Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

Shape Detection using Geometrical Features


Affiliations
1 Dept. of Computer Science & Engineering and I.T, 1Assam Don Bosco University, Guwahati, Assam, India
2 National Institute of Electronics and Information Technology (NIELIT), Kolkata, India
 

In this paper, we have presented an approach for object detection system. This approach is used for detect two-dimensional shapes such as lines, rectangle, square, circle, triangle, polygon, star etc. Proposed technique of shape detection is based on the statistical properties of distribution of points on bitmap image and sub-windowimage of a shape. For recognition, we have considered sub-windowbased features and Nearest Neighbours classifier. By applying these features, we achieve maximum recognition accuracy of 96.7% using 4556 samples.

Keywords

Shape Detection, Feature Extraction, Geometric Features.
User
Notifications
Font Size

  • D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes”, Pattern Recognition, vol. 13, Issue 2, pp. 111-122, 1981.

  • S. Ullman, “Visual Routines”, cognition, 18, pp. 97-156, 1984.

  • L. Itti, E. Niebur and C. Koch, “A model of saliency-based visual attention for rapid scene analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, Issue 11, pp. 1254-1259, 1998.

  • S. Minut and S. Mahadevan, “A reinforcement learning model of selective visual attention”, Proceedings of fifth international conference on Autonomous Agents, ACM, pp. 457-464, 2001.

  • D. G. Lowe, “Distinctive image features from scale invariant key points”, International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.

  • G. Mori, S. Belongie and J. Malik, “Efficient shape matching using shape contexts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, Issue 11, pp. 1832-1837, 2005.

  • E. Nadernejad, S. Sharifzadeh and H. Hassanpour, “Edge Detection Techniques: Evaluation and Comparisons”, in Applied Mathematics Sciences, vol. 2, Issue 31, pp. 1507 – 1520, 2008.

  • A. Toshev, B. Taskar and K. Daniilidis, “Object detection via boundary structure segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 950-957, 2010.

  • S. Tripathi, K. Kumar, B. K. Singh and R. P. Singh, “Image segmentation: A Review”, International Journal of Computer Science and Management Research, vol. 1, Issue 4, 2012.

  • M. Kumar, M. K. Jindal and R. K. Sharma, “Classification of characters and grading writers in offline handwritten Gurumukhi script”, International Conference on Image Information Processing, pp. 1-4, 2011.

  • M. Kumar, R. K. Sharma and M. K. Jindal, “Segmentation of Lines and Words in Handwritten Gurmukhi Script Documents”, International Conference on on Intelligent Interactive Technologies and Multimedia, pp. 25-28, 2010.

  • M. Kumar, M. K. Jindal and R. K. Sharma, “k-nearest neighbours based offline handwritten Gurmukhi character recognition”, International Conference on Image Information Processing, pp. 1-4, 2011.

  • Biederman, “Recognition-by-components: A theory of human image understanding”, Psychological Review, vol. 94, Issue 2, pp. 115-147, 1987.

  • A. Elnagara, R. Alhajib, “Segmentation of connected handwritten numeral strings”, Pattern Recognition, vol. 36, Issue 3, pp. 625-634, 2003.

  • M. Hanmandlu, J. Grover, VK. Madasu and S. Vasikarla, “Inputfuzzy for the recognition of handwritten Hindi numeral”, Proceedings of Information Technology (ITNG’07), pp. 208-213, 2007.

  • S. V. Rajashekararadhva and S. V. Ranjan, “Zone based feature extraction algorithm for handwritten numeral recognition of Kannada Script”, Proceedings of IACC, pp. 525-528, 2009.

  • S. Palmer, “Vision science: Photons to phenomenology”, 1999.

  • M. Peterson and B. Gibson, “Must Figured-Ground Organization Precede Object Recognition? An Assumption in Peril”, Psychological Science, vol. 5, Issue 5, pp. 253-259, 1994.

  • SX. Yu and J. Shi, “Object–specific figure-ground segregation”, Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 39-45, 2003.

  • V. Ferrari, F. Jurie and C. Schmid, “From Images to Shape Models for Object Detection”, International Journal of Computer Vision, vol. 87, pp. 284-303, 2010.

  • M. Leordeanu, M. Hebert, and R. Sukthankar, “Beyond local appearance: Category recognition from pairwise interactions of simple features”, Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2007.

  • S. Belongie and J. Malik, “Shape matching and object recognition using shape contexts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, Issue 4, pp. 509-522, 2002.


Abstract Views: 211

PDF Views: 0




  • Shape Detection using Geometrical Features

Abstract Views: 211  |  PDF Views: 0

Authors

Shalu Gupta
Dept. of Computer Science & Engineering and I.T, 1Assam Don Bosco University, Guwahati, Assam, India
Y. Jayanta Singh
National Institute of Electronics and Information Technology (NIELIT), Kolkata, India

Abstract


In this paper, we have presented an approach for object detection system. This approach is used for detect two-dimensional shapes such as lines, rectangle, square, circle, triangle, polygon, star etc. Proposed technique of shape detection is based on the statistical properties of distribution of points on bitmap image and sub-windowimage of a shape. For recognition, we have considered sub-windowbased features and Nearest Neighbours classifier. By applying these features, we achieve maximum recognition accuracy of 96.7% using 4556 samples.

Keywords


Shape Detection, Feature Extraction, Geometric Features.

References