000			03653nas|a2200433 a 4500
005			20260817161131.0
008			091112s2009 th uu|m rtt 0| a1eng d
035			_a.b12070361
099		9	_aAIT RSPR no.CS-09-04
100	1		_aHussain, Akhtar
245	1	0	_a3D reconstruction using dense stereo correspondence
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2009
300			_a27 p. : _bill.
490	1		_aResearch studies project report ; _vno. CS-09-04
500			_aSubmitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, School of Engineering and Technology
502			_aResearch Studies Project Report (M.Eng.) - Asian Institute of Technology, 2009
520			_aNowadays, robots can perform many tasks. One of their potential tasks is to bin picking and part placement. Now many industries could use robots to perform bin picking, to saves time and money. To perform the difficult task of picking the objects from a bin, first robot needs to recognize particular objects. For recognition of the objects a 3D model beneficial, for that purpose, people use many methods; some people use CAD software to create 3D models, but this is very costly and time consuming. some people are now using computer vision techniques, which are very cheap compared to human labor and more expensive sensors such as lasers. So in this research study I use stereo vision-based system to reconstruct a 3D model of objects. Stereo vision can help the robot to estimate depth of objects. If correct depth information is known then a 3D model of the object can be reconstructed correctly. There are two kinds of stereo vision methods, useful for reconstructing 3D model of the objects. Here I used dense stereo correspondence which is more accurate and detailed than sparse correspondence methods. I use the dynamic programming method in OpenCV to create disparity map and then extract one object and make depth map of that object. From the depth map I generated a 3D point cloud. After that, in the future work I will apply 2D Delaunay triangulation to create a 3D mesh and visualize the object using OpenGL or any other tool. If the reconstructed 3D model is correct, the robot will recognize the object and grasp it correctly.
650		0	_aThree-dimensional imaging
650		0	_aComputer vision
650		0	_aRobot vision
700	1		_aDailey, Mathew N., _eChairperson
700	1		_aAfzulpurkar, Nitin V., _eExamination Committee
700	1		_aSumanta, Guha, _eExamination Committee
710	2		_aHigher Education Commission (HEC), Pakistan-Asian Institute of Technology Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tResearch studies project report ; _vno. CS-09-04
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B01286
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