A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Image Processing: The Fundamentals
Image Processing: The Fundamentals
Subpixel-Precise Extraction of Watersheds
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Scene interpretation by combining probability theory and logic: The tower of knowledge
Computer Vision and Image Understanding
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Cognitive systems are trained to recognise perceptually meaningful parts of an image. These regions contain some variation, i.e. local texture, and are roughly convex. We call such regions "blobs". We define blobs to be components that merit further analysis by a higher level interpretation module as they very likely constitute semantically meaningful units, rather than characteristic features or salient spots. A scheme, independent of scale and colour, is proposed, based on the use of Gaussian kernels and mathematical morphology for the extraction of blobs. For understanding how well the extracted blobs match the meaningful regions, we present an eye-tracking experiment using 20 subjects and 20 different colour images using the hypothesis that the gaze of the viewers are more attracted to the meaningful regions/objects of a scene. We show that the gaze of the subjects is attracted more to the regions which were extracted by our model in comparison with the regions which were extracted by the saliency map model, proposed by Itti et al.