OBJECT RECOGNITION BASED ON EMPIRICAL WAVELET TRANSFORM

Authors

  • Murugan S
  • Anjali Bhardwaj
  • Ganeshbabu T R

DOI:

https://doi.org/10.20894/IJMSR.117.007.001.008

Keywords:

Object recognition, Empirical wavelet transform, Energy Features, KNN classifier.

Abstract

Object recognition is the method of finding an object in an image. We recognize objects without any effort easily. It is a challenging task for computer vision systems due to the size, shape, and structure of objects in an image. In this paper, an efficient object recognition system is presented based on Empirical Wavelet Transform (EWT). The energy features obtained form the EWT decomposed image is used as features for the given object. As EWT, a multiresolution analysis, the given image is decomposed at various level of decomposition and the obtained features are analyzed at each level of decomposition. The evaluation of the system is carried on Columbia Object Image Library Dataset (COIL) which consists of 100 objects captured at different orientations. The classification is done with K- nearest neighbor (KNN) which gives 98.42% accuracy.

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Author Biographies

Murugan S

Research scholar, Department of ECE, Maharishi University of Information Technology, Lucknow, India

Anjali Bhardwaj

Associate Professor, Department of ECE, Maharishi University of Information Technology, Lucknow, India

Ganeshbabu T R

Professor, Department of ECE, Muthayammal Engineering College, Rasipuram, India

References

1. Liu, Y. H., Lee, A. J and Chang, F., "Object Recognition using Discriminative Parts", Computer Vision and Image Understanding, pp. 854-867, 2012.

2. Zhu, J., Yu, J., Wang, C & Li, F.Z., “Colour Combination Attention for Object Recognition”, IET Image Processing, vol. 8, no. 9, pp. 539-547, 2014.

3. Liu, Huaping, and Fuchun Sun, "Online Kernel Dictionary Learning for Object Recognition", IEEE International Conference on Automation Science and Engineering, pp. 268-273, 2016.

4. Guerrero-Peña, F. A., and Vasconcelos, G. C., "Search-Space Sorting with Hidden Markov Models for Occluded Object Recognition", IEEE 8th International Conference on Intelligent Systems, pp. 47- 52, 2016.

5. Jiang, Ping, et al., "Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking", IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 3032-3046, 2016.

6. Han, Xian-Hua, Yen-Wei Chen, and Xiang Ruan, "Multilinear Supervised Neighborhood Embedding of a Local Descriptor Tensor for Scene/Object Recognition", IEEE Transactions on Image Processing, vol. 21, no. 3, pp. 1314-1326, 2012.

7. Poonam and Sharma, M., "A Corner Feature Adaptive Neural Network Model for Partial Object Recognition", 4th International Conference on Reliability, Infocom Technologies and Optimization, pp. 1-6, 2015.

8. Qingnian, Cao, and Jiang Yuanyuan., "Action Recognition Based on Local Spatio- Temporal Oriented Energy Features and Additive Kernel SVM", IEEE Fifth International Conference on Intelligent Systems Design and Engineering Applications, pp. 118-122, 2014.

9. Sahu, Uma, et al., "LIBO: The Grasping Robot Using Object Recognition", International Conference on Electrical, Electronics, and Optimization Techniques, pp.1-6, 2016.

10. Kim, Kyekyung, et al., "Object Recognition for Cell Manufacturing System", IEEE 9th International Conference on Ubiquitous and Ambient Intelligence, pp. 512-514, 2012.

11. Gilles, J., “Empirical Wavelet Transform”, IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010, 2013.

12. Gilles, J., Tran, G., and Osher, S., “2D Empirical Transforms: Wavelets, Ridgelets, and Curvelets Revisited”, SIAM Journal on Imaging Sciences, vol. 7, no. 1, pp. 157–186, 2014.

13. Maheshwari, Shishir, Ram Bilas Pachori, and Rajendra Acharya, U., "Automated Diagnosis of Glaucoma using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images", IEEE Journal of Biomedical and Health Informatics, vol. 99, pp. 2168-2194, 2016.

14. COIL Database: http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.

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Published

2015-12-16

Issue

Vol. 7 No. 1 (2015)

Section

Articles

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