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We present a novel framework for robustly understanding the geometrical and semantic structure of a cluttered room from a small number of images captured from different viewpoints. The tasks we seek to address include: i) estimating the 3D layout of the room - that is, the 3D configuration of floor, walls and ceiling; ii) identifying and localizing all the foreground objects in the room. We jointly...
Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however,...
To overcome the disadvantages of the traditional subjective weight method-AHP (eigenvalue method), research paper gives a direct, simple and easy to calculation method-relations sequence analysis. The paper will develop sequence analysis relations, puts forward an interval estimation relations sequence analysis, the method is closer to the actual application. Finally, a numerical example will be given...
Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects...
Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints....
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