Refine your search
Collections
Co-Authors
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
Patavardhan, Prashant P.
- Segmentation and Quality Analysis of Long Range Captured IRIS Image
Abstract Views :150 |
PDF Views:3
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 4 (2016), Pagination: 1280-1283Abstract
The iris segmentation plays a major role in an iris recognition system to increase the performance of the system. This paper proposes a novel method for segmentation of iris images to extract the iris part of long range captured eye image and an approach to select best iris frame from the iris polar image sequences by analyzing the quality of iris polar images. The quality of iris image is determined by the frequency components present in the iris polar images. The experiments are carried out on CASIA-long range captured iris image sequences. The proposed segmentation method is compared with Hough transform based segmentation and it has been determined that the proposed method gives higher accuracy for segmentation than Hough transform.Keywords
Segmentation, Quality Score, Long Range Iris Images.- Single Frame Super Resolution of Noncooperative IRIS Images
Abstract Views :184 |
PDF Views:3
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 2 (2016), Pagination: 1362-1365Abstract
Image super-resolution, a process to enhance image resolution, has important applications in biometrics, satellite imaging, high definition television, medical imaging, etc. The long range captured iris identification systems often suffer from low resolution and meager focus of the captured iris images. These degrade the iris recognition performance. This paper proposes enhanced iterated back projection (EIBP) method to super resolute the long range captured iris polar images. The performance of proposed method is tested and analyzed on CASIA long range iris database by comparing peak signal to noise ratio (PSNR) and structural similarity index (SSIM) with state-of-the-art super resolution (SR) algorithms. It is further analyzed by increasing the up-sampling factor. Performance analysis shows that the proposed method is superior to state-of-the-art algorithms, the peak signal-to-noise ratio improved about 0.1-1.5 dB. The results demonstrate that the proposed method is well suited to super resolve the iris polar images captured at a long distance.Keywords
Super Resolution, Iterated Back Projection, Iris Recognition, SSIM, PSNR.- Survey of Super Resolution Techniques
Abstract Views :197 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, IN
2 Department of Electronics and Communication Engineering, Gogte Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1927-1934Abstract
The key objective of super-resolution imaging is the process of reconstructing a high resolution image from one or a set of low resolution images, to overcome the ill-posed conditions of the image capturing process to get better visualization. It has found practical applications in many real world problems such as surveillance, medical imaging, text image analysis, biometrics, satellite imaging, to name a few. This has encouraged many researchers to develop a new super resolution algorithm for a particular purpose. The objective of the review paper is to explore the different image super resolution algorithms used to enhance the low resolution images to high resolution images. This survey on super resolution algorithms will help the researchers to comprehend the effectiveness of the super resolution process and will make easy to develop advanced super resolution methods.Keywords
Super Resolution, PSNR, Frequency Domain, Gaussian Process Regression, Total Variation.References
- Subhasis Chaudhuri, “Super Resolution Imaging”, Kluwer Academic Publishers, 2000.
- Sung Cheol Park, Min Kyu Park and Moon Gi Kang, “Super-Resolution Image Reconstruction: A Technical Overview”, IEEE Signal Processing Magazine, Vol. 20, No. 3, pp. 21-36, 2003.
- R.Y. Tsai and T.S. Huang, “Multiframe Image Restoration and Registration”, Advances in Computer Vision and Image Processing, Vol. 1, pp. 317-339, 1984.
- P. Vandewalle, “A Frequency Domain Approach to Super-Resolution Imaging from Aliased Low Resolution Images”, Master Thesis, Department of Electrical Engineering and Computer Science, University of California, 2004.
- Hasan Demirel and Gholamreza Anbarjafari, “Image Resolution Enhancement by using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, Vol. 20, No. 5, pp. 1458-1460, 2011.
- N. Nguyen and P. Milanfar, “A Wavelet-based Interpolation-Restoration Method for Super Resolution”, Circuits Systems and Signal Processing, Vol. 19, No. 4, pp. 321-338, 2000.
- S.E. Ei-Khamy, M.M. Hadhoud, M.I. Dessouky, B.M. Salam and F.E. Abd El-Samie, “Regularized Super-Resolution Reconstruction of Images using Wavelet Fusion”, Optical Engineering, Vol. 44, No. 9, pp. 1-10, 2005.
- S.E. Ei-Khamy, M.M. Hadhoud, M.I. Dessouky, B.M. Salam and F.E. Abd El-Samie, “Wavelet Fusion: a Tool to Break the Limits on LMMSE Image Super-Resolution”, International Journal of Wavelet Multi Resolution, Vol. 4, No. 1, pp. 105-118, 2006.
- H. Ji and C. Fermuller, “Wavelet-based Super-Resolution Reconstruction: Theory and Algorithm”, Proceedings of European Conference on Computer Vision, pp. 262-268, 2006.
- H. Ji and C. Fermuller, “Robust Wavelet-based Super-Resolution Reconstruction: Theory and Algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 4, pp. 649-660, 2009.
- Anand Deshpande and Prashant Patavardhan, “Unconstrained Iris Image Super Resolution in Transform Domain”, Proceedings of International Conference on Networking Communication and Data Knowledge Engineering, pp. 173-180, 2017.
- R. Franke, “Scattered Data Interpolation: Tests of Some Methods”, Mathematics of Computation, Vol. 38, No. 157, pp. 181-200, 1982.
- J. Allebach and P.W. Wong, “Edge-Directed Interpolation”, Proceedings of IEEE International Conference on Image Processing, pp. 707-710, 1996.
- F.N. Fritsch and R.E. Carlson, “Monotone Piecewise cubic interpolation”, SIAM Journal on Numerical Analysis, Vol. 17, No. 2, pp. 238-246, 1980.
