Recognition of partially occluded objects

A computer vision system for the recognition of real world image is developed and reported. The system is capable of identifying multiple overlapped objects in a scene without stringent restrictions on their size, shape and orientation. An object shape is identified by the system through the detection of selected discrete feature segments in the contour code instead of attempting to search for a complete boundary. Consequently, an object that is partially occluded can still be recognized with its remaining unmasked portion. Extraction of salient features from an unknown geometry is performed using the nonlinear elastic matching technique. This algorithm is insensitive to sizing and distortions of the feature segments, hence reducing the problems caused by the error imposed during the image capturing process. A multilayer artificial neural network is used to provide the final identification of an unknown object based on the extracted features. A case study on the recognition of handtools with different surface reflectiveness is presented as an example. Possible improvements in the performance of the system are discussed