This paper proposes a channel allocation principle that prevents TCP throughput degradation in multihop transmissions in a mesh network based on the carrier sense multiple access with collision avoidance (CSMA/CA) MAC protocol. We first address the relationship between the network topology of wireless nodes and the TCP throughput degradation based on computer simulations. The channel allocation principle is discussed in terms of resolution into a coloring problem based on throughput degradation. The number of required channels for the proposed channel allocation principle is evaluated and it is shown that two channels are sufficient for more than 96% simulated multihop patterns. The proposed channel allocation principle is extendable to generic mesh networks. We also clarify the number of required channels for mesh networks. The simulation results show that three channels are sufficient for more than 98% patterns in the generic mesh networks when the number of nodes is less than 10.

In this paper, we present a pixel-level color image sensor with efficient ambient light suppression using a modulated RGB flashlight to support a recognition system. The image sensor employs bidirectional photocurrent integrators for pixel-level demodulation and ambient light suppression. It demodulates a projected flashlight with suppression of an ambient light at short intervals during an exposure period. In the imaging system using an RGB modulated flashlight, every pixel provides innate color and depth information of a target object for color-based categorization and depth-key object extraction. We have designed and fabricated a prototype chip with 6464 pixels using a 0.35 µm CMOS process. Color image reconstruction and time-of-flight range finding have been performed for the feasibility test.

Digital cinema will continue, for some time, to use image signals converted from the density values of film stock through some form of digitization. This paper investigates the required numbers of quantization bits for both intensity and density. Equations for the color differences created by quantization distortion are derived on the premise that the uniform color space L* a* b* can be used to evaluate color differences in digitized pictorial color images. The location of the quantized sample that yields the maximum color difference in the color gamut is theoretically analyzed with the proviso that the color difference must be below the perceivable limit of human visual systems. The result shows that the maximum color difference is located on a ridge line or a surface of the color gamut. This can reduce the computational burden for determining the required precision for color quantization. Design examples of quantization resolution are also shown by applying the proposed evaluation method to three actual color spaces: NTSC, HDTV, and ROMM.

Liquid-crystal devices with in-plane switching electrodes (IPS-LCD) are superior to twisted nematic ones in their wider range of viewing angle, but show serious color shift with viewing angle. The color shift is a phenomenon governed by the three-dimensional orientation of liquid-crystal molecules. In order to evaluate such effects numerically, light wave propagation in the IPS-LCD is studied using a two-dimensional finite-difference time-domain method, where all six components of electromagnetic field are analysed and the three-dimensional properties of liquid-crystal materials are taken into account through the dielectric tensor. The computational space termination is provided by a combination of the uniaxial perfectly matched layer and periodic boundary conditions. It is found for the first time numerically that the color shift effects strongly depend on the asymmetrical profile of liquid-crystal orientation, which is originated from the small pretilt angle.

We have developed a two-dimensional flat-panel imager (FPI) utilizing conventional amorphous silicon (a-Si) thin film transistor (TFT) technology for AM-LCDs, and we have made a prototype. We can experimentally manufacture the FPI basically by utilizing conventional production lines of AM-LCDs, because the imager is based on the TFT array for AM-LCDs. The TFT performs both switching and photo-detecting functions itself. Using the FPI, we can capture monochrome images in real time, and can also achieve full-color images by introducing time-sequential driving based on a color backlight system with RGB-LEDs. The reliability of the TFT under bias and irradiation stress caused by capturing images is maintained by introducing an original driving method and processing the captured image. By making use of advantages the FPI has over conventional imaging systems, we hope that the FPI will be a useful compact imaging device for documents, pictures, fingerprints, and the like.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

