Imagery test suites and their implication on the testability of computer vision algorithms

Andrew C. Segal; Richard Greene; Robert Kero; Daniel Steuer

doi:10.1117/12.58061

1 April 1992 Imagery test suites and their implication on the testability of computer vision algorithms

Andrew C. Segal, Richard Greene, Robert Kero, Daniel Steuer

Proceedings Volume 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges; (1992) https://doi.org/10.1117/12.58061
Event: The 20th AIPR Workshop, 1991, McLean, VA, United States

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.

Citation Download Citation

Andrew C. Segal, Richard Greene, Robert Kero, and Daniel Steuer "Imagery test suites and their implication on the testability of computer vision algorithms", Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); https://doi.org/10.1117/12.58061

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KEYWORDS

Databases

Algorithm development

Computer vision technology

Machine vision

Distortion

Image processing

Sensors

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