Machine learning in computer vision - Info and Reading Options
By Nicu Sebe
"Machine learning in computer vision" was published by Springer in 2005 - Dordrecht, it has 240 pages and the language of the book is English.
“Machine learning in computer vision” Metadata:
- Title: ➤ Machine learning in computer vision
- Author: Nicu Sebe
- Language: English
- Number of Pages: 240
- Publisher: Springer
- Publish Date: 2005
- Publish Location: Dordrecht
“Machine learning in computer vision” Subjects and Themes:
- Subjects: ➤ Computer vision - Machine learning - Computer science - Multimedia systems - Computer Imaging, Vision, Pattern Recognition and Graphics - User Interfaces and Human Computer Interaction - Multimedia Information Systems - Probability and Statistics in Computer Science
Edition Specifications:
- Pagination: xv, 240 p. :
Edition Identifiers:
- The Open Library ID: OL22715376M - OL19493930W
- Online Computer Library Center (OCLC) ID: 60800944
- Library of Congress Control Number (LCCN): 2005283108
- ISBN-10: 1402032749
- All ISBNs: 1402032749
AI-generated Review of “Machine learning in computer vision”:
"Machine learning in computer vision" Description:
The Open Library:
It started withimageprocessing inthesixties. Back then, it took ages to digitize a Landsat image and then process it with a mainframe computer. P- cessing was inspired on theachievements of signal processing and was still very much oriented towards programming. In the seventies, image analysis spun off combining image measurement with statistical pattern recognition. Slowly, computational methods detached themselves from the sensor and the goal to become more generally applicable. In theeighties, model-drivencomputervision originated when arti?cial- telligence and geometric modelling came together with image analysis com- nents. The emphasis was on precise analysiswithlittleorno interaction, still very much an art evaluated by visual appeal. The main bottleneck was in the amount of data using an average of 5 to 50 pictures to illustrate the point. At the beginning of the nineties, vision became available to many with the advent of suf?ciently fast PCs. The Internet revealed the interest of the g- eral public im images, eventually introducingcontent-basedimageretrieval. Combining independent (informal) archives, as the web is, urges for inter- tive evaluation of approximate results andhence weak algorithms and their combination in weak classi?ers.
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