Incremental refinement of image salient-point detection.

Full Abstract

Low-level image analysis systems typically detect "points of interest", i.e., areas of natural images that contain corners or edges. Most of the robust and computationally efficient detectors proposed for this task use the autocorrelation matrix of the localized image derivatives. Although the performance of such detectors and their suitability for particular applications has been studied in relevant literature, their behavior under limited input source (image) precision or limited computational or energy resources is largely unknown. All existing frameworks assume that the input image is readily available for processing and that sufficient computational and energy resources exist for the completion of the result. Nevertheless, recent advances in incremental image sensors or compressed sensing, as well as the demand for low-complexity scene analysis in sensor networks now challenge these assumptions. In this paper, we investigate an approach to compute salient points of images incrementally, i.e., the salient point detector can operate with a coarsely quantized input image representation and successively refine the result (the derived salient points) as the image precision is successively refined by the sensor. This has the advantage that the image sensing and the salient point detection can be terminated at any input image precision (e.g., bound set by the sensory equipment or by computation, or by the salient point accuracy required by the application) and the obtained salient points under this precision are readily available. We focus on the popular detector proposed by Harris and Stephens and demonstrate how such an approach can operate when the image samples are refined in a bitwise manner, i.e., the image bitplanes are received one-by-one from the image sensor. We estimate the required energy for image sensing as well as the computation required for the salient point detection based on stochastic source modeling. The computation and energy required by the proposed incremental refinement approach is compared against the conventional salient-point detector realization that operates directly on each source precision and cannot refine the result. Our experiments demonstrate the feasibility of incremental approaches for salient point detection in various classes of natural images. In addition, a first comparison between the results obtained by the intermediate detectors is presented and a novel application for adaptive low-energy image sensing based on points of saliency is presented.

Learn Faster Today Improve your study skills

Author information

Author/s: Andreopoulos, Yiannis (Y); Patras, Ioannis (I);

Affiliation: University College London, Department of Electronic and Electrical Engineering, London, UK. iandreop(-atsign-)ee.ucl.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

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: 2008-Sep; vol 17 (issue 9) : pp 1685-99

Dates: Created 2008/08/20; Completed 2008/09/23;

PMID: 18713674, status: MEDLINE (last retrieval date: 11/6/2008)

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

External Links for this article (including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

Image Interpretation, Computer-Assisted - methods
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity

These are the highest related articles currently in the database:

The long-range saliency of edge- and corner-based salient points. 30 Oct 2005
Boosting color saliency in image feature detection. 30 Dec 2005
Using hidden scale for salient object detection. 30 Aug 2006
Detection of hepatocellular carcinoma by ferumoxides-enhanced MR imaging in cirrhosis: Incremental value of dynamic gadolinium-enhancement. 30 Dec 2005
Spatiotemporal salient points for visual recognition of human actions. 30 May 2006
Application of the kohonen network for automatic point correspondence in retinal images. 30 Dec 2006
Learning-based deformable registration of MR brain images. 30 Aug 2006
Interesting objects are visually salient. 5 Mar 2008
Salient closed boundary extraction with ratio contour. 30 Mar 2005
Utilization of evidence theory in the detection of salient regions in successive CT images. 30 Dec 2005

See 100+ related articles.