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{ "item_title" : "Statistical Image Processing Techniques for Noisy Images", "item_author" : [" Phillipe Réfrégier", "François Goudail "], "item_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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/46/134/692/1461346924_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Statistical Image Processing Techniques for Noisy Images|Phillipe Réfrégier

Statistical Image Processing Techniques for Noisy Images : An Application-Oriented Approach

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Overview

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.

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Details

  • ISBN-13: 9781461346920
  • ISBN-10: 1461346924
  • Publisher: Springer
  • Publish Date: November 2013
  • Dimensions: 9.21 x 6.14 x 0.57 inches
  • Shipping Weight: 0.85 pounds
  • Page Count: 254

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