Spelling suggestions: "subject:"attern recognition systems"" "subject:"gattern recognition systems""
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Hand-written Chinese character recognition by first and second order Hidden Markov Models and radical modelingWong, Ho-ting., 黃浩霆. January 2003 (has links)
published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy
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Pattern recognition for automated die bonding曾昭明, Tsang, Chiu-ming. January 1982 (has links)
published_or_final_version / Electrical Engineering / Master / Master of Philosophy
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Matching patterns of line segments by affine-invariant area features陳浩邦, Chan, Hau-bang, Bernard. January 2002 (has links)
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
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Dimensionality reduction in the recognition of patterns for electric power systemsFok, Danny Sik-Kwan. January 1981 (has links)
No description available.
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A method for human identification using static, activity-specific parametersJohnson, Amos Y., Jr. 05 1900 (has links)
No description available.
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Surface extraction from coordinate measurement data to facilitate dimensional inspectionLloyd, Timothy Brian 05 1900 (has links)
No description available.
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Vision-based recognition of actions using contextMoore, Darnell Janssen 05 1900 (has links)
No description available.
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High speed target tracking using Kalman filter and partial window imagingHawkins, Mikhel E. 05 1900 (has links)
No description available.
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An experimental investigation on dynamic vision guided pick-up of moving objectsDowns, James Douglas 08 1900 (has links)
No description available.
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Selection and extraction of local geometric features for two dimensional model-based object recognitionDitzenberger, David A. January 1992 (has links)
A topic of computer vision that has been recently studied by a substantial number of scientists is the recognition of objects in digitized gray scale images. The primary goal of model-based object recognition research is the efficient and precise matching of features extracted from sensory data with the corresponding features in an object model database. A source of difficulty during the feature extraction is the determination and representation of pertinent attributes from the sensory data of the objects in the image. In addition, features which are visible from a single vantage point are not usually adequate for the unique identification of an object and its orientation. This paper will describe a regimen that can be used to address these problems. Image preprocessing such as edge detection, image thinning, thresholding, etc., will first be addressed. This will be followed by an in depth discussion that will center upon the extraction of local geometric feature vectors and the hypothesis-verification model used for two dimensional object recognition. / Department of Computer Science
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