Robust fitting of 3D CAD models to video streams

Meilhac, Christophe; Nastar, Chahab

doi:10.1007/3-540-63507-6_258

Cited by 2 publications

(2 citation statements)

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“…Our aim is to retrieve the image features and their geometrical aspects without such constraints. Several ways of image tracking has been proposed by various authors in monocular vision [2,3,4,5,6] and stereo vision [7].Earlier attempts to solve this problem led to hand/arm angle measurements and spatial positions. This is best represented by Glove based devices [8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Real time robust hand gesture recognition and visual servoing

Bhat

2012

2012 Annual IEEE India Conference (INDICON)

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The paper introduces hand gesture recognition and morphological model for visual servoing in real time unconstrained environments for positioning, docking or mobile target pursuing in robotics. Vision based hand tracking is an important problem in human computer interaction, since hand motions and gestures could potentially be used to interact with computers in more natural ways. A simple and fast process for gesture recognition and direction control is implemented. SIFT algorithm is used for hand gesture recognition by generating and comparing the feature vectors. Once the gesture is confirmed for particular system (e.g. motor) selection, further procedure would be to drive the system in required direction. For this purpose a morphological operation is performed and Hough transform is applied on the image of interest for angle measurement. This value is fed to the control system for further servo actions. The entire process is implemented in MATLAB. The paper includes experiments of 7 different hand postures and their results. Experiments show that the result is efficient and can be applied for real time applications.

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Section: Introductionmentioning

confidence: 99%

Real time robust hand gesture recognition and visual servoing

Bhat

2012

2012 Annual IEEE India Conference (INDICON)

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“…When the object's 3D model is known as a prior knowledge, the 3D-2D registration generally is converted into 2D-2D matching problem, which means converting 3D information to 2D information and then performing the matching in 2D image domain. In this kind of methods, the features of 3D model, such as points [3][4][5] and lines [1] [2], are projected onto the image plane and then they are matched with the corresponding image features. When the object's 3D data such as range data and LiDAR data is known as a prior knowledge, the 3D-2D registration generally is converted into 3D-3D matching problem, which means converting 2D information to 3D information and performing the matching in 3D space domain.…”

Section: Introductionmentioning

confidence: 99%