Research article summary (published 30 Aug 2006):

Using hidden scale for salient object detection.

Full Abstract

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.

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Author information

Author/s: Chalmond, Bernard (B); Francesconi, Benjamin (B); Herbin, Stéphane (S);

Affiliation: Centre de Mathématiques et de Leurs Applications, CNRS (UMR 8536), Ecole Normale Supérieure de Cachan, 94235 Cachan, France. bernard.chalmond(-atsign-)cmla.ens-cachan.fr

Journal and publication information

Publication Type: Comparative Study; Journal Article

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process), published in United States. (Language: eng)

Reference: 2006-Sep; vol 15 (issue 9) : pp 2644-56

Dates: Created 2006/09/04; Completed 2006/09/26; Revised 2006/11/15;

PMID: 16948309, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Artificial Intelligence Computer Simulation Image Enhancement - methods

Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Models, Statistical Pattern Recognition, Automated - methods

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