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- W1515001984 abstract "Short-pulse laser radar systems are capable of producing range-resolved, i.e., three-dimensional (3-D), images which can be used for automatic object detection and recognition. The success achieved in these scene understanding tasks relies in part on the development of effective image processing algorithms.In the practical realm, algorithm design is governed by trade-offs among detection or recognition performance and speed, implementation ease and cost, and other factors. This thesis addresses the need for a theoretical approach to algorithm design. A central objective of this thesis is to develop detection and recognition processors which can serve as benchmarks for processors subject to a broad range of design constraints. A Neyman-Pearson criterion for statistical optimality is adopted, leading to statistically optimal likelihood ratio test (LRT) detectors and recognizers. Generalized LRT processors result when unknown parameters are considered. In a departure from previous work, unknown range profiles are considered. Consequently, a general theory for maximum-likelihood (ML) range-profiling is derived, and is implemented using the expectation-maximization (EM) algorithm for the case of planar range-profiling. Performance predictions for the collection of estimation, detection and recognition algorithms derived herein are made via computer simulation. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)" @default.
- W1515001984 created "2016-06-24" @default.
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- W1515001984 date "1992-01-01" @default.
- W1515001984 modified "2023-09-24" @default.
- W1515001984 title "Three-dimensional object recognition using laser radar" @default.
- W1515001984 hasPublicationYear "1992" @default.
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