Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection

Abstract

Human target detection is known to be dependent on a number of components: one, basic electro-optics including image contrast, the target size, pixel resolution, and contrast sensitivity; two, target shape, image type and features, types of clutter; and three, context and task requirements. Here, we consider a Bayesian approach to investigating how these components contribute to target detection. To this end, we develop and compare three different formulations for contrast: mean contrast, perceptual contrast, and a Bayesian-based histogram contrast statistic. Results on past detection data show how the latter contrast measure correlates well with human performance factoring out all other dimensions. As for clutter, our findings show that with large targets, there are effectively no clutter effects. Furthermore, clutter does not have a major effect on detection when it is not contiguous with the target even when it is smaller. However, except for large targets, when the target is contiguous with the clutter, detection clearly decreases as a function of the similarity of target and clutter features-creating type of "clutter camouflage". This Bayesian formulation uses priors based on the contrast histogram statistics derived from all the images, the image context, and implies that human observers have adapted their criteria to fit with the image set, context, and task.

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

“…Statistical method recognize the target through training the classification function using algorithms such as support vector machine (SVM), Bayesian, decision making, neural network [40], [41], and their combination [40] based on image feature data. Neural network is quite commonly studied mainly since it can generalize the information from training images far less than the possible images needed in other machining learning methods such as SVM, Bayesian, and decision making.…”