• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.173-174
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• 2012

문자 영역의 추출은 명함 등 문서의 정형화된 문자 인식, 비전 기반 감시 시스템에서의 간판, 부호 등의 자연영상에서의 문자 인식 등 다양한 분야에 활용될 수 있다. 우리가 관심을 갖는 문자는 간판이나 이름표 등 다른 이에게 정보를 전달해주는 기능을 하는 것으로 그 전배경의 구분이 명확하다. 이러한 특징은 히스토그램의 변화와 관련이 되어 있으며 본 논문에서는 그 변화를 분석함으로써 문자 영역 추출 방법을 제안한다.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.375-381
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• 2016

Recently, a foreground-background segmentation using codebook model has been researched actively. The codebook is created one for each pixel in the image. The codewords are vector-quantized representative values of same positional training samples from the input image sequences. The training is necessary for a long time in the most of codebook-based algorithms. In this paper, the initial codebook model is generated simply using median operation with several image frames. The initial codebook is updated to adapt the dynamic changes of backgrounds based on the frequencies of codewords that matched to input pixel during the detection process. We implemented the proposed algorithm in the environment of visual c++ with opencv 3.0, and tested to some of the public video sequences from PETS2009. The test sequences contain the various scenarios including quasi-periodic motion images, loitering objects in the local area for a short time, etc. The experimental results show that the proposed algorithm has good performance compared to the GMM algorithm and standard codebook algorithm.