In this paper, a simple and practical corresponding-color reproduction model based on the chromatic adaptation of the human visual system (HVS), named a modified von Kries chromatic-adaptation model, is proposed for TV and PC monitors under a variety of viewing conditions. We derived the proposed corresponding-color reproduction model based on Breneman's corresponding-color data. The proposed model has a 1-2% less error than Fairchild's and Breneman's model, 11.5% less error than von Kries' model and 60% less error than CIECAM97s' model in terms of color reproduction errors, (). Also, these tendency is similar in color reproduction errors, . We implemented a corresponding-color reproduction system using the proposed model under a variety of viewing conditions. For the determination of viewing conditions, illuminant correlated color temperatures (CCTs) are measured by yellow/cyan sensors. These measured surround illuminant CCTs can estimate an adaptated neutral point of the HVS in TV viewing conditions. Experiments were carried out to assess the proposed model performance in terms of color fidelity by comparing complex images on a LCD monitor under illuminants from 2500 K to 7500 K. We confirmed that the implemented system using the proposed model can predict corresponding-color data very well under a variety of viewing conditions. Therefore, by applying the proposed model and system to the LCD, the reproduction colors viewed in real surrounding viewing conditions on the LCD could appear the same as the original object colors under standard viewing conditions. Furthermore, they could be applied to any other color display device such as a CRT, a PDP, and a DLP in order to get better reproduction colors.

Recently, the mulitihop wireless network system attracts the interest of many people as a communication network system of the next generation. The multihop wireless network has unique features in which neither base stations nor wired backbone networks are required and a terminal can communicate with the other terminal beyond the transmission range by multihopping. In this network, a communication link between two terminals which can communicate directly is required a channel. Since cochannel interference may occur, we need to assign channels to communication links carefully. In this paper, we describe a channel assignment strategy which takes the degree of cochannel interference into consideration, and we evaluate an effectiveness of this strategy by computer simulations. We show that this strategy is more effective than a strategy which does not take the degree of cochannel interference into consideration. And we also consider a few channel assignment algorithms briefly.

A nearly equitable edge-coloring of a multigraph is a coloring such that edges incident to each vertex are colored equitably in number. This problem was solved in O(kn2) time, where n and k are the numbers of the edges and the colors, respectively. The running time was improved to be O(n2/k + n|V|) later. We present a more efficient algorithm for this problem that runs in O(n2/k) time.

H-coloring problem is a coloring problem with restrictions such that some pairs of colors cannot be used for adjacent vertices, where H is a graph representing the restrictions of colors. We deal with the case that H is the complement graph of a cycle of odd order 2p + 1. This paper presents the following results: (1) chordal graphs and internally maximal planar graphs are -colorable if and only if they are p-colorable (p 2), (2) -coloring problem on planar graphs is NP-complete, and (3) there exists a class that includes infinitely many -colorable but non-3-colorable planar graphs.

Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.

A total coloring of a graph G is to color all vertices and edges of G so that no two adjacent or incident elements receive the same color. Let C be a set of colors, and let ω be a cost function which assigns to each color c in C a real number ω(c) as a cost of c. A total coloring f of G is called an optimal total coloring if the sum of costs ω(f(x)) of colors f(x) assigned to all vertices and edges x is as small as possible. In this paper, we give an algorithm to find an optimal total coloring of any tree T in time O(nΔ3) where n is the number of vertices in T and Δ is the maximum degree of T.

In this paper, we propose a new region extraction method with a chromakey technique using a two-tone striped background. A chromakey compositing is a technique for separating actors or actresses from a background, and then compositing a different background. The conventional chromakey technique usually uses an unicolored blue or green background, and has a problem that one's clothes are regarded as the background if their colors are same with the background's color. Therefore, we use two-tone striped background and utilize the adjacency condition between two-tone striped areas on the background to extract the foreground regions whose colors are same with the background. The procedure of our proposed method consists of four steps: 1) background color extraction, 2) striped region extraction, 3) foreground extraction, and 4) image composition. As to the background color extraction, the color space approach is used. As to the striped region extraction, it is difficult to extract striped region by a color space approach because the color of this region may be a composite of two background colors and different from them. Therefore, the striped region is extracted from adjacency conditions between two background colors. As to the foreground extraction, the boundary between the foreground and the background is detected to recheck the foreground region whose color is same as the background, and the background region whose color is same as the foreground. To detect the region whose color is same as the background, the adjacency conditions with the striped region are utilized. As to the image composition, the process that smoothes the color of the foreground's boundary against the new background is carried out to create natural images. The validity of proposed method is shown through experiments with the foreground objects whose color is same as the background color.

