Hajime Nobuhara

Fast solving method of fuzzy relational equation and its application to lossy image compression/reconstruction

A fast solving method of the solution for max continuous t-norm composite fuzzy relational equation of the type G(i, j)=(R/sup T//spl square/A/sub i/)/sup T//spl square/B/sub j/, i=1, 2, ..., I, j=1, 2, ..., J, where A/sub i//spl isin/F(X)X={x/sub 1/, x/sub 2/, ..., x/sub M/}, Bj/spl isin/F(Y) Y={y/sub 1/, y/sub 2/, ..., y/sub N/}, R/spl isin/F(X/spl times/Y), and /spl square/: max continuous t-norm composition, is proposed. It decreases the computation time IJMN(L+T+P) to JM(I+N)(L+P), where L, T, and P denote the computation time of min, t-norm, and relative pseudocomplement operations, respectively, by simplifying the conventional reconstruction equation based on the properties of t-norm and relative pseudocomplement. The method is applied to a lossy image compression and reconstruction problem, where it is confirmed that the computation time of the reconstructed image is decreased to 1/335.6 the compression rate being 0.0351, and it achieves almost equivalent performance for the conventional lossy image compression methods based on discrete cosine transform and vector quantization.

On various eigen fuzzy sets and their application to image reconstruction

In this study, we formulate and solve a problem of image reconstruction using eigen fuzzy sets. Treating images as fuzzy relations, we propose two algorithms of generating eigen fuzzy sets that are used in the reconstruction process. The first one corresponds to a convex combination of eigen fuzzy set equations, i.e., fuzzy relational equations involving convex combination of max-min and min-max compositions. In the case of the first algorithm, various eigen fuzzy sets can be generated by changing the parameter controlling the convex combination of the corresponding equations. The second algorithm generates various eigen fuzzy sets with respect to the original fuzzy relation using a permutation matrix. A thorough comparison of the proposed algorithms and a conventional algorithm which reconstructs an image using the greatest and smallest eigen fuzzy sets is presented as well. In the experiments, 10,000 artificial images of size 5x5 pixels. The approximation error in the case of the first/second algorithm is decreased to 68.2%/97.9% of that of the conventional algorithm, respectively. Furthermore, through the experimentation using real images extracted from Standard Image DataBAse (SIDBA), it is confirmed that the approximation error of the first algorithm is decreased to 41.5% of that of the conventional one.

Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time-temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time-temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry.

A digital watermarking algorithm using image compression method based on fuzzy relational equation

A digital watermarking method using image compression based on a fuzzy relational equation (ICF) is proposed. The method is based on least significant bit modification. If the coding system of ICF is not designed appropriately, the fuzzy relational equation will be unsolvable due to the watermarking (modification of compressed image). In order to avoid this problem, a condition for appropriate coding system design is represented in terms of the solvability degree of the fuzzy relational equation. Image compression and reconstruction experiments using 100 images (extracted from Corel Gallery) are performed, and it is confirmed that the signed image is indistinguishable from the unsigned one.

Detection of spiculated masses in mammograms based on Fuzzy Image processing

This paper presents an efficient technique for the detection of spiculated massesin the digitized mammogram to assist the attending radiologist in making his decisions. The presented technique consists of two stages, enhancement of spiculation masses followed by the segmentation process. Fuzzy Histogram Hyperbolization (FHH) algorithm is first used to improve the quality of the digitized mammogram images. The Fuzzy C-Mean (FCM) algorithm is then applied to the preprocessed image to initialize the segmentation. Four measures of quantifying enhancement have been developed in this work. Each measure is based on the statistical information obtained from the labelled region of interest and a border area surrounding it. The methodology is based on the assumption that target and background areas are accurately specified. We have tested the algorithms on digitized mammograms obtained from the Digital Databases for Mammographic Image Analysis Society (MIAS).

A Solution for the N -bit Parity Problem Using a Single Translated Multiplicative Neuron

A solution to the N-bit parity problem employing a single multiplicative neuron model, called translated multiplicative neuron (πt-neuron), is proposed. The πt-neuron presents the following advantages: (a) ∀N≥1, only 1 πt-neuron is necessary, with a threshold activation function and parameters defined within a specific interval; (b) no learning procedures are required; and (c) the computational cost is the same as the one associated with a simple McCulloch-Pitts neuron. Therefore, the πt-neuron solution to the N-bit parity problem has the lowest computational cost among the neural solutions presented to date.

Approximation by pseudo-linear operators

The approximation operators provided by classical approximation theory use exclusively as underlying algebraic structure the linear structure of the reals. Also they are all linear operators. We address in the present paper the following problems: Need all the approximation operators be linear? Is the linear structure the only one which allows us to construct particular approximation operators? As an answer to this problem we propose new, particular, pseudo-linear approximation operators, which are defined in some ordered semirings. We study these approximations from a theoretical point of view and we obtain that these operators have very similar properties to those provided by classical approximation theory. In this sense we obtain uniform approximation theorems of Weierstrass type, and Jackson-type error estimates in approximation by these operators.

Max-Product Shepard Approximation Operators

In crisp approximation theory the operations that are used are only the usual sum and product of reals. We propose the following problem: are sum and product the only operations that can be used in approximation theory? As an answer to this problem we propose max-product Shepard Approximation operators and we prove that these operators have very similar properties to those provided by the crisp approximation theory. In this sense we obtain uniform approximation theorem of Weierstrass type, and Jackson-type error estimate in approximation by these operators.

Using GIS to develop a mobile communications network for disaster-damaged areas

Communications network damage resulting from a large disaster causes difficulties in the ability to rapidly understand the current situation and thus make appropriate decisions towards mitigating problems, such as where to send and dispense emergency supplies. The research outlined in this paper focuses on the rapid construction of a network after a disaster occurs. This study suggests ZigBee and geographic information systems (GIS) technologies to resolve these problems and provide an effective communication system. The experimental results of the ZigBee network system are presented, examples are provided of the mapping and analysis undertaken using GIS for the disaster-stricken area of Tsukuba City, Japan, and the communications node arrangements are determined for this region. These results demonstrate the effectiveness of establishing such a communications system for supporting efforts to relieve disaster-damaged areas.

Color restoration algorithm for dynamic images under multiple luminance conditions using correction vectors

An algorithm for color restoration under multiple luminance conditions is proposed. It automatically produces correction vectors to restore the color information in the L^*a^*b^* color metric space, using color values of a target object within the well-illuminated region in a given dynamic image. The use of the correction vectors provides better image quality than that obtained by the restoration algorithm using color change vectors. An experiment is done with two real dynamic images, where a walking person in a building is observed, to evaluate the performance of the proposed algorithm in terms of color-difference. The experimental results show that the restored image by the proposed algorithm decreases the color-difference by 30% compared to the restoration algorithm using color change vectors. The proposed algorithm presents the foundation to identify the person captured by a practical security system using a low cost CCD camera.