• 한국멀티미디어학회:학술대회논문집
• /
• 한국멀티미디어학회 2002년도 춘계학술발표논문집(상)
• /
• pp.191-194
• /
• 2002

영상분할이란 영상 내에 존재하는 객체를 배경에서 분리해내는 것을 말한다. Active Contour 모델은 객체를 영상에서 분리하는 gradient 기반의 영상분할 방식이다. 전통적인 의미의 Active Contour 모델에서 사용한 gradient 함수 기반의 영상분할은 잡영이 많고 객체와 배경간 뚜렷한 경계가 없는 영상에서는 그 한계를 보이고 있다. 이에 본 논문에서는 이러한 Active Contour 모델의 단점을 극복하기 위한 방법으로 영상 내의 진화곡선에 의존하는 에너지 함수인 Mumford-Shah Functional을 이용한 방법을 제안한다. 이 방법은 영상 내의 Active Contour를 진화시켜 Mumford-Shah 함수의 에너지를 최소화시키는 Level Set 함수를 찾고 Level Set 함수에 의해 얻어진 부분영상에서 히스토그램을 이용한 임계치(thresholding) 방식을 사용하는 보다 효과적인 객체추출 모델이다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.1315-1318
• /
• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• 한국멀티미디어학회논문지
• /
• 제20권1호
• /
• pp.10-16
• /
• 2017

This paper proposes efficient schemes for image segmentation using the region-based active contour model. The developed methods can approach the boundaries of the desired objects by evolving the curves through minimization of the Mumford-Shah energy functionals, given arbitrary curves as initial conditions. Topological changes such as splitting or merging of curves should be handled for the methods to work properly without prior knowledge of the number of objects to be segmented. This paper introduces how to change topological structure of the curves and shows experimental results by applying the methods to the images.