| Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach 2004 Edition Contributor(s): Réfrégier, Phillipe (Author), Goudail, François (Author) |
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ISBN: 030647865X ISBN-13: 9780306478659 Publisher: Springer OUR PRICE: $94.05 Product Type: Hardcover - Other Formats Published: December 2003 Annotation: Statistical Processing Techniques for Noisy Images presents a statistical framework to design algorithms for target detection, tracking, segmentation and classification (identification). Its main goal is to provide the reader with efficient tools for developing algorithms that solve his/her own image processing applications. In particular, such topics as hypothesis test-based detection, fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated, from theoretical foundations to practical implementations. With a large number of illustrations and practical examples, this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing, as well as a reference for researchers in related fields. |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Imaging Systems - Computers | Optical Data Processing - Computers | Programming - Algorithms |
| Dewey: 621.367 |
| LCCN: 2003058900 |
| Physical Information: 0.53" H x 5.9" W x 9.32" (1.32 lbs) 254 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Statistical Processing Techniques for Noisy Images presents a statistical framework to design algorithms for target detection, tracking, segmentation and classification (identification). Its main goal is to provide the reader with efficient tools for developing algorithms that solve his/her own image processing applications. In particular, such topics as hypothesis test-based detection, fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated, from theoretical foundations to practical implementations. With a large number of illustrations and practical examples, this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing, as well as a reference for researchers in related fields. |