Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks

Signal processing and pattern recognition algorithms make exten(cid:173) sive use of convolution. In many cases, computational accuracy is not as important as computational speed. This form of noise justifies some level of quantization in order to achieve faster feature extraction . Our approach consists of approximating regions of the signal with low degree polynomi(cid:173) als, and then differentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite effectively.