This paper describes a mobile robot system designed to explore and map an indoor area such as is encountered in urban search and rescue mock-ups. The robot uses homogeneous artificial landmarks deployed during exploration for localization as it constructs a map, determining landmark distance and bearing with groundplane calculations from a single camera and using Kalman filtering techniques to perform localization. When implemented on a Magellan II mobile robot, the localization technique correctly localized the robot while exploring and mapping.
3D color histograms are introduced as an effective means of object recognition. No globally optimal set of color histogram parameters is known, and the choice of data-set specific parameters is far from obvious due to the size of the search space involved. Evolution Strategies (ES), a form of Evolutionary Computation, are introduced as a method of optimizing histogram parameters specific to a known data set. An ES is implemented on a 22-object, 110 image database, and a 93 percent recognition rate achieved, a significant improvement over the 86 percent recognition rate of standard histogram axes. The results demonstrate the efficacy of ES and underscore the importance of the assumptions that histogram-based recognition methods are built upon.
A method for locating the doors in an image is presented. The method integrates an edge map with color information known about the doors to provide better results than methods based purely on color or edges. The Hough transform is used to find lines and then a set of heuristics is used to find possible door regions. The door regions are tested by determining whether the region is the correct color. The combination of color and edge data allowed the system to successfully identify doors within a complex environment at different scales, orientations, lighting conditions and using different cameras.
This paper gives a comparative overview of ten of the currently available computer vision and image processing textbooks. These texts differ significantly in their coverage, scope, approach, and target audience. Because of the multi-disciplinary nature of this field, it is important to select a textbook that takes advantage of students' backgrounds and gives them the foundation necessary to integrate diverse concepts. This comparative review provides computer vision and image processing educators with a starting point from which they can select a textbook appropriate for their students' needs.
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