Abstract
The paper presents a method of image recognition, which is inspired by research in visual cortex. The architecture of our model called CaNN is similar to the one proposed in neocognitron, LeNet or HMAX networks. It is composed of many consecutive layers with various number of planes (receptive fields). Units in the corresponding positions of the planes in one layer receive input from the same region of the precedent layer. Each plane is sensitive to one pattern. The method assumes that the pattern recognition is based on edges, which are found in the input image using Canny detector. Then, the image is processed by the network. The novelty of our method lies in the way of information processing in each layer and an application of clustering module in the last layer where the patterns are recognized. The transformations performed by the CaNN model find the own representation of the training patterns. The method is evaluated in the experimental way. The results are presented.
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Markowska-Kaczmar, U., Puchalski, A. (2011). Similar Image Recognition Inspired by Visual Cortex. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_34
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DOI: https://doi.org/10.1007/978-3-642-25330-0_34
Publisher Name: Springer, Berlin, Heidelberg
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