Using models of feature perception in distortion measure guidance

In this paper we present three error measures based on feature perception models, in which pixel errors are computed on locations at which humans might perceive features in the reference image. In the first part of this work, the three schemes of feature detection will be discussed and evaluated in terms of their performance for a simple visual signal-processing task. The first model is based on the use of local intensity gradients, the second based on the use of phase congruency in an image, and the third based on the use of local energy maxima for a few active sensors under a multichannel organization of the reference picture. In the second part of this paper, examples are provided of object detection and recognition applications that illustrate the ability of the induced error measures to predict the detectability of objects in natural backgrounds as well as their perceptual capabilities. (C) 1998 Elsevier Science B.V.