Color histograms have been considered to be effective for color image indexing and retrieval. However, the histogram only represents the global statistical color information. We propose a new method: A Spatial Weighted Color Histogram (SWCH), for image retrieval. The color space of a color image is partitioned into several color subsets according to hue, saturation and value in HSV color space. Then, the spatial center moment of each subset is calculated as the weight of the corresponding subset. Experiments show that our method is more effective in indexing color image and insensitive to intensity variations.

In databases based on a multi-aspects object data model whcih enables multiple aspects of a real-world entity to be represented and to be acquired/lost dynamically, Object Migration (OM) updating membership relationships between an object and classes occurs, as the properties of the object evolve in its lifetime. We have proposed an OM behavior modeling framework using Colored Petri Nets (CPN) to analyze OM behavior. Based on the proposed framework, this paper presents a technique for constructing OM behavior models from OM constraint descriptions and class schemas as its input. The presented technique makes it easy to construct consistent and complete OM behavior models, since OM constraints are described in a simple, modular, and declarative form.

Usually, paintings are more appealing than photographic images. This is because paintings can incorporate styles based on the artist's subjective view of motif. This style can be distinguished by looking at elements such as motif, color, shape deformation and brush texture. In our work, we focus on the effect of "color" element and devise a method for transforming the color of an input photograph according to a reference painting. To do this, we consider basic color category concepts in the color transformation process. We assume that color transformations from one basic color category to another may cause peculiar feelings. Therefore, we restrict each color transformation within the same basic color category. For this, our algorithm first categorizes each pixel color of a photograph into one of eleven basic color categories. Next, for every pixel color of the photograph, the algorithm finds its corresponding color in the same category of a reference painting. Finally, the algorithm substitutes the pixel color with its corresponding color. In this way, we achieve large but natural color transformations of an image.

We developed a process to fabricate optical functions such as, lens, prism, or diffuser directly on to a glass substrate. Processes include precision mastering by diamond cutting, and multi-layer photopolymer (2P) molding process to realize flat surface and integration of multiple functions with a good alignment within few micrometers.

In this paper we present an unsupervised color image segmentation algorithm based on statistical models. We have adopted the Gaussian mixture model to represent the distribution of color feature vectors. A novel deterministic annealing EM and mean field theory from statistical mechanics are used to compute the posterior probability distribution of each pixel and estimate the parameters of the Gaussian Mixture Model. We describe the noncontexture segmentation algorithm that uses a deterministic annealing approach and the contexture segmentation algorithm that uses the mean field theory. The experimental results show that the deterministic annealing EM and mean field theory provide a global optimal solution for the maximum likelihood estimators and that these algorithms can efficiently segment the real image.

A novel effective method for detection and removal impulse noise in highly corrupted color images is proposed. This detection-estimation method consists of two steps. Outliers are first detected using spatial relations between the color components. Then the detected noise pixels are replaced with the output of the vector median filter over a local spatially connected area excluding the outliers. Simulation results in a test color image show a superior performance of the proposed filtering algorithm comparing to the conventional vector median filter. The comparisons are made using a mean square error and a mean absolute error criteria.

In this paper color image compression using a fuzzy Hopfield-model net based on rough-set reasoning is created to generate optimal codebook based on Vector Quantization (VQ) in Discrete Wavelet Transform (DWT). The main purpose is to embed rough-set learning scheme into the fuzzy Hopfield network to construct a compression system named Rough Fuzzy Hopfield Net (RFHN). First a color image is decomposed into 3-D pyramid structure with various frequency bands. Then the RFHN is used to create different codebooks for various bands. The energy function of RFHN is defined as the upper- and lower-bound fuzzy membership grades between training samples and codevectors. Finally, near global-minimum codebooks in frequency domain can be obtained when the energy function converges to a stable state. Therefore, only 32/N pixels are selected as the training samples if a 3N-dimensional color image was used. In the simulation results, the proposed network not only reduces the consuming time but also preserves the compression performance.