Literature DB >> 22383341

Active visual segmentation.

Ajay K Mishra1, Yiannis Aloimonos, Loong-Fah Cheong, Ashraf A Kassim.   

Abstract

Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the "optimal" closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the "optimal" closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

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Year:  2012        PMID: 22383341     DOI: 10.1109/TPAMI.2011.171

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  5 in total

1.  Gaze-enabled Egocentric Video Summarization via Constrained Submodular Maximization.

Authors:  Jia Xut; Lopamudra Mukherjee; Yin Li; Jamieson Warner; James M Rehg; Vikas Singht
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2015-06

Review 2.  Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Authors:  Thi-Hoa-Cuc Nguyen; Jean-Christophe Nebel; Francisco Florez-Revuelta
Journal:  Sensors (Basel)       Date:  2016-01-07       Impact factor: 3.576

3.  Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera.

Authors:  Jiatong Bao; Yunyi Jia; Yu Cheng; Hongru Tang; Ning Xi
Journal:  Sensors (Basel)       Date:  2016-12-13       Impact factor: 3.576

4.  Combining segmentation and attention: a new foveal attention model.

Authors:  Rebeca Marfil; Antonio J Palomino; Antonio Bandera
Journal:  Front Comput Neurosci       Date:  2014-08-14       Impact factor: 2.380

5.  Biomedical literature classification with a CNNs-based hybrid learning network.

Authors:  Yan Yan; Xu-Cheng Yin; Chun Yang; Sujian Li; Bo-Wen Zhang
Journal:  PLoS One       Date:  2018-07-26       Impact factor: 3.240

  5 in total

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