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research-article

Direction Selective Contour Detection for Salient Objects

Author:

Andrea Manno-KovacsAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 29, Issue 2

Pages 375 - 389

https://doi.org/10.1109/TCSVT.2018.2804438

Published: 01 February 2019 Publication History

Abstract

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods.

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Cited By

Al-Amaren AAhmad MSwamy M(2022)A low-complexity residual deep neural network for image edge detectionApplied Intelligence10.1007/s10489-022-04062-653:9(11282-11299)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-022-04062-6
Chen ZZhou HLai JYang LXie X(2021)Contour-Aware Loss: Boundary-Aware Learning for Salient Object SegmentationIEEE Transactions on Image Processing10.1109/TIP.2020.303753630(431-443)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2020.3037536

Direction Selective Contour Detection for Salient Objects
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning algorithms

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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 29, Issue 2

February 2019

318 pages

ISSN:1051-8215

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Cited By

Al-Amaren AAhmad MSwamy M(2022)A low-complexity residual deep neural network for image edge detectionApplied Intelligence10.1007/s10489-022-04062-653:9(11282-11299)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-022-04062-6
Chen ZZhou HLai JYang LXie X(2021)Contour-Aware Loss: Boundary-Aware Learning for Salient Object SegmentationIEEE Transactions on Image Processing10.1109/TIP.2020.303753630(431-443)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2020.3037536

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