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Title: Multi-scale saliency search in image analysis.

Abstract

Saliency detection in images is an important outstanding problem both in machine vision design and the understanding of human vision mechanisms. Recently, seminal work by Itti and Koch resulted in an effective saliency-detection algorithm. We reproduce the original algorithm in a software application Vision and explore its limitations. We propose extensions to the algorithm that promise to improve performance in the case of difficult-to-detect objects.

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
875629
Report Number(s):
SAND2005-6057
TRN: US200603%%274
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; DESIGN; DETECTION; PERFORMANCE; VISION; IMAGES; DATA ANALYSIS; V CODES; Digital techniques.; Image analysis.

Citation Formats

Slepoy, Alexander, Campisi, Anthony, and Backer, Alejandro. Multi-scale saliency search in image analysis.. United States: N. p., 2005. Web. doi:10.2172/875629.
Slepoy, Alexander, Campisi, Anthony, & Backer, Alejandro. Multi-scale saliency search in image analysis.. United States. doi:10.2172/875629.
Slepoy, Alexander, Campisi, Anthony, and Backer, Alejandro. Sat . "Multi-scale saliency search in image analysis.". United States. doi:10.2172/875629. https://www.osti.gov/servlets/purl/875629.
@article{osti_875629,
title = {Multi-scale saliency search in image analysis.},
author = {Slepoy, Alexander and Campisi, Anthony and Backer, Alejandro},
abstractNote = {Saliency detection in images is an important outstanding problem both in machine vision design and the understanding of human vision mechanisms. Recently, seminal work by Itti and Koch resulted in an effective saliency-detection algorithm. We reproduce the original algorithm in a software application Vision and explore its limitations. We propose extensions to the algorithm that promise to improve performance in the case of difficult-to-detect objects.},
doi = {10.2172/875629},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Oct 01 00:00:00 EDT 2005},
month = {Sat Oct 01 00:00:00 EDT 2005}
}

Technical Report:

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