In this paper, a new approach of mobile robotics in pattern recognition is introduced. Its originality lies in the fact that it is based on a hybrid parametric technique which uses the neural network and the eneralized Incremental Hough Transform (GIHT) for recognition of objects. The problem is first formulated as an optimization task where a cost function, representing the constraints on the solution, is to be minimized. The optimization problem is then performed by Hopfield neural network. We solve the correspondence problem for a set of segments extracted from a pair of stereo images. The segments are extracted from binary image edges using the Hough transform (HT). Its advantage is its ability to detect discontinuous patterns in noisy images but it requires a large amount of computing power. For field programmable gate arrays (FPGA) implementation our algorithm does not require any Look up Table or trigonometric calculations at run time. This algorithm leads to a significant reduction of the HT computation time and can be therefore used in real-time applications.