Refine your search
Collections
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Geetha, A.
- Edge Detection Using Multispectral Thresholding
Abstract Views :282 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, J.K.K. Nataraja College of Arts & Science, IN
2 Department of Computer Science, Vellalar College for Women, IN
1 Department of Computer Science, J.K.K. Nataraja College of Arts & Science, IN
2 Department of Computer Science, Vellalar College for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 4 (2016), Pagination: 1267-1272Abstract
Edge detection is a fundamental tool in image processing and computer vision, particularly in the areas of feature detection and extraction. Among various edge detection methods, Otsu method is one of the best optimal thresholding methods for general real world images with regard to uniformity and shape measures. In this paper, a multispectral thresholding algorithm using Otsu method is proposed to detect the edges in multispectral images. Natural, art and simulated images are considered for testing. Since the edges are well known in the simulated images, they are considered for performance evaluation. The results of proposed method, Edge Detection using MultiSpectral Thresholding (EDMST), are compared against the results of Canny Otsu, Improved Otsu, Median based Otsu and Improved Gray Image Otsu edge detection algorithms based on the human visual system, the number of edges and the number of pixels. The experimental results show that the proposed method achieves better performance and hence applied on Satellite images.Keywords
Edge Detection, Multispectral Thresholding, Otsu Method, Satellite Images, EDMST.
- Face Recognition Based on Local Derivative Tetra Pattern
Abstract Views :437 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Applications, Nesamony Memorial Christian College, IN
2 Department of Computer Science, Sadakathullah Appa College, IN
3 Department of Computer Science, Nesamony Memorial Christian College, IN
1 Department of Computer Applications, Nesamony Memorial Christian College, IN
2 Department of Computer Science, Sadakathullah Appa College, IN
3 Department of Computer Science, Nesamony Memorial Christian College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 3 (2017), Pagination: 1393-1400Abstract
This paper proposes a new face recognition algorithm called local derivative tetra pattern (LDTrP). The new technique LDTrP is used to alleviate the face recognition rate under real-time challenges. Local derivative pattern (LDP) is a directional feature extraction method to encode directional pattern features based on local derivative variations. The nth -order LDP is proposed to encode the first (n-1)th order local derivative direction variations. The LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. The local tetra pattern (LTrP) encodes the relationship between the reference pixel and its neighbours by using the first-order derivatives in vertical and horizontal directions. LTrP extracts values which are based on the distribution of edges which are coded using four directions. The LDTrP combines the higher order directional feature from both LDP and LTrP. Experimental results on ORL and JAFFE database show that the performance of LDTrP is consistently better than LBP, LTP and LDP for face identification under various conditions. The performance of the proposed method is measured in terms of recognition rate.Keywords
Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Local Derivative Pattern (LDP), Local Tetra Pattern (LTrP).References
- T. Ahonen, A. Hadid and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
- M. Turk and A. Pentland, “Eigen Faces for Recognition”, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.
- P. Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs. Fisherfaces: Rcoginition using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.
- Baochang Zhang, Yongsheng Gao, Sanqiang Zhao and Jianzhuang Liu, “Local Derivative Pattern Verses Local Binary Pattern: Face Recognition with High-order Local Pattern Descriptor”, IEEE Transactions on Image Processing, Vol. 19, No.2, pp. 533-544, 2010.
- W. Zhao, R. Chellappa P.J. Phillips and A. Rosenfeld, “Face Recognition: A Literature Survey”, ACM Computing Surveys, Vol. 35, No. 4, pp. 399-459, 2003.
- R.Chellapa, C.L.Wilson and D.JKriegman, “Eigenfaces vs. Fisherfaces A Survey”, Proceedings of IEEE, Vol. 83, No. 5, pp. 705-740, 1995.
- Hongming Zhang, Wen Gao, Xilin Chen and Debin Zhao, “Learning Informative Features for Spatial Histogram based Object Detection”, Proceedings of IEEE International Joint Conference on Neural Networks, Vol. 3, pp. 1806-1811, 2005.
- S. Murala, R.P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-based Image Retrieval System”, IEEE Transactions on Image Processing, Vol. 21, No. 5, pp. 2874-2886, 2012.
- The Database of Faces, Available at, http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces
- Yong Rui, Thomas S. Huang and Shih-Fu Chang, “Image Retrieval: Current Techniques, Promising Directions and Open Issues”, Journal of Visual Communication and Image Representation, Vol. 10, No. 1, pp. 39-62, 1999.
- Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature sets for Face Recognition under Difficult Lighting Conditions”, Proceedings of 3rd International Workshop, Analysis and Modeling of Faces and Gestures, pp. 1635-1650, 2010.
- Timo Ahonen, Abdenour Hadid and Matti Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
- T. Ojala, M. Pietikainen and D. Harwood, “A Comparative Study of Texture Measures with Classification based on Feature Distributions”, Pattern Recognition, Vol. 29, No. 1, pp. 51-59, 1996.
- Wen-Hung Liao and Ting-Jung Young, “Texture Classification using Uniform Extended Local Ternary Patterns”, Proceedings of IEEE International Symposium on Multimedia, pp. 191-195, 2010.
- K. Thangadurai, S. Bhuvana and R. Radhakrishnan, “An Improved Local Tetra Pattern for Content based Image Retrieval”, Journal of Global Research in Computer Science, Vol. 4, No. 4, pp. 37-42,2013.
- JAFFE images, Available at: http://www.kasrl.org/ jaffe_info.html