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 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 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.

A vocal-tract model with boundary conditions is proposed, and it is shown that the model can be applied to a vocal-tract simulator add an estimation algorithm of vocal-tract shapes. Since our vocal-tract simulator can dynamically synthesize continuous speech with the vocal-fold vibration and the loss factor in the vocal-tract, this simulator can be used as a tool for accurate evaluation of the estimation accuracy of the formants or the characteristics of the vocal-tract. We propose a new method estimating cross-sectional area of vocal-tract from the ARMA parameters, and this method is based on the vocal-tract model and our ARMA estimation algorithms.

Recently, a model identification system ( MIS ) has been studied for ARMA parameter estimation. Since in MIS an estimator with a high-order ARMA parameter and a model reduction algorithm are used, the tracking ability of the spectral estimation is good. However in real speech analysis, estimation errors are caused by the use of a low-pass filter for A/D conversion. In order to compensate for such errors we propose a weighted MIS ( WMIS ) which includes a compensation for the characteristics of the low-pass filter. The WMIS estimates the input of the reference model and time-varying ARMA parameters, and achieves the property of rapid convergence by using a high-order model. Furthermore the algorithm for minimum realization is shown as a model reduction algorithm. The proposed algorithms are applied to synthetic speech and real speech, and it is shown that the estimated spectra sufficiently represent the variation of formants without jitters in the high-frequency part.