- We will send in 10–14 business days.
/images/books/12554578/9781933019130.jpg)
- Author: Axel Pinz
- Publisher: Now Publishers
- ISBN-10: 1933019131
- ISBN-13: 9781933019130
- Format: 15.6 x 23.4 x 0.6 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
Object Categorization presents foundations, original research and trends in the field of object categorization by computer vision methods. It provides a review of existing representations, algorithms, systems and databases for visual object categorization. It goes beyond a pure review of the area though, by including original research in categorization, presenting a prototype system for categorization, discussing the various databases and providing experimental results on object categorization and localization in still images. Object Categorization will serve as a valuable reference for students and graduate students in computer vision, providing a thorough review of the state of the art in visual object categorization. Researchers in computer vision will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research. The book will also be of interest to researchers from related fields who have an interest in visual processing.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.19:08:19
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Axel Pinz
- Publisher: Now Publishers
- ISBN-10: 1933019131
- ISBN-13: 9781933019130
- Format: 15.6 x 23.4 x 0.6 cm, minkšti viršeliai
- Language: English English
Object Categorization presents foundations, original research and trends in the field of object categorization by computer vision methods. It provides a review of existing representations, algorithms, systems and databases for visual object categorization. It goes beyond a pure review of the area though, by including original research in categorization, presenting a prototype system for categorization, discussing the various databases and providing experimental results on object categorization and localization in still images. Object Categorization will serve as a valuable reference for students and graduate students in computer vision, providing a thorough review of the state of the art in visual object categorization. Researchers in computer vision will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research. The book will also be of interest to researchers from related fields who have an interest in visual processing.
Reviews