Hypercolumn-array based image representation and its application to shape-based object detection |
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| Affiliation: | 1. School of Computer Science, Laboratory of Cognitive Modeling and Algorithms, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China;2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China;1. Ind. Eng., Alzahra University, Tehran, Iran;2. Ind. Eng., Shahed University, Tehran, Iran;1. Department of Psychology, Sun Yat-Sen University, Guangzhou, China;2. School of Computer Science & Engineering, South China University of Technology, Guangzhou, China;1. School of Aerospace, Transport Systems and Manufacturing, Cranfield University, College Road, Bedfordshire MK43 0AL, UK;2. College of Engineering, Mathematics and Physical Systems, University of Exeter, EX4 4SB, UK;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran;1. Institute of Industrial Research, Unit 1 St Andrews Court, University of Portsmouth, Hampshire, PO1 2PR, United Kingdom;2. Seagate Technology, Langstone Road, Havant, Hampshire, PO9 1SA, United Kingdom |
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| Abstract: | Biological and psychological evidence increasingly reveals that high-level geometrical and topological features are the keys to shape-based object recognition in the brain. Attracted by the excellent performance of neural visual systems, we simulate the mechanism of hypercolumns in the mammalian cortical area V1 that selectively responds to oriented bar stimuli. We design an orderly-arranged hypercolumn array to extract and represent linear or near-linear stimuli in an image. Each unit of this array covers stimuli of various orientations in a small area, and multiple units together produce a low-dimensional vector to describe shape. Based on the neighborhood of units in the array, we construct a graph whose node represents a short line segment with a certain position and slope. Therefore, a contour segment in the image can be represented with a route in this graph. The graph converts an image, comprised of typically unstructured raw data, into structured and semantic-enriched data. We search along the routes in the graph and compare them with a shape template for object detection. The graph greatly upgrades the level of image representation, remarkably reduces the load of combinations, significantly improves the efficiency of object searching, and facilitates the intervening of high-level knowledge. This work provides a systematic infrastructure for shape-based object recognition. |
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| Keywords: | Orientation column Shape representation Object recognition Primary visual cortex |
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