Implementing a Visual Servoing System for Robot Controlling
Nowadays, with the emerging of the new applications
like robot control in image processing, artificial vision for visual
servoing is a rapidly growing discipline and Human-machine
interaction plays a significant role for controlling the robot. This
paper presents a new algorithm based on spatio-temporal volumes for
visual servoing aims to control robots. In this algorithm, after
applying necessary pre-processing on video frames, a spatio-temporal
volume is constructed for each gesture and feature vector is extracted.
These volumes are then analyzed for matching in two consecutive
stages. For hand gesture recognition and classification we tested
different classifiers including k-Nearest neighbor, learning vector
quantization and back propagation neural networks. We tested the
proposed algorithm with the collected data set and results showed the
correct gesture recognition rate of 99.58 percent. We also tested the
algorithm with noisy images and algorithm showed the correct
recognition rate of 97.92 percent in noisy images.
[1] Pavel. A. and Buiu. C.: Development of an embedded artificial vision
system for an autonomous robot, International Journal of Innovative
Computing, Information and Control(ICIC 2011), Vol. 7, Num. 2, pp.
745-762, 2011.
[2] Ospina. E., Valencia. J. and Madrigal. C.: Traffic flow control using
artificial vision techniques, 2011 6-th Colombian Computing Congress
(CCC) available on IEEE explore, 2011.
[3] Cindy. X. You and Mark. A. Tarbell.: Microcomputer-based artificial
vision support system for real-time image processing for camera-driven
visual prostheses, Journal of Biomedical Optics, Vol. 15, Issue 1,
Research Papers: Imaging, 2010.
[4] Harding. P.R.G. and Ellis. T.: Recognition Hand Gesture Using Fourier
Descriptors, Proc. of IEEE Conf. on Pattern Recognition(ICPR2004),
vol. 3, pp. 286-289, 2004.
[5] Dionisio. C.R.P., Cesar. R.M. and Jr.: A Project for Hand Gesture
Recognition", Proc. of IEEE Symposium on Computer Graphics and
Image Processing, pp. 345, 2000.
[6] Lamar. M.V., Bhuiyan M.S. and Iwata. A.: Hand Gesture Recognition
Analysis and an Iimproved CombNET-II, Proc. Of IEEE Conf. on Man
and Cybernetics, vol. 4, 1999.
[7] Moghaddam. B. and pentland. A.: Probabilistic Visual Learning for
Object Detection, Conf. on Computer Vision, Cambridge, MA, 1995.
[8] Moghaddam. B.: Principal Manifolds and Bayesian Subspaces for Visual
Recognition, Proc. Of IEEE Conf. on Computer Vision, ICCV99, 1999.
[9] Freeman. W.T. and Roth. M.: Orientation Histogram for Hand Gesture
Recognition, IEEE Int. Workshop on Automatic face and gesture
recognition, 1995.
[10] Shan. C., Yucheng. W., Xianchao. Q. and Tieniu. T.: Gesture
Recognition Using Temporal Template Based Trajectories", Int. Conf.
on Pattern Recognition, vol. 3, pp. 954 - 957, 2004.
[11] Kumar. S., Kumar. D.K., Sharma. A. and McLachlan. N.: Classification
of Hand Movements Using Motion Templates and Geometrical based
Moments, vol. 3, pp. 299 - 304, 2004.
[12] Vafadar. M. and Behrad. A.: Human Hand Gesture Recognition Using
Motion Orientation Histogram for Interaction of Handicapped Persons
with Computer, Proc. Of Int. Conf. on Image and Signal Processing,
ICISP2008, 2008.
[13] Collins. T.: Analysing Video Sequences using the Spatio-temporal
Volume, Informatic Research Review, 2004.
[14] Konrad. J. and Ristivojevic. M,: Joint Space-time image sequence
segmentation:Object Tunnels and Occlusion Volumes, Proc. Of Int.
Conf. on Acoustic, Speech and Signal Processing, 2004.
[15] Swaminathan. R., Kang. S.B. and Criminisi. A. and Szeliski. R.: On the
Motion and Appearance of Specularities in Image Sequences, Proc. Of
European Conf. in Computer Vision, 2002.
