Parsing Images into Region and Curve Processes

Abstract. Natural scenes consist of a wide variety of stochastic patterns. While many patterns are represented well by statistical models in two dimensional regions as most image segmentation work assume, some other patterns are fundamentally one dimensional and thus cause major problems in segmentation. We call the former region processes and the latter curve processes. In this paper, we propose a stochastic algorithm for parsing an image into a number of region and curve processes. The paper makes the following contributions to the literature. Firstly, it presents a generative rope model for curve processes in the form of Hidden Markov Model (HMM). The hidden layer is a Markov chain with each element being an image base selected from an over-complete basis, such as Difference of Gaussians (DOG) or Difference of Offset Gaussians (DOOG) at various scales and orientations. The rope model accounts for the geometric smoothness and photometric coherence of the curve processes. Secondly, it...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Zhuowen Tu, Song Chun Zhu
• Keywords: Computer Vision, Curve Models, Curve Processes, ECCV 2002, Hidden Markov Model, Markov Chain, Markov Chain Monte