Shock graph for shape recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Shock graphs have emerged as a powerful generic 2-D shape representation. In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is to use shock graphs as shape representation for recognition where features of objects are detected for recognition and classification. We propose to represent the medial axis characteristic points as an attribute to model the shape. The information about the object shape and its topology is totally embedded in thera and this allows the potential to compare different shapes by their shock graph. The techniques developed are skeletonization, followed by thinning and finally detection of end points and junction points that are present in every region of the shock graph for possible modelling of shape recognition. Experimental results demonstrate the correctness in detecting its characteristic points for recognition. It will be shown that this framework is effective in shape recognition. The algorithms were tested with wide varieties of 2D shapes. Research on this promising approach is ongoing, focusing both on new applications and on techniques that will further enhance its performance in a variety of computer vision and pattern recognition domains.

Original languageEnglish
Title of host publicationSCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region"
Pages103-107
Number of pages5
DOIs
Publication statusPublished - 2006
Event2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006 - Shah Alam
Duration: 27 Jun 200628 Jun 2006

Other

Other2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006
CityShah Alam
Period27/6/0628/6/06

Fingerprint

Computer vision
Pattern recognition
Topology

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Tahir, N. M., Hussain, A., Abdul Samad, S., Husain, H., & Mustafa, M. M. (2006). Shock graph for shape recognition. In SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region" (pp. 103-107). [4339318] https://doi.org/10.1109/SCORED.2006.4339318

Shock graph for shape recognition. / Tahir, Nooritawati Md; Hussain, Aini; Abdul Samad, Salina; Husain, Hafizah; Mustafa, Mohd. Marzuki.

SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. p. 103-107 4339318.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tahir, NM, Hussain, A, Abdul Samad, S, Husain, H & Mustafa, MM 2006, Shock graph for shape recognition. in SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region"., 4339318, pp. 103-107, 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006, Shah Alam, 27/6/06. https://doi.org/10.1109/SCORED.2006.4339318
Tahir NM, Hussain A, Abdul Samad S, Husain H, Mustafa MM. Shock graph for shape recognition. In SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. p. 103-107. 4339318 https://doi.org/10.1109/SCORED.2006.4339318
Tahir, Nooritawati Md ; Hussain, Aini ; Abdul Samad, Salina ; Husain, Hafizah ; Mustafa, Mohd. Marzuki. / Shock graph for shape recognition. SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. pp. 103-107
@inproceedings{ddd7630c41a74e9980036881c1693cdf,
title = "Shock graph for shape recognition",
abstract = "Shock graphs have emerged as a powerful generic 2-D shape representation. In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is to use shock graphs as shape representation for recognition where features of objects are detected for recognition and classification. We propose to represent the medial axis characteristic points as an attribute to model the shape. The information about the object shape and its topology is totally embedded in thera and this allows the potential to compare different shapes by their shock graph. The techniques developed are skeletonization, followed by thinning and finally detection of end points and junction points that are present in every region of the shock graph for possible modelling of shape recognition. Experimental results demonstrate the correctness in detecting its characteristic points for recognition. It will be shown that this framework is effective in shape recognition. The algorithms were tested with wide varieties of 2D shapes. Research on this promising approach is ongoing, focusing both on new applications and on techniques that will further enhance its performance in a variety of computer vision and pattern recognition domains.",
author = "Tahir, {Nooritawati Md} and Aini Hussain and {Abdul Samad}, Salina and Hafizah Husain and Mustafa, {Mohd. Marzuki}",
year = "2006",
doi = "10.1109/SCORED.2006.4339318",
language = "English",
isbn = "1424405270",
pages = "103--107",
booktitle = "SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development {"}Towards Enhancing Research Excellence in the Region{"}",

}

TY - GEN

T1 - Shock graph for shape recognition

AU - Tahir, Nooritawati Md

AU - Hussain, Aini

AU - Abdul Samad, Salina

AU - Husain, Hafizah

AU - Mustafa, Mohd. Marzuki

PY - 2006

Y1 - 2006

N2 - Shock graphs have emerged as a powerful generic 2-D shape representation. In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is to use shock graphs as shape representation for recognition where features of objects are detected for recognition and classification. We propose to represent the medial axis characteristic points as an attribute to model the shape. The information about the object shape and its topology is totally embedded in thera and this allows the potential to compare different shapes by their shock graph. The techniques developed are skeletonization, followed by thinning and finally detection of end points and junction points that are present in every region of the shock graph for possible modelling of shape recognition. Experimental results demonstrate the correctness in detecting its characteristic points for recognition. It will be shown that this framework is effective in shape recognition. The algorithms were tested with wide varieties of 2D shapes. Research on this promising approach is ongoing, focusing both on new applications and on techniques that will further enhance its performance in a variety of computer vision and pattern recognition domains.

AB - Shock graphs have emerged as a powerful generic 2-D shape representation. In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is to use shock graphs as shape representation for recognition where features of objects are detected for recognition and classification. We propose to represent the medial axis characteristic points as an attribute to model the shape. The information about the object shape and its topology is totally embedded in thera and this allows the potential to compare different shapes by their shock graph. The techniques developed are skeletonization, followed by thinning and finally detection of end points and junction points that are present in every region of the shock graph for possible modelling of shape recognition. Experimental results demonstrate the correctness in detecting its characteristic points for recognition. It will be shown that this framework is effective in shape recognition. The algorithms were tested with wide varieties of 2D shapes. Research on this promising approach is ongoing, focusing both on new applications and on techniques that will further enhance its performance in a variety of computer vision and pattern recognition domains.

UR - http://www.scopus.com/inward/record.url?scp=46849104487&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46849104487&partnerID=8YFLogxK

U2 - 10.1109/SCORED.2006.4339318

DO - 10.1109/SCORED.2006.4339318

M3 - Conference contribution

AN - SCOPUS:46849104487

SN - 1424405270

SN - 9781424405275

SP - 103

EP - 107

BT - SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region"

ER -