A parallel algorithm for real-time object recognition

The aim of this paper is to present a new generalized Hough transform-based hardware algorithm in order to detect non-analytic objects in a two-dimensional (2D) image space. Our main idea consists to use, during voting process into the 5D parameter space, only meaningful set of edge points that belong to the boundary of the target object and that feature a similar geometric property. In this paper, a same line support property has been used. This has the merit to reduce the size of the 5D parameter space, while increasing the detection accuracy. The whole algorithm was implemented into a highly parallel architecture supported by a single PC board. It is composed of a mixture of digital signal processing and field programmable gate array technologies and uses the content addressable memory as a main processing unit. Complexity evaluation of the whole system indicated that a set of 46 different images of 256×256 pixels each can be classified in real-time (e.g. under frame rate).