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Proceedings Paper

Imagery test suites and their implication on the testability of computer vision algorithms
Author(s): Andrew C. Segal; Richard Greene; Robert Kero; Daniel Steuer
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Paper Abstract

A fundamental question in the ability to determine the effectiveness of any computer vision algorithm is the construction and application of proper test data suites. The purpose of this paper is to develop an understanding of the underlying requirements necessary in forming test suites, and the limitations that restricted sample sizes have on determining the testability of computer vision algorithms. With the relatively recent emergence of high performance computing, it is now highly desirable to perform statistically significant testing of algorithms using a test suite containing a full range of data, from simple binary images to textured images and multi-scale images. Additionally, a common database of test suites would enable direct comparisons of competing imagery exploitation algorithms. The initial step necessary in building a test suite is the selection of adequate measures necessary to estimate the subjective attributes of images, similar to the quantitative measures from speech quality. We will discuss image measures, their relation to the construction of test suites and the use of real sensor data or computer generated synthetic images. By using the latest technology in computer graphics, synthetically generated images varying in degrees of distortion both from sensors models and other noise source models can be formed if ground-truth information of the images is known. Our eventual goal is to intelligently construct statistically significant test suites which would allow for A/B comparisons between various computer vision algorithms.

Paper Details

Date Published: 1 April 1992
PDF: 5 pages
Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); doi: 10.1117/12.58061
Show Author Affiliations
Andrew C. Segal, Univ. of Illinois/Chicago (United States)
Richard Greene, Argonne National Lab. (United States)
Robert Kero, Argonne National Lab. (United States)
Daniel Steuer, Univ. of Illinois/Chicago (United States)


Published in SPIE Proceedings Vol. 1623:
The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges

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