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Author Stawiaski, Jean ♦ Decencière, Etienne
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Poor Detection ♦ Efficient Approximation ♦ Graph-cuts Optimization ♦ Segmentation Process Merges Region ♦ Interactive Medical Image Segmentation ♦ Dissimilarity Measure ♦ Adjacent Region ♦ Low Boundary ♦ Large Image Segmentation ♦ Pre-computed Low Level Segmentation ♦ Specific Criterion ♦ Minimal Surface ♦ Efficient Way ♦ Second Method Deal ♦ Watershed Transform ♦ Experimental Result ♦ Region Histogram
Description In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criterion using graph-cuts optimization. Two methods will be introduced in this paper. The first is based on regions histogram and dissimilarity measures between adjacent regions. The second method deals with efficient approximation of minimal surfaces and geodesics. Experimental results show that these techniques can efficiently be used for large images segmentation when a pre-computed low level segmentation is available. We will present these methods in the context of interactive medical image segmentation.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution in Image Anal Stereol ♦ Ecole Des Mines De Paris
Organization Centre De Morphologie Mathématique