Authors:
Jan Tünnermann
;
Christian Born
and
Bärbel Mertsching
Affiliation:
University of Paderborn, Germany
Keyword(s):
Visual Attention, Saliency, Top-Down Control, Visual Search, Object Detection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early and Biologically-Inspired Vision
;
Image and Video Analysis
;
Visual Attention and Image Saliency
Abstract:
Visual attention can support autonomous robots in visual tasks by assigning resources to relevant portions of
an image. In this biologically inspired concept, conspicuous elements of the image are typically determined
with regard to different features such as color, intensity or orientation. The assessment of human visual attention
suggests that these bottom-up processes are complemented – and in many cases overruled – by top-down
influences that modulate the attentional focus with respect to the current task or a priori knowledge. In artificial
attention, one branch of research investigates visual search for a given object within a scene by the use
of top-down attention. Current models require extensive training for a specific target or are limited to very
simple templates. Here we propose a multi-region template model that can direct the attentional focus with
respect to complex target appearances without any training. The template can be adaptively adjusted to compensate
gradual c
hanges of the object’s appearance. Furthermore, the model is integrated with the framework
of region-based attention and can be combined with bottom-up saliency mechanisms. Our experimental results
show that the proposed method outperforms an approach that uses single-region templates and performs
equally well as state-of-the-art feature fusion approaches that require extensive training.
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