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염동훈,이상학,김두영 동아대학교 정보기술연구소 2002 情報技術硏究所論文誌 Vol.9 No.2
This paper investigated the problem of image modeling segmentation using random field models. The paper presented the MLL(multi-level-logistic) model which are commonly used in image processing, and the characteristics of this model were examined. For image segmentation, a doubly stochastic or hierarchical model was presented and applied to the segmentation of noisy images and textured images. Finally this paper investigated the problem of phase transition phenomena appearing in the random field model-based image processing. The phase transition problem makes it difficult to realize images which consist of moderate-to-large scale regions.
염동훈,주동현,김하형,김두영 동아대학교 정보기술연구소 2001 情報通信硏究所論文誌 Vol.8 No.2
Iterative curve evolution techniques are powerful methods for image segmentation. In this paper, we present a now geometric active contour model based on level set methods introduced by Osher & Sethian for detection of object boundaries or shape. We adopt sigma filtering method for removing noise from original image. Classical methods allow only one-way curve evolutions : shrinking or expanding of the curve. Thus, the initial curve must encircle all the objects to be segmented or several curves must be used, each one totally inside one object. But our method allows a two-way curve evolution : parts of the curve evolve in the outward direction while others evolve in the inward direction. It offers much more freedom in the initial curve position than with a classical geodesic search method. Our algorithm performs accurate and precise segmentations from noisy images with complex objects(including sharp angles, deep concavities or holes). Besides it easily handled curve topology changes. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image.
윤곽선 정보와 watershed segmentation을 이용한 영역 추출
이원효,설경호,염동훈,고기영,김두영 동아대학교 정보기술연구소 2003 情報技術硏究所論文誌 Vol.11 No.1
This paper proposed a extracting method of the region for image using segmentation and edge information. First proposed algorithm extract information using canny edge detector and the image was divided by watershed segmentation. And it extract the image with edge information by merging region. Finally we compare the proposed method with levelset method. In the result proposed method not only extract the image with accurate region but also reduce operation time.