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Performance characterization in computer vision: A guide to best practices

Thacker, Neil A and Clark, Adrian F and Barron, John L and Ross Beveridge, J and Courtney, Patrick and Crum, William R and Ramesh, Visvanathan and Clark, Christine (2008) 'Performance characterization in computer vision: A guide to best practices.' Computer Vision and Image Understanding, 109 (3). pp. 305-334. ISSN 1077-3142

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It is frequently remarked that designers of computer vision algorithms and systems cannot reliably predict how algorithms will respond to new problems. A variety of reasons have been given for this situation and a variety of remedies prescribed in literature. Most of these involve, in some way, paying greater attention to the domain of the problem and to performing detailed empirical analysis. The goal of this paper is to review what we see as current best practices in these areas and also suggest refinements that may benefit the field of computer vision. A distinction is made between the historical emphasis on algorithmic novelty and the increasing importance of validation on particular data sets and problems. © 2007 Elsevier Inc. All rights reserved.

Item Type: Article
Uncontrolled Keywords: performance assessment; performance evaluation; vision system design
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 06 Mar 2012 12:30
Last Modified: 23 Sep 2022 18:27

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