Imaging and machine vision book recommendations: 1/27

In order to provide our readers with as many resources on imaging and machine vision as possible, Andy Wilson, Vision Systems Design Editor in Chief, has compiled a list of educational and informative books on various imaging topics that he personally recommends. Check out this week’s recommendations here.

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In order to provide our readers with as many resources onimaging and machine vision as possible, Andy Wilson, Vision Systems Design Editor in Chief, has compiled a list of educational and informative books on various imaging topics that he personally recommends. Check out this week’s recommendations here:

  • Digital Geometry: Geometric Methods for Digital Picture Analysis by Reinhard Klette and Azriel Rosenfeld: This comprehensive text and reference provides an introduction to the mathematical foundations of digital geometry, some of which date back to ancient times, and also discusses the key processes involved, such as geometric algorithms as well as operations on pictures. 
  • Pattern Recognition Technologies and Applications: Recent Advances by Brijesh Verma and Michael Blumenstein: Pattern Recognition Technologies and Applications: Recent Advances provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification.
  • Handbook of Pattern Recognition and Computer Visionby C. H. Chen, L. F. Pau, and P. S. P. Wang: The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures.
  • Neural Networks for Pattern Recognition by Christopher M. Bishop: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models.
  • Pattern Recognition, Fourth Editionby Sergios Theodoridis and Konstantinos Koutroumbas: A classic -- offering comprehensive and unified coverage with a balance between theory and practice! Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications, computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information.

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