Visual attention is the biological mechanism allowing to turn mere sensing into conscious perception. In this process, object-based modulation of attention provides a further layer between low-level space/feature-based region selection and full object recognition. In this context, motion is a very powerful feature, naturally attracting our gaze and yielding rapid and effective shape distinction. Moving from a pixel-based account of attention to the definition of proto-objects as perceptual units labelled with a single saliency value, we present a framework for the selection of moving objects within cluttered scenes. Through segmentation of motion energy features, the system extracts coherently moving proto-objects defining them as consistently moving blobs and produces an object saliency map, by evaluating bottom-up distinctiveness of each object candidate with respect to its surroundings, in a center-surround fashion.
@InProceedings{belardinelli:DagSemProc.10081.3, author = {Belardinelli, Anna}, title = {{Attending to Motion: an object-based approach}}, booktitle = {Cognitive Robotics}, pages = {1--11}, series = {Dagstuhl Seminar Proceedings (DagSemProc)}, ISSN = {1862-4405}, year = {2010}, volume = {10081}, editor = {Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10081.3}, URN = {urn:nbn:de:0030-drops-26285}, doi = {10.4230/DagSemProc.10081.3}, annote = {Keywords: Visual attention model, motion selection, saliency map} }
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