Multi Swarm Optimization Methodology for Classification of Images
Classification of Images
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Machine recognition by human beings from still and video images has become a very important and vigorous area of research in the communities of image processing, pattern recognition, neural networks and computer vision. Image recognition is one of the most extraordinary abilities of human vision. The core and central part of the image research is classification and extracting. Although image recognition by human beings and computer machines are developed, it is still challenging to design an automatic system for the given task. The reason is that illumination, complex background, visual angle ...
Machine recognition by human beings from still and video images has become a very important and vigorous area of research in the communities of image processing, pattern recognition, neural networks and computer vision. Image recognition is one of the most extraordinary abilities of human vision. The core and central part of the image research is classification and extracting. Although image recognition by human beings and computer machines are developed, it is still challenging to design an automatic system for the given task. The reason is that illumination, complex background, visual angle and variation expression for images are highly variable in the real world. Several techniques have been proposed for image detection including graph matching, neural networks and geometric features. Even though researchers of Psychology, Neural Sciences, Engineering, Image Processing and Computer Vision have investigated a number of issues related to image recognition by human beings and machines, still there is need to design an automatic system for this task, especially when real-time identification is required.
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