Machine Learning in Computer Vision (Computational Imaging and Vision, #29)

by IRA Cohen, Ashutosh Garg, Thomas S. Huang, and Nicu Sebe

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Book cover for Machine Learning in Computer Vision

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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.
  • ISBN10 6610283591
  • ISBN13 9786610283590
  • Publish Date 1 January 2005
  • Publish Status Active
  • Out of Print 9 February 2012
  • Publish Country US
  • Imprint Springer
  • Format eBook
  • Pages 252
  • Language English