors that are actually measured.

The visual attributes, brightness, colorfulness, and hue, are affected by chromaticity coordinates, excitation purity, and luminance of background, and these attributes are changed according as intensity of illumination changes. Therefore there is a need to correct display's conditions by controlling image factors such as luminance offset, contrast, color gain, and gamma to maintain the perceived display quality. The focus of our study is on the formulation of a simpler and more effective model for color and tone reproduction for digital imaging systems to utilize color appearance models. It can be applicable to optimum color display conditions to give comfort and consistency visually in various ambient conditions.

This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.

The planar Hajos calculus (PHC) is the Hajos calculus with the restriction that all the graphs that appear in the construction (including a final graph) must be planar. The degree-d planar Hajos calculus (PHC(dd)) is PHC with the restriction that all the graphs that appear in the construction (including a final graph) must have maximum degree at most d. We prove the followings: (1) If PHC is polynomially bounded, then for any d ≥ 4, PHC(dd+2) can generate any non-3-colorable planar graphs of maximum degree at most d in polynomial steps. (2) If PHC can generate any non-3-colorable planar graphs of maximum degree 4 in polynomial steps, then PHC is polynomially bounded.