- Xin Li and Michael T. Orchard, “Newedge-Directed Interpolation”, IEEE Transactions on Image Processing, Vol. 10, No. 10, pp. 152-1527, 2001.
- D. Zhang and Xiaolin Wu, “An Edge-Guided Image Interpolation Algorithm Via Directional Filtering and Data Fusion”, IEEE Transactions on Image Processing, Vol. 15, No. 8, pp. 2226-2238, 2006.
- X. Li and T.Q. Nguyen, “Markov Random Field Model-based Edge Directed Image Interpolation”, IEEE Transactions on Image Processing, Vol. 7, No. 7, pp. 1121-1128, 2008.
- Anand Deshpande, Prashant Patavardhan and D.H. Rao, “Iterated Back Projection Based Super-Resolution for Iris Feature Extraction”, Procedia Computer Science, Vol. 48, pp. 269-275, 2015.
- Anand Deshpande and Prashant Patavardhan, “Single Frame Super Resolution of Non-Cooperative Iris Images”, ICTACT Journal on Image and Video Processing, Vol. 7, No. 2, pp. 1362-1365, 2016.
- M. Bertero and P. Boccacci, “Introduction to Inverse Problems in Imaging”, 1st Edition, Routledge, 1998.
- M. Irani and S. Peleg, “Improving Resolution by Image Registration”, CVGIP: Graphical Models and Image Processing, Vol. 53, No. 3, pp. 231-239, 1991.
- Manjunath V. Joshi, Subhasis Chaudhuri and Rajkiran Panuganti, “A Learning-Based Method for Image Super-Resolution from Zoomed Observations”, IEEE Transactions on Systems, Man, and Cybernetic-Part B: Cybernetics, Vol. 35, No. 3, pp. 441-456, 2005.
- Antonio Marquina and Stanley J. Osher, “Image Super-Resolution by TV- Regularization and Bregman Iteration”, Journal of Scientific Computing, Vol. 37, No. 3, pp. 367-382, 2008.
- Shen Lijun, Xiao Zhi Yun and Han Hua, “Image Super-Resolution based on MCA and Wavelet Domain HMT”, Proceedings of IEEE International Forum on Information Technology and Applications, pp. 1-8, 2010.
- H. Xu, G. Zhai and X. Yang, “Single Image Super-Resolution with Detail Enhancement based on Local Fractal Analysis of Gradient”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 23, No. 10, pp. 1740-1754, 2013.
- Michael K. Ng, Huanfeng Shen, Edmund Y. Lam and Liangpei Zhang, “A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video”, EURASIP Journal on Advances in Signal Processing, Vol. 4, No. 1, pp. 103-112, 2007.
- Qiangqiang Yuan, Liangpei Zhang and Huanfeng Shen, “Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 3, pp. 561-574, 2012.
- Zemin Ren, Chuanjiang He and Qifeng Zhang, “Fractional Order Total Variation Regularization for Image Super-Resolution”, Journal Signal Processing, Vol. 93, No. 9, pp. 2408-2421, 2013.
- Anand Deshpande and Prashant Patavardhan, “Gaussian Process Regression Based Iris Polar Image Super Resolution”, Proceedings of IEEE International Conference on Applied and Theoretical Computing and Communication Technology, pp. 1123-1129, 2016.
- Anand Deshpande and Prashant Patavardhan, “Super Resolution of Long Range Captured Multiframe Iris Polar Images”, IET Biometrics, Vol. 6, No. 5, pp. 360-368, 2017.
- Anand Deshpande and Prashant Patavardhan, “Multiframe Super-Resolution for Long Range Captured Iris Polar Image”, IET Biometrics, Vol. 6, No. 2, pp. 108-116, 2017.
- Jieping Xu, Yonghui Liang, Jin Liu and Zongfu Huang, “Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images”, Sensors, Vol. 17, No. 9, pp. 23-35, 2017.
- Mingzhu Shi and Liang Feng, “A Novel Local and Nonlocal Total Variation Combination Method for Image Restoration in Wireless Sensor Networks”, EURASIP Journal on Wireless Communications and Networking, Vol. 14, No. 2, pp. 113-118, 2017.
- L. Liu, W. Huang and C. Wang, “Texture Image Prior for SAR Image Super Resolution based on Total Variation Regularization using Split Bregman Iteration”, International Journal of Remote Sensing, Vol. 38, No. 2, pp. 212-219, 2017.
- Q. Wang, X. Tang and H. Shum, “Patch based Blind Image Super-Resolution”, Proceedings of IEEE Conference on Computer Vision, pp. 709-716, 2005.
- T. Huang, “Earning based Resolution Enhancement of Iris Images”, Proceedings of British Machine Vision Conference, pp. 1-6, 2003.
- A. Chakrabarti, A.N. Rajagopalan and R. Chellappa, “Super-Resolution of Face Images using Kernel PCA-based Prior”, IEEE Transactions on Multimedia, Vol. 9, No. 4, pp. 888-892, 2007.
- F. Brandi, R. De Queiroz and D. Mukherjee, “Super Resolution of Video using Key Frames”, Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1608-1611, 2008.
- K. Shin, “Super-Resolution Method based on Multiple Multi-Layer Perceptrons for Iris Recognition”, Proceedings of 4th International Conference on Ubiquitous Information Technologies Applications, pp. 661-668, 2009.
- J. Wang, S. Zhu and Y. Gong, “Resolution Enhancement based on Learning the Sparse Association of Image Patches”, Pattern Recognition Letters, Vol. 31, pp. 1-10, 2010.
- Y.-W. Tai, S. Liu, M.S. Brown and S. Lin, “Super Resolution using Edge Prior and Single Image Detail Synthesis”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 23-29, 2010.
- K. Zhang, “Single Image Super-Resolution with Multi-Scale Similarