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A Biologically Inspired Optical Flow System: An Application for Motion Segmentation and Object Identification - Softcover
Synopsis
Optical flow is possibly the most used method for motion segmentation. However its application is often restricted to off-line processing as it requires extensive computational resources and time. In this work, we explore an optical flow method derived from research on the vision system of diptereous insects. The proposed method, Biological Optical Flow (BioOF) was implemented using a series of filters, and therefore is much faster than any existing machine-coded optical flow algorithm. Like other optical flow methods, the output of the BioOF has two components: horizontal and vertical optical flows -- both of them are combined in order to get a better final result in terms of motion segmentation. The result is a framework that can extract an excellent contour of the moving objects segmented out from the images. Finally the object contour is projected onto a Fourier feature space, leading to a representation of the object that is rotational and translational invariant. Over the Fourier feature space, various classification algorithms are investigated for object recognition.
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About the Author
Dr. DeSouza received a Ph.D. from Purdue University. He is withthe ECE Depart at the University of Missouri. His researchinterests include robotic vision, computational intelligence,mobile robotics, and multi-view 3D vision. He is the chair of theIEEE-CIS MU Chapter and a member of the IEEE CIS, IEEE R&A, andIEEE SMC Societies.
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A Biologically Inspired Optical Flow System
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Optical flow is possibly the most used method for motion segmentation. However its application is often restricted to off-line processing as it requires extensive computational resources and time. In this work, we explore an optical flow method derived from research on the vision system of diptereous insects. The proposed method, Biological Optical Flow (BioOF) was implemented using a series of filters, and therefore is much faster than any existing machine-coded optical flow algorithm. Like other optical flow methods, the output of the BioOF has two components: horizontal and vertical optical flows -- both of them are combined in order to get a better final result in terms of motion segmentation. The result is a framework that can extract an excellent contour of the moving objects segmented out from the images. Finally the object contour is projected onto a Fourier feature space, leading to a representation of the object that is rotational and translational invariant. Over the Fourier feature space, various classification algorithms are investigated for object recognition. 72 pp. Englisch. Seller Inventory # 9783838341224
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A Biologically Inspired Optical Flow System
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Optical flow is possibly the most used method for motion segmentation. However its application is often restricted to off-line processing as it requires extensive computational resources and time. In this work, we explore an optical flow method derived from. Seller Inventory # 5414633
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A Biologically Inspired Optical Flow System : An Application for Motion Segmentation and Object Identification
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ISBN 13: 9783838341224
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optical flow is possibly the most used method for motion segmentation. However its application is often restricted to off-line processing as it requires extensive computational resources and time. In this work, we explore an optical flow method derived from research on the vision system of diptereous insects. The proposed method, Biological Optical Flow (BioOF) was implemented using a series of filters, and therefore is much faster than any existing machine-coded optical flow algorithm. Like other optical flow methods, the output of the BioOF has two components: horizontal and vertical optical flows -- both of them are combined in order to get a better final result in terms of motion segmentation. The result is a framework that can extract an excellent contour of the moving objects segmented out from the images. Finally the object contour is projected onto a Fourier feature space, leading to a representation of the object that is rotational and translational invariant. Over the Fourier feature space, various classification algorithms are investigated for object recognition. Seller Inventory # 9783838341224
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A Biologically Inspired Optical Flow System
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LAP LAMBERT Academic Publishing Jan 2010, 2010
ISBN 10: 3838341228
ISBN 13: 9783838341224
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Optical flow is possibly the most used method for motion segmentation. However its application is often restricted to off-line processing as it requires extensive computational resources and time. In this work, we explore an optical flow method derived from research on the vision system of diptereous insects. The proposed method, Biological Optical Flow (BioOF) was implemented using a series of filters, and therefore is much faster than any existing machine-coded optical flow algorithm. Like other optical flow methods, the output of the BioOF has two components: horizontal and vertical optical flows -- both of them are combined in order to get a better final result in terms of motion segmentation. The result is a framework that can extract an excellent contour of the moving objects segmented out from the images. Finally the object contour is projected onto a Fourier feature space, leading to a representation of the object that is rotational and translational invariant. Over the Fourier feature space, various classification algorithms are investigated for object recognition.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. Seller Inventory # 9783838341224
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A Biologically Inspired Optical Flow System: An Application for Motion Segmentation and Object Identification
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