[16] Bolduc. M. M. and Deschenes. F.: Collision and Event Detection using
Geometric Features in Spatio-temporal Volumes, Proc. Of IEEE
Canadian Conf. on Computer and Robot Vision, CRV2005, 2005.
[17] Bloom. J. A. and Reed. T. R.: On the Compression of Video using the
Derivative of Gaussian Transform, Proc. Conf. on Signals, Systems and
Computers, 1998.
[18] Ohara. Y., Sagawa. R., Echigo. T. and Yagi. Y.: Gait Volume: Spatiotemporal
Analysis of Walking, European Conference on Computer
Vision, ECCV2004, 2004.
[19] Nianjun Liu Lovell. B. C. and Kootsookos. P. J.: Evaluation of HMM
Training Algorithms for Letter Hand Gesture Recognition, IEEE Int.
Sym. On Signal Processing and Information Technology, ISSPIT2003,
2003.
[20] Chang. M.C., Matshoba. L. and Preston. S., "A Gesture Driven 3D
interface", Technical Report CS05-15-00, University of Cape Town,
2005.
[21] Yoon. H.S., Min. B.W., Soh. J., Bae. Y. and Yang. H.S., "Human
Computer Interface for Gesture-based Editing System", IEEE Int. Conf.
on Image Analysis and Processing, 1999.
[22] Liu. N., Lovell. B. C., Kootsookos. P. J., "Evaluation of HMM Training
Algorithms for Letter Hand Gesture Recognition", IEEE Int. Sym. on
Signal Processing and Information Technology, vol. 14-17, pp. 648 -
651, 2003.
[23] Shalbaf. R., vafadoost. M. and Shalbaf. A.: Lipreading Using Image
Processing for Helping Handicap", Iranian conf. on Biomedical
Engineering, 2007.
[1] Pavel. A. and Buiu. C.: Development of an embedded artificial vision
system for an autonomous robot, International Journal of Innovative
Computing, Information and Control(ICIC 2011), Vol. 7, Num. 2, pp.
745-762, 2011.
[2] Ospina. E., Valencia. J. and Madrigal. C.: Traffic flow control using
artificial vision techniques, 2011 6-th Colombian Computing Congress
(CCC) available on IEEE explore, 2011.
[3] Cindy. X. You and Mark. A. Tarbell.: Microcomputer-based artificial
vision support system for real-time image processing for camera-driven
visual prostheses, Journal of Biomedical Optics, Vol. 15, Issue 1,
Research Papers: Imaging, 2010.
[4] Harding. P.R.G. and Ellis. T.: Recognition Hand Gesture Using Fourier
Descriptors, Proc. of IEEE Conf. on Pattern Recognition(ICPR2004),
vol. 3, pp. 286-289, 2004.
[5] Dionisio. C.R.P., Cesar. R.M. and Jr.: A Project for Hand Gesture
Recognition", Proc. of IEEE Symposium on Computer Graphics and
Image Processing, pp. 345, 2000.
[6] Lamar. M.V., Bhuiyan M.S. and Iwata. A.: Hand Gesture Recognition
Analysis and an Iimproved CombNET-II, Proc. Of IEEE Conf. on Man
and Cybernetics, vol. 4, 1999.
[7] Moghaddam. B. and pentland. A.: Probabilistic Visual Learning for
Object Detection, Conf. on Computer Vision, Cambridge, MA, 1995.
[8] Moghaddam. B.: Principal Manifolds and Bayesian Subspaces for Visual
Recognition, Proc. Of IEEE Conf. on Computer Vision, ICCV99, 1999.
[9] Freeman. W.T. and Roth. M.: Orientation Histogram for Hand Gesture
Recognition, IEEE Int. Workshop on Automatic face and gesture
recognition, 1995.
[10] Shan. C., Yucheng. W., Xianchao. Q. and Tieniu. T.: Gesture
Recognition Using Temporal Template Based Trajectories", Int. Conf.
on Pattern Recognition, vol. 3, pp. 954 - 957, 2004.
