Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach - Phillipe Réfrégier,François Goudail
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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 ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Phillipe Réfrégier, François Goudail |
|---|---|
| Julkaisija | Springer New York |
| Julkaisuvuosi | 2013 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9781461346920 |