Visual Constructed Representations for Object Recognition and Detection

We propose a neurally inspired model for parallel visual process for recognition and detection. This model is based on the Gabor feature explicit representation construction. An input image is decomposed of different scale features through the low-pass filter. Nevertheless, recycling and overlapping again the scale features, the most likely object stored in memory can be detected on the input image. This is done by scale feature correspondence finding. Simultaneously, Gabor feature representations stored in memory are also constructed by selecting the most similar scale features to the input. We also test a recognition ability of our model, using a number of facial images of different persons. Distortion invariant recognition is also demonstrated.