کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970412 | 1450120 | 2017 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Severe-occluded 3D object identification via region-based descriptions
ترجمه فارسی عنوان
شناسایی دقیق ابعاد سه بعدی با استفاده از توصیفهای منطقهای
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
This paper describes a region-based strategy for part-based object identification with independence of the external factors that affect its captured image: light variations, capture point-of-view or occlusions. Starting from color images and depth estimations, i.e. not requiring 3-dimensional models, we focus on the identification of learned objects in severe-occlusion scenarios. To face this problem, we assume that objects have been preliminarily segregated from the scene. Strong changes of appearance-due to one or several of the aforementioned factors or to the object nature, e.g. deformable objects-substantially increase the problem complexity. The proposed algorithm operates by splitting segregated objects in successively coarser region-partitions, with each region representing a part of the object from which it was extracted. For the characterization of these parts, two region-driven descriptors are proposed: R-DAISY and R-SHOT. Their novelty relies on the use of a size-and-shape-variable description support which is automatically defined by the object part itself. Descriptions obtained in this way are self-organized in a single neural structure by an unsupervised learning process. Experimental results are promising in the identification of severe-occluded objects using a small set of training instances-1-to-8 short-varied views per object.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 58, October 2017, Pages 240-257
Journal: Signal Processing: Image Communication - Volume 58, October 2017, Pages 240-257
نویسندگان
Marcos Escudero-Viñolo, Jesus Bescos,