Revision as of 16:04, 21 January 2021

Abstract

This chapter explores some hypothetical computer vision pipeline designs to understand HW/SW design alternatives and optimizations. Instead of looking at isolated computer vision algorithms, this chapter ties together many concepts into complete vision pipelines. Vision pipelines are sketched out for a few example applications to illustrate the use of different methods. Example applications include object recognition using shape and color for automobiles, face detection and emotion detection using local features, image classification using global features, and augmented reality. The examples have been chosen to illustrate the use of different families of feature description metrics within the Vision Metrics Taxonomy presented in Chap. 5. Alternative optimizations at each stage of the vision pipeline are explored. For example, we consider which vision algorithms run better on a CPU versus a GPU, and discuss how data transfer time between compute units and memory affects performance.

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• http://link.springer.com/content/pdf/10.1007/978-3-319-33762-3_8,

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• https://link.springer.com/chapter/10.1007/978-3-319-33762-3_8,

https://core.ac.uk/display/81551279,

https://rd.springer.com/chapter/10.1007/978-1-4302-5930-5_8,

https://www.scipedia.com/public/Krig_2016a,

https://link.springer.com/chapter/10.1007/978-3-319-33762-3_8/fulltext.html,

https://link.springer.com/chapter/10.1007/978-1-4302-5930-5_8/fulltext.html,

https://academic.microsoft.com/#/detail/73737209

DOIS: 10.1007/978-3-319-33762-3_8 10.1007/978-1-4302-5930-5_8