Object recognition system using temporal pattern recognition networks with quantizer neuron chip

One of the biggest issues of an object recognition is the recognition with rotation invariance under a fluctuating noisy environment. We developed an object recognition system using temporal pattern recognition network with quantizer neuron chip (QNC) and a φ-s transformation of shapes and applied them to object recognition. The shape of the object is converted to a series of angles as a function of the circumference of the shape (φ-s data) and can be treated as a series of temporal patterns. The system consists of a multifunctional layered network(MFLN) with QNC and a layer of neurons with self feedback (self feedback layer). The self feedback layer unifies the temporal recognition results of networks with QNC during a certain period defined by the time constant of self feedback and this function can realize the function of selective attention to certain areas of a series of temporal patterns. As a result, the system realizes rotation invariance in recognition and we obtained 100% recognition accuracy of 50 trials with fluctuating noise taken by CCD camera.