Methods and Processors for Image Recognition in a Linear and Quadratic Hamming Space

Image recognition is one of the challenges facing various industries. Image recognition methods based on Euclidean and Hamming distance estimates are the most algorithmically simple and efficient. The paper proposes methods for recognition of analog and digital signals due to the linear and quadratic Euclidean distances in the Hamming space, which are characterized by simplified algorithms and reduced computational complexity. Special-purpose processors for image recognition are designed on the basis of the Hamming distance estimates and structurally optimized components.