This paper proposes number-driven perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm for an easy decision of the optimal segmentation result. A fuzzy-based homogeneity measure makes a fusion of the L*a*b* color features and the SGF texture features. Proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate an easy determination of the optimal segmentation result.

Telecommunication networks are spreading worldwide. People can communicate with each other beyond spacial restrictions using the Internet. In this situation, the demand for transmission bandwidth and storage space continues to outstrip the capacity of existing technologies. Image and video compression technology is therefore essential for the effective use of communication networks. In this paper, we propose a new method to compress images using vector quantization. Dimensions of the training vectors to prepare a codebook are determined on the basis of local fractal dimensions (LFDs) of a learning image. This means that each block size to divide the learning image is specified by the LFDs. In principle, the smaller the number of pixels in a training vector is, the larger is a codebook (low bit rate). Furthermore, the smaller the number of pixels in a codebook is, the higher is the quality of the encoded image. This is a tradeoff between compression rate and quality of the encoded image. The tradeoff can be solved by the division of images with different block sizes. Furthermore, the code-vectors are computed from the training vectors using a fuzzy k-means clustering algorithm. The performance of the proposed algorithm was evaluated by compression rate and quality of encoded images in comparison with those using fuzzy generalized Lloyd algorithm. The experiments showed that the proposed algorithm solved the tradeoff between compression rate and quality of image.

We describe the overall role of soft computing (SC) in signal processing and pattern recognition (SPPR) with specific applications to biomedical engineering, geoscience for mining and civil engineering human interfaces, and image processing. Detection of characteristic points in an electrocardiogram to implement an advanced ECG analyzer is presented which is carried out using both conventional SPPR techniques and self-organizing neural networks. Successful technologies for monitoring a geostructure by supervised and self-organizing neural networks are described. Identification of a freehand drawing by a combination of fuzzy logic and neural networks is also described. Moreover, application of fuzzy logic to image segmentation is presented. Finally, innovation of SPPR using SC technologies is discussed.

We present a rough and an accurate segmentation of natural color images using a fuzzy region-growing algorithm. In the proposed method, the color difference and the color gradient are used as the pixel features to produce an accurate segmentation, while the local fractal dimension is used as the region feature to yield a rough segmentation in a natural color image. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate a rough segmentation at the fine-texture regions and an accurate segmentation at the strong-edge regions simultaneously.

We present a rough and an accurate segmentation of natural images using a fuzzy region-growing algorithm. First, an optimum number of the blanket for local areas is determined to estimate the optimal local fractal dimension. Then, the intensity features and the local fractal-dimension feature are integrated into the fuzzy region-growing algorithm. In the proposed method, the intensity features are used to produce an accurate segmentation, while the fractal-dimension feature is used to yield a rough segmentation in a natural image. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate a rough segmentation at the fine-texture regions and an accurate segmentation at the strong-edge regions simultaneously.

We propose a multiwindowed approach to the estimation of a local fractal-dimension map as an image feature for a natural image segmentation. The fractal dimension is estimated by the blanket method by using the optimum number of blankets. The local fractal-dimension map is created for a natural image using several sizes of windows which are selected by fuzzy set theory. We also propose an image segmentation algorithm that combines a rough and an accurate segmentation by integrating different features into the region growing algorithm. The effectiveness of the multiwindowed approach and the proposed segmentation algorithm are confirmed through computer simulations.

We present a new method that integrates intensity features and a local fractal-dimension feature into a region growing algorithm for the segmentation of natural images. A fuzzy rule is used to integrate different type of feature into a segmentation algorithm. In the proposed algorithm, intensity features are used to produce an accurate segmentation, while the fractal-dimension feature is used to yield a rough segmentation in a natural image. The effective combination of the different features provides the segmented results similar to the ones by a human visual system.

An associative memory (AM) system using fuzzy sets is presented in this paper. Vectors to be stored in the AM are transformed into fuzzy sets. Fuzzy rules are constructed from the fuzzified vectors to associate vector when a stimulus vector is input to the AM system. Computational experiments are carried out to evaluate performance of the AM system. The experiments show that the AM system is responsive to feature of the stimulus vectors. In addition, the AM system performs more than 95% correct association rate until 20% of the binary valued elements of the stored vectors are flipped randomly from either zero or one.

We present a method that integrates local fractal dimension and edge information into a region growing algorithm for the segmentation of natural images. We compare two methods of estimating the local fractal dimension in the proposed segmentation algorithm. One is a blanket method and the other is a Fourier-wavelet method. We also propose a technique to store the edge information not on a pixel itself but on a boundary between pixels in the region-edge integrating algorithm in order to use the edge information more effectively and to simplify the algorithm.

The author has developed a self-organizing QRS-wave recognition system for electrocardiograms (ECGs) using neural networks. An ART2 (adaptive resonance theory) network was employed in this self-organizing neural-network system. The system consists of a preprocessor, an ART2, network, and a recognizer. The preprocessor detects R points in the ECG and divides the ECG into cardiac cycles. A QRS-wave is the part of the ECG that is between a Q point and an S point. The input to the ART2 network is one cardiac cycle from which the ART2 network indicates the approximate locations of both the Q and S points. The recognizer establishes search regions for the Q and S points. Then, it locates the Q and S points in each search region. The system uses this method to recognize a QRS-wave. Then, the ART2 network learns the new QRS-wave pattern from the incoming ECG. The ART2 network self-organizes in response to the input ECG. The average recognition error of the present system is less than 1 ms in the recognition of the Q and S points.