Fundamentals of computer vision / Wesley E. Snyder, Hairong Qi.
By: Snyder, Wesley E [author.]
.
Contributor(s): Qi, Hairong [author.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2017Description: 1 online resource (xiv, 377 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781316882641 (ebook).Subject(s): Image processing -- Digital techniquesAdditional physical formats: Print version: : No titleDDC classification: 621.367 Online resources: Click here to access online Summary: Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.
Title from publisher's bibliographic system (viewed on 24 Oct 2017).
Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.
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