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Book Details

Cover of Computational Models for Predicting Visual Target Distinctness

Computational Models for Predicting Visual Target Distinctness

ISBN 9780819439967
Pages 212
Year 2001
Edition 1
Publisher SPICE PRESS
Rs 5104.00 Rs 4083.20
Description
Measuring target acquisition performance in field situations is usually impractical and often very costly or even dangerous. Thus it is of great benefit to have advance knowledge of human visual target acquisition performance for targets or other relevant objects. However, search performance inherently shows a large variance, and depends strongly on prior knowledge of the perceived scene. Therefore, a typical search experiment requires a large number of observers to obtain statistically reliable data.

Visual target acquisition is a complex process, and many factors involved are not yet fully understood. One thing is evident: the more a target stands out from its background the easier it will be to detect it, and the quicker it will be found. It is therefore likely that visual target distinctness is an important determinant of visual search performance.

Target saliency for humans performing visual search and detection tasks can be estimated by means of the difference between the image from the target- and-background scene and the image from the same background with no target. Thus, relevant computational models of early human vision typically process an input image through various spatial and temporal bandpass filters and analyze first- order statistical properties of the filtered images to compute a target distinctness metric. If they give good predictors of target saliency for humans performing visual search and detection tasks, they may be used to compute visual distinctness of image subregions (target areas) from digital imagery.

About Author(s)
 Jose Antonio Garcia Joaquin Fdez-ValdiviaXose R. Fernandez-VidaRosa Rodriguez-Sanchez
Content
Preface

1 Models of feature perception in distortion measure guidance

2 Computational measures based on space-frequency analysis

3 Defining the notion of visual pattern

4 Information theoretic measures

Epilogue

A Comparison with other saliency models

B Integral opponent-colors features

C Forms of gain and divergence

D Calculating derivatives

References

Index

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