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.
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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.
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Paperback. Condición: As New. Reprint. slightly corrected republication of the Elsevier Science Amsterdam 1996 edition 509 pages. ".discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sonsor data that contain noise--a task involving autonomous robots guided by video cameras and sensors." ; 5 3/8 x 8 1/2 ". Nº de ref. del artículo: 52029
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Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9780486443089
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