The Research On Technology Of Image Nderstanding Based On Biological Isual Perception

As a great challenge for state-of-the-art systems, scene understanding plays animportant role in computer vision. Animals, however, can quickly arrive at ahypothesis about its main parts so that an appropriate reaction (e.g., escape) isimmediately possible when they are confronted by a visual scene. Based on thecognitive physiology and psychology research results, considering the correlationbetween image understanding and cognitive sciences, according to the structure andmechanism of animal visual perception, some models of image understanding areresearched in this paper.There are ventral and dorsal visual pathways in the human visual system, Objectrecognition in cortex is thought to be mediated by the ventral visual pathway. Threeparallel pathways, within the early stages of visual information processing, wereestablished in the retina and preserved in the lateral geniculate nucleus (LGN). Thethree pathways were then rearranged into three concurrent streams running throughdifferent compartments of area V1and V2. The V1and V2integrate the informationabout the input and output to V3, V4, and inferotemporal cortex, IT. Based onphysiological experiments in monkeys, IT has been postulated to play a central role inobject recognition. IT in turn is a major source of input to PFC involved in linkingperception to memory. The Mapping of computational architecture to visual areas, withlateral competition and feedback, is hierarchy.To segment an object from its background image for advanced vision processing, anovel bio-inspired general framework for image segmentation in complex naturescenes is investigated, which is a hierarchical system that mimics the organization oflayered early visual area in primate visual cortex. The proposed methodology consistsof two typical stages: the first stage is a parallel modular structure including threesegmenting operators based on color feature, form feature and texture feature, each ofwhich solves the segmentation problem independently for the same input. Theyimplement the similar computing as the parvocellular, the magnocellular and koniocellular pathway in LGN from the retina to the primary visual cortex. Then, afusion operation, multiple feature fusion segmentation, integrates these three featuresegmentations together through the backpropagation neuron network in the last stage,which simulates the operation of area following the LGN in primary visual cortex.Another model closely follows the computation of trickle-up and trickle-downprocessing in primate visual pathways. The trickle-down path from the frontal cortex tothe lower level visual areas, predicts incoming stimuli, based on the prior knowledgeof the classes; the computation model of this pathway includes mainly a coveringoperator, which covers the result of the trickle-up with the fragments of specific class.As two important computations in the trickle-down stage, associate method andoptimal method base on Bayesian inference are discussed to improve the performanceof the model also. The proposed approaches is applied to several segmentationexperiments of many single objects in clustering conditions, the result shows that theapproaches are capable of competing with state-of-the-art systems.The early visual area of the animal can perform a great combine function integratingmultiple features of the image to solve the challenges "where" and "what" in the scene.A model for scene image classification is presented in this work; it extends thehierarchical feed-forward model of the visual cortex. Firstly, each of three paths ofclassification uses one image property (i.e. shape, edge or color based features)independently. Then, a single classifier assigns the category of an image based on theprobability distributions of the previous outputs. Experiments show that the modelboosts the classification accuracy over the shape based model. Meanwhile, theproposed approach achieves a high accuracy comparable to other reported methods onpublicly available color image dataset. The second model for object perception mimicsthe computation of trickle-up and trickle-down process in primate visual pathway. Theinformation of high spatial frequency in an image is extracted and optimized to keepthe invariability and selectivity of an object in the trickle-up process. In parallel, thetrickle-down computation is facilitated by the low spatial frequency components topredict the possible objects and most likely context. The object recognition iscompleted by the detailed information through the trickle-up process and thesecontext-and gist-based predictions from trickle-down process. Based on the priorknowledge of the objects and scenes, several recognition experiments demonstrate that the proposed approach is good at object recognition. In addition to its relevance forcomputer vision, the success of this approach suggests a plausibility method for thecombination of forward and backward processes for object perception and sceneidentification in computer vision.Finally, the merits and disadvantages of the models above are analyzed; the futurework is referred in the last of this paper.