An image recognition system can be based on a single-layer neural network composed of Min/Max nodes. This principle is easy to use for greyscale images. However, this article deals with the possibilities of utilising neural nets for colour image recognition. Several principles are demonstrated and tested by recently developed software. A new modified Min/Max node Single Layer Net, suitable for recognition in HSV (Hue Saturation Value) colour space, is presented in this paper.
This article deals with a neural network based on Min/Max nodes and its utilisation for image recognition purposes. The general concepts of the Min/Max nodes and the single-layer neural networks are outlined. The developed software systems for simulation are briefly introduced and the results of simulations with the various settings of a neural net are presented. The subject of simulations was the recognition of human faces. Finally, the hardware design of the neural network in VHDL is shown. The design demonstrates the ease of systems realisation and the achieving of high performance.
The Hypercolumri (HCM) neural network model is an unsupervised
competitive network consisting of hierarchical layers of the Hierarchical Self-Organizing Map (HSOM) neural networks arranged by similar to the cell planes in the Neocognitron (NC) neural network. The HCM model combines the advantages of both the HSOM and the NC while rejecting their disadvantages, and alleviates many difficulties associated with image recognition applications. It can recognize images with variant objects size, position, orientation, and spatial resolution. However, due to the hierarchical structure of the HCM model, the network spends a long tirne in the recognition. In this paper, the HCM model is introduced with a new competitive algorithm that reduces the network recognition tinie into a realtime range. The proposed algorithm uses the subset frorri the most discriminate codebook of the network weights to find the winner of each HSOM in the hrst layer of the HCM model.