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.>

The AIMS (automatic interpretation using multiple sensors) system, which uses registered laser radar and thermal imagers, is discussed. Its objective is to detect and recognize man-made objects at kilometer range in outdoor scenes. The multisensor fusion approach is applied to four sensing modalities (range, intensity, velocity, and thermal) to improve both image segmentation and interpretation. Low-level attributes of image segments (regions) are computed by the segmentation modules and then converted to the KEE format. The knowledge-based interpretation modules are constructed using KEE and Lisp. AIMS applies forward chaining in a bottom-up fashion to derive object-level interpretations from databases generated by the low-level processing modules. The efficiency of the interpretaton process is enhanced by transferring nonsymbolic processing tasks to a concurrent service manager (program). A parallel implementation of the interpretation module is reported. Experimental results using real data are presented.>

An algorithm is presented that integrates segmentation maps using both region and edge segmentation maps as input. The result of integration is a region map in which each region is large and compact. The operation is efficient and independent of image sources as well as segmentation techniques. The proposed algorithm allows multiple input maps and applies user-selected weights on various information sources. The scope of integration is parametrically controlled for the desired spatial resolution. A maximum likelihood estimator provides initial solutions of edge positions and strengths from multiple inputs. An iterative procedure is then used to smooth the resultant edge patterns. The edge map is converted to a region map using closed edge contours if desired. Finally, regions are merged to ensure that every region has the required properties. Experimental results are demonstrated using various segmentation techniques and real data from laser radar and thermal sensors.>