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This paper presents a practical optimization procedure for object detection and recognition algorithms. It is suitable for object recognition using a catadioptric omnidirectional vision system mounted on a mobile robot. We use the SIFT descriptor to obtain image features of the objects and the environment. First, sample object images are given for training and optimization procedures. Bayesian classification is used to train various test objects based on different SIFT vectors. The systemdoi:10.1109/icsmc.2009.5345895 dblp:conf/smc/WangL09 fatcat:yb3om4gqinf7vhwx5ybgmnwi3a