Vision system based on a classification connectionist algorithm
Gabriel A. Oliver, Núria Piera · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
In this work, we assume that the degree of confidence in the information provided by a TV camera is a value composed of different aspects of the original scene and image. This fact affects the whole vision system, from the acquisition step to the classification or rejection of the objects observed. The vision system presented in this paper is a simple but efficient system for the classification of plane objects. Images are recorded by a TV camera. After carrying out the contour detection of the objects present in the scene, we go through a process of obtention of some singular points on these contours with which the descriptors of the objects will be built up. Due to the loss of information introduced in the digitalization and in the segmentation of the image, some uncertainty is introduced in the process that has to be taken into account in the classification process. In that sense, it is interesting to use a classification algorithm that can accept some kind of imprecision in the representation of the objects to be classified. The classification algorithm used is based on an incremental clustering methodology termed LAMDA (Learning Algorithm for multivariate data analysis). Such a system takes into consideration the interaction of two processes, learning and recognition. It gives a representation of each class and enables us, if it is deemed necessary, to look for a specification or generalization of previously constructed classes. Examples and experimental results are presented to illustrate the performance of the system.