Abstract
Conventional computer vision systems detect object after super-resolution (SR) or image reconstruction of the whole image, which is not an economical manner. By imitating the visual system of human beings, we proposed the bionic vision system (BVS), which is mainly composed by three parts: object detection by visual attention model, object-oriented SR reconstruction and object recognition by convolutional neural networks. The visual attention model contains both bottom-up and top-down cues. The bottom-up cues integrate low-level features by the feature integration theory. An Adaboost detector imitates the top-down cues. Sparse coding and compressed sensing reconstruction realize the object-oriented SR reconstruction. The BVS was validated on license plate recognition task. Both detection performance and SR reconstruction performance are tested. Besides of these, we also test the final recognition rate, all the experimental results are quite encouraging.
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