Showing papers by "Kuo-Chin Fan published in 1995"

A new vectorization-based approach to the skeletonization of binary images

Abstract: Thinning is a very important preprocessing step for many image analysis operations. The main drawbacks of traditional thinning algorithms are slow speed and serious deformation. To remedy these problems, a novel approach which generates the skeleton of binary patterns via block decomposition and contour vector matching is proposed. The performance of the proposed algorithm is compared with the existing algorithm. Experimental results confirm the superiority of the new approach.

Image Registration Using a New Edge-Based Approach

Abstract: A new edge-based approach for efficient image registration is proposed. The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. By using a line-fitting model, all the edge directions of the feature points are estimated from the edge outputs of a transformed image. In order to estimate the orientation difference between two partially overlapping images, a so-called “angle histogram” is calculated. From the angle histogram, the rotation angle which can be used to compensate for the difference between two target images can be decided by seeking the angle that corresponds to the maximum peak in the histogram. Based on the rotation angle, an initial matching can be performed. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided. The proposed method can tolerate roughly about 10% scaling variation and does not restrict the position and orientation of images. Further, since all the selected feature points are edge points, the restriction can significantly reduce the search space and, meanwhile, speed up the matching process. Compared with conventional algorithms, the proposed scheme is a great improvement in efficiency as well as reliability for the image registration problem.

Solving weighted graph matching problem by modified microgenetic algorithm

Abstract: Microgenetic algorithm (MGA) is genetic algorithm (GA) using a very small population size (population size<20). The weighted graph matching problem (WGMP) has received much attention in the field of pattern recognition. In this paper, a hybrid MGA with larger population is proposed to solved the weighted graph matching problem. In our hybrid microgenetic algorithm, many modules, such as local search algorithm, biased initial population, a modified selection scheme, and a refining procedure, are embedded to improve the performance of the algorithm. Experimental results show that our method outperforms a well-known method, the symmetric polynomial transform (SPT), on most instances of the weighted graph matching problems.

A feature point clustering approach to the segmentation of form documents

Abstract: Among various kinds of documents, forms are one of the important types. The prerequisite for form optical character recognition (Form OCR) is the extraction of characters from form documents. The authors present a clustering based technique for extracting characters from form documents. In this method, they treat the character extraction process as a pattern clustering problem. The feasibility of the novel method is demonstrated through experimenting various kinds of forms. Experimental results reveal the feasibility of the novel method.

Adaptive optimization for solving a class of subgraph isomorphism problems

Abstract: In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism ("I) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSl algorithms are devised. The first algorithm implements pure genetic algorithms with permutation representation. The second is a hybrid algorithm that amalgamates assignment algorithms and local search strategy to improve convergence speed. From experiments, the second algorithm shows better performance than the first one and also reveals that the approach is superior to traditional tree search approach

Separation of Chinese characters from graphics

Abstract: In this paper, we propose a robust algorithm to separate Chinese characters from line drawings. This approach is based on the clustering of all feature points in the images. Using our algorithm, all Chinese characters can be completely separated from graphics without regard to the size, orientation and location of Chinese characters even if the characters touching or overlapping line problem occurs. Experiments show that our algorithm can be applied to both geographical information systems and forms processing.

Finding of optimal stack filter by using graphic searching methods

Abstract: A graphic searching algorithm is proposed to find the optimal stack filter. The search of the optimal stack filter is reduced to a problem of finding a minimal path from the root node to the optimal node in the error cone graph (ECG). Two graphic searching techniques, the greedy and A* algorithms, are applied to avoid the searching an extremely large search space. Experimental results are given to show the efficiency and performance of our proposed method.