This paper proposes a robust method for detecting step and ramp edges. In this method, an edge is defined not as a point where there is a large change in intensity, but as a region boundary based on the separability of image features which can be calculated by linear discriminant analysis. Based on this definition of an edge, its intensity can be obtained from the separability, which depends only on the shape of an edge. This characteristic enables easy selection of the optimum threshold value for the extraction of an edge, and this method can be applied to color and texture edge extraction. Experimental results have demonstrated that this proposed method is robust to noise and dulled edges, and, in addition, allows easy selection of the optimum threshold value.

This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are stored in a heap. Then the best pair can be found as the element of the root node of the binary tree corresponding to the heap. Since only adjacent pairs of regions are possible to be merged in image segmentation, this constraints of neighboring relations are represented by sorted linked lists. Then we can reduce the computation for updating the dissimilarity values and neighboring relations which are influenced by the merging of the best pair. The proposed algorithm is applied to the segmentations of a monochrome image and range images.

Active net is a deformable model which utilizes the network analogy of a physical region. In the model, the region of a target is detected by minimizing the energy defined for the sample points of the model. The region of the target is extracted using fixed network topology in the orginally proposed algorithm. In this paper, we introduce the network reconfiguration mechanisms such as tearing and division to realize multiple objects detection and complex object detecion. The introduced algorithm dynamically unlinks the arcs of the network when their strain value exceeds predefined threshold level. In the method, we propose a new image energy which improves the position sensitivity of edges without increasing computation cost. Experimental results for images taken by video camera show the validity of our approach.

An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained tesults are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.

A C1 interpolation scheme for constructing surface patch on n-sided region (n5, 6) is presented. The constructed surface patch matches the given boundary curves and cross-boundary slopes on the sides of the n-sided region (n5, 6). This scheme has relatively simple construction, and offers one degree of freedom for adjusting interior shape of the constructed interpolation surface. The polynomial precision set of the scheme includes all the polynomials of degree three or less. The experiments for comparing the proposed scheme with two schemes proposed by Gregory and Varady respectively and also shown.

High variability of object features and bad class separation of objects are the main causes for the difficulties encountered during the interpretation of ground-level natural scenes. For coping with these two problems we propose a method which extracts those regions that can be segmented and immediately recognized with sufficient reliability (core regions) in the first stage, and later try to extend these core regions up to their real object boundaries. The extraction of reliable core regions is generally difficult to achieve. Instead of using fixed sets of features and fixed parameter settings, our method employs multiple local features (including textural features) and multiple parameter settings. Not all available features may yield useful core regions, but those core regions that are extracted from these multiple features make a cntributio to the reliability of the objects they represent. The extraction mechanism computes multiple segmentations of the same object from these multiple features and parameter settings, because it is not possible to extract such regions uniquely. Then those regions are extracted which satisfy the constraints given by knowledge about the objects (shape, location, orientation, spatial relationships). Several spatially overlapping regions are combined. Combined regions obtained for several features are integrated to form core regions for the given object calss.

In this paper, a definitce relation between the TSP's optimal solution and the attracting region in the parameters space of TSP's energy function is discovered. An many attracting region relating to the global optimal solution for TSP is founded. Then a neural network algorithm with the optimized parameters by using Orthogonal Array Table Method is proposed and used to solve the Travelling Salesman Problem (TSP) for 30, 31 and 300 cities and Map-coloring Problem (MCP). These results are very satisfactory.

In this paper, we present an efficient method for the fault simulation of the reconvergent fan-out stem. Our method minimizes the fault propagating region by analyzing the topology of the circuit, whose region is smaller than that of Tulip's. The efficiency of our method is illustrated by experimental results for a set of benchmark circuits.

Correspondence based on regions rather than lines seems to be effective, as regions are usually fewer than other image features and provide global information such as size, color, adjacency, etc. In this paper, we present a region matching approach for solving the correspondence problem. Images are segmented into regions and are individually described by classification tables using region adjacencies. From the structural description of the two images, the region matching process based on color and structural similarity is carried out. First, a small number of significant regions are selected and matched by using color, and then they are used as handles for constraint propagation to match the remaining regions by using structures. Our technique was implemented by using an efficient selection and propagation algorithm and was tested with a variety of scenes.

In the delayed regulation medel, X(t+1)=AX(t){1-X(t-1)}, new bifurcation regions which have been overlooked in the past studies were found out for -1.01A0 and 2.27563A2.2838. In the former fixed point lying at 0 is destabilized at A=-1, and new type bifurcation is induced for A-1, where oscillation with saw-tooth waveform is observed. In the latter the stability once lost for A2.271 is restored for A2.27563, and the stable region continues up to A=2.2838.