This paper addresses the problem of recognizing signs generated by a person to guide a robot. The proposed method is based on video color analysis of a moving person making signs. The analysis consists of segmentation of the middle body, arm and forearm location and recognition of the arm and forearm positions. The proposed method was experimentally tested on videos with different target colors and illumination conditions. Quantitative evaluations indicate 97.76% of correct detection of the signs in 1807 frames.

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This paper describes the implementation of a system based on Pulse Coupled Neural Networks (PCNNs) and Field Programmable Gate Arrays (FPGAs). The PCNN implemented is oriented to the industrial application of segmentation in sequences of images. The work went through several real physical stages of implementation and optimization to achieve the needed performance. The greatest performance achieved by the digital system was of 250M pixels per second, enough to process a sequence of images in real time. Details of these stages about the neuron implementation with different Altera's FPGAs families are presented. Furthermore, the implementation is compared with previous implemented schemes based on floating point DSP microprocessor.

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