@article{audet_salient_2017,

title = {Salient object detection in images by combining objectness clues in the RGBD space},

author = {F. Audet and M. S. Allili and A. -M. Cretu},

editor = {Campilho A. Karray F. Cheriet F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},

doi = {10.1007/978-3-319-59876-5_28},

issn = {03029743},

year  = {2017},

date = {2017-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {10317 LNCS},

pages = {247–255},

abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},

note = {ISBN: 9783319598758 

Publisher: Springer Verlag},

keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},

pubstate = {published},

tppubtype = {article}

}

@article{filali_multi-graph_2016,

title = {Multi-graph based salient object detection},

author = {I. Filali and M. S. Allili and N. Benblidia},

editor = {Karray F. Campilho A. Campilho A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},

doi = {10.1007/978-3-319-41501-7_36},

issn = {03029743},

year  = {2016},

date = {2016-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9730},

pages = {318–324},

abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},

note = {ISBN: 9783319415000 

Publisher: Springer Verlag},

keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},

pubstate = {published},

tppubtype = {article}

}