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Original Essays | June 11, 2013
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Statistical Optimization for Geometric Computation: Theory and Practiceby Kenichi Kanatani
Synopses & ReviewsPublisher Comments:This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sensor data that contain noise--a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. The numerous mathematical prerequisites for developing the theories are explained systematically in separate chapters, and examples drawn from both synthetic and real data demonstrate the improvements in accuracy that result from the use of optimal methods. 1996 ed. Synopsis:This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise — a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition. Synopsis:Appropriate for graduate students, this text explains the foundations of statistical inference for building three-dimensional models with image and sensor data containing noise. What Our Readers Are SayingBe the first to add a comment for a chance to win!Product Details
Related Subjects
Computers and Internet » Artificial Intelligence » Image Processing
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