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<p>We present an algorithm that integrates multiple region segmentation maps and edge maps. It operates independently of image sources and specific region-segmentation or edge-detection techniques. User-specified weights and the arbitrary mixing of region/edge maps are allowed. The integration algorithm enables multiple edge detection/region segmentation modules to work in parallel as front ends. The solution procedure consists of three steps. A maximum likelihood estimator provides initial solutions to the positions of edge pixels from various inputs. An iterative procedure using only local information (without edge tracing) then minimizes the contour curvature. Finally, regions are merged to guarantee that each region is large and compact. The channel-resolution width controls the spatial scope of the initial estimation and contour smoothing to facilitate multiscale processing. Experimental results are demonstrated using data from different types of sensors and processing techniques. The results show an improvement over individual inputs and a strong resemblance to human-generated segmentation.</p>
image segmentation maps; region/edge maps; information integration; edge detection; region segmentation modules; maximum likelihood estimator; edge pixels; iterative procedure; contour curvature; contour smoothing; multiscale processing; edge detection; filtering and prediction theory; image segmentation; iterative methods; optimisation; probability

C. Chu and J. Aggarwal, "The Integration of Image Segmentation Maps using Region and Edge Information," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 1241-1252, 1993.
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