Algorithms for defining visual regions-of-interest: comparison with eye fixations

Abstract

ÐMany machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest, ROIs. We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (algorithmically detected ROIs) using different Image Processing Algorithms, IPAs, and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (human identified ROIs). In this paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed.

An Approach to the Parameterization of Structure for Fast Categorization

“…Indeed, our decomposition model can explain all variances discovered in seminal studies of human visual search (Noton, 1971;Treisman and Gormican, 1988). Others have already implemented architectures to mimic such search behavior (Itti et al, 1998;Privitera and Stark, 2000;Rajashekar et al, 2008), however those models merely extract straight contour orientations and can therefore explain only a small number of those findings. In contrast, the presented model explains a much larger number of findings such as the computation of precise contour curvature, contour angle and aperture of an arc ( (Treisman and Gormican, 1988), figure 5, 10 and 11 respectively).…”

Model Performance for Visual Attention in Real 3D Color Scenes

“…This can be partially explained by the anatomical structure of the human retina. Thanks to the availability of sophisticated eye tracking technologies, several recent works have confirmed this link between visual attention and eye movements [1,2,3]. Thus, eye movement recording is a suitable means for studying the temporal and spatial deployment of visual attention in most situations.…”

The Active Sampling of Gaze-Shifts

“…Interestingly enough, one point that is not addressed by most models is the "noisy", idiosyncratic variation of the random exploration exhibited by different observers when viewing the same scene, or even by the same subject along different trials [15]. Such variations speak of the stochastic nature of scanpaths.…”