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심영석,문영식,박성한 대한전자공학회 1995 전자공학회논문지-B Vol.b32 No.11
For the factory automation(FA) of production or assembly lines, computer vision techniques have been widely used for the recognition and position-control of objects. In this application, it is very important to analyze characteristic features of each object and to find an efficient matching algorithm using the selected features. If the object has regular or homogeneous patterns, the problem is relatively simple. However, If the object is shifted or rotated, and if the depth of the input visual system is not fixed, the problem becomes very complicated. Also, in order to understand and recognize objects with concentric noise patterns, it is more effective to use feature-information represented in polar coordinates than in cartesian coordinates. In this paper, an algorithm for the recognition of objects with concentric circular noise-patterns is proposed. And position-conrtol information is calculated with the matching result. First, a filtering algorithm for eliminating concentric noise patterns is proposed to obtain concentric-feature patterns. Then a shift, rotation and scale invariant alogrithm is proposed for the recognition and position-control of objects uusing invariant feature information. Experimental results indicate the effectiveness of the proposed alogrithm.
심영석,이학준,Shim, Young-Serk,Lee, Hark-Jun 대한전자공학회 1989 전자공학회논문지 Vol. No.
본 논문에서는 4${\times}$4 블록 절단부호화를 근사화 파라메타 {($Y_{\alpha},\;Y_{\beta}),\;P_{{\beta}/{\beta}}$}에 의한 블록 근사화 및 그 파라메타 부호화의 두 과정으로 나누고, 각 과정에 대해 연구하였다. 제안된 방식은 일단 블록을 평탄 및 에지블록으로 분류하여 평탄 블록은 한개의 근사화 레벨 Y로만 근사화하도록 하였다. 에지블록의 라벨 평면 $P_{{\beta}/{\beta}}$는 준비된 32개의 표준 패턴을 이용하여 근사화하도록 노력하였고, 근사화가 어려운 것은 그대로 전송하였으며, 근사화 레벨 $Y_{\alpha},\;Y_{\beta}$는 이미 전송된 라벨 평면을 이용하여 예측 양자화한 후 Huffman 부호화하도록 하였다. 본 방식의 성능은 배경부분에서의 표현에는 약간의 문제가 있는 것으로 나타나지만 SNR 면에서는 복잡한 변환 부호화 방식보다도 좋은 결과를 보이며, 특히 에지가 잘 보존되었다. An efficient quantization and encoding of BTC (Block Truncation Coding) parameters {($Y_{\alpha},\;Y_{\beta}),\;P_{{\beta}/{\beta}}$} are investigated, In our algorithm 4${\times}$4 blocks are classified into flat or edge block. While edge block is represented by two approximation level $Y_{\alpha},\;Y_{\beta}$ with label plane $P_{{\beta}/{\beta}}$, flat block is represented by single approximation level Y. The approximation levels Y, $Y_{\alpha}$ and $Y_{\beta}$ are encoded by predictive quatization specially designed, and the label plane $P_{{\beta}/{\beta}}$ is tried to be encoded using stored 32 reference plantes. The performance of the proposed scheme has appeared comparable to much more complex transform coding in terms of SNR, although it requires more study on the representation of small slope in background.