[11] Kumar. S., Kumar. D.K., Sharma. A. and McLachlan. N.: Classification
of Hand Movements Using Motion Templates and Geometrical based
Moments, vol. 3, pp. 299 - 304, 2004.
[12] Vafadar. M. and Behrad. A.: Human Hand Gesture Recognition Using
Motion Orientation Histogram for Interaction of Handicapped Persons
with Computer, Proc. Of Int. Conf. on Image and Signal Processing,
ICISP2008, 2008.
[13] Collins. T.: Analysing Video Sequences using the Spatio-temporal
Volume, Informatic Research Review, 2004.
[14] Konrad. J. and Ristivojevic. M,: Joint Space-time image sequence
segmentation:Object Tunnels and Occlusion Volumes, Proc. Of Int.
Conf. on Acoustic, Speech and Signal Processing, 2004.
[15] Swaminathan. R., Kang. S.B. and Criminisi. A. and Szeliski. R.: On the
Motion and Appearance of Specularities in Image Sequences, Proc. Of
European Conf. in Computer Vision, 2002.
[16] Bolduc. M. M. and Deschenes. F.: Collision and Event Detection using
Geometric Features in Spatio-temporal Volumes, Proc. Of IEEE
Canadian Conf. on Computer and Robot Vision, CRV2005, 2005.
[17] Bloom. J. A. and Reed. T. R.: On the Compression of Video using the
Derivative of Gaussian Transform, Proc. Conf. on Signals, Systems and
Computers, 1998.
[18] Ohara. Y., Sagawa. R., Echigo. T. and Yagi. Y.: Gait Volume: Spatiotemporal
Analysis of Walking, European Conference on Computer
Vision, ECCV2004, 2004.
[19] Nianjun Liu Lovell. B. C. and Kootsookos. P. J.: Evaluation of HMM
Training Algorithms for Letter Hand Gesture Recognition, IEEE Int.
Sym. On Signal Processing and Information Technology, ISSPIT2003,
2003.
[20] Chang. M.C., Matshoba. L. and Preston. S., "A Gesture Driven 3D
interface", Technical Report CS05-15-00, University of Cape Town,
2005.
[21] Yoon. H.S., Min. B.W., Soh. J., Bae. Y. and Yang. H.S., "Human
Computer Interface for Gesture-based Editing System", IEEE Int. Conf.
on Image Analysis and Processing, 1999.
[22] Liu. N., Lovell. B. C., Kootsookos. P. J., "Evaluation of HMM Training
Algorithms for Letter Hand Gesture Recognition", IEEE Int. Sym. on
Signal Processing and Information Technology, vol. 14-17, pp. 648 -
651, 2003.
[23] Shalbaf. R., vafadoost. M. and Shalbaf. A.: Lipreading Using Image
Processing for Helping Handicap", Iranian conf. on Biomedical
Engineering, 2007.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54702", author = "Maryam Vafadar and Alireza Behrad and Saeed Akbari", title = "Implementing a Visual Servoing System for Robot Controlling", abstract = "Nowadays, with the emerging of the new applications
like robot control in image processing, artificial vision for visual
servoing is a rapidly growing discipline and Human-machine
interaction plays a significant role for controlling the robot. This
paper presents a new algorithm based on spatio-temporal volumes for
visual servoing aims to control robots. In this algorithm, after
applying necessary pre-processing on video frames, a spatio-temporal
volume is constructed for each gesture and feature vector is extracted.
These volumes are then analyzed for matching in two consecutive
stages. For hand gesture recognition and classification we tested
different classifiers including k-Nearest neighbor, learning vector
quantization and back propagation neural networks. We tested the
proposed algorithm with the collected data set and results showed the
correct gesture recognition rate of 99.58 percent. We also tested the
algorithm with noisy images and algorithm showed the correct
recognition rate of 97.92 percent in noisy images.", keywords = "Back propagation neural network, Feature vector,
Hand gesture recognition, k-Nearest Neighbor, Learning vector
quantization neural network, Robot control, Spatio-temporal volume,
Visual servoing", volume = "6", number = "9", pages = "940-7", }