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Detection of Object by Robot using Image Processing

Shilpa Khot

Abstract


In this paper demonstration is carried out on an anthropomorphic assist- stance robot as part of a multi- modal man-machine interaction. In this paper, by detecting hand position the robot recognizes the object and work will be carried out as per the human being. When there are many applications are used for optimized image processing methods for detection of human hand due to the camera which have been used or developed. In this hand can be detected by due to its movement in the image, where remaining image is static. Gives fast tracking. In this also Bird’s eye view the Bird’s eye view is correlated with the blurred copy of scene. So that verification takes place by inverse correlation indicate position of hand in the image. In this rotation steps used are having degrees in between 15 and 30.so that arm of robot will rotate in this degrees and it can detect the object which come under the cameras which are used at the top of robot. Mainly in this hand detection takes place, after that skin color segmentation takes place. position of hand given by stereo based localization and then tracking of object and then object recognition takes place. So once object is selected that object is used for further processing.


Keywords


Gaze Direction, Grasping, Object Recognition, Segmentation

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References


T. Bergener and P. Dahm. A framework for Dynamic man-machine interaction implemented on an autonomous mobile robot. In Proceedings of the IEEE International Symposium on Industrial Electronics, ISIE’97, 1997.

C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and W. von Seelen. Walking Pedestrian Recognition. In ITSC, pages 292–297, Tokyo, Japan, 1999. IEEE.

U. Handmann, T. Kalinke, C. Tzomakas, M. Werner, and W. von Seelen. An Image Processing System for Driver Assistance. Image and Vision Computing (Elsevier), 18(5):367 – 376, 2000.

D.P. Huttenlocher, G.A. Klanderman, and W.J.Rucklidge. Comparing Images Using the Hausdorff Distance. IEEE Trans. on PAMI, PAMI-15- 9:850–863, 1993.

I. Iossifidis and A. Steinhage. Control of an 8 dof manipulator by means of neural fields. In FSR2001, International Conference on Field and Service Robotics, Helsinki, Finland, 2001.

Michael Schwarzinger and Detlev Noll. Object recognition with constrained elastic models. Mathematical and Computer Modelling, pages 163–184, 1995.

Rainer Menzner. A Unified Architecture for Speech- Controlled Robot Behavior Based on Nonlinear Dynamics. ibidem-Verlag, 2001.

Percy Dahm. Beiträge zu Anthropomorphen Robotorarmen (Ph.D. thesis). Ruhr Universität Bochum, Institut für Neuroinformatik, Lehrstuhl für Theoretische Biologie, 1999

Rainer Menzner. A Unified Architecture for Speech- Controlled Robot Behavior Based on Nonlinear Dynamics. ibidem-Verlag, 2001.

C. Papageorgiou, T. Eugeniou, and T. Poggio, “A trainable pedastrian detection system,” in Proc. Intelligent Vehicles, 1998, pp. 241–246.

J. C. Wohler, J. Anlauf, T. Pörtner, and U. Franke, “A time delay neuralnetwork al gorithm for real-time pedestrian recognition,” in Proc Intelligent Vehicles, 1998, pp. 247–252.

A.L. Yuille, D.S. Cohen and P.W. Hallinan, Feature extraction from faces using deformable templates,In Proceedings on the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,pp. 104-109, (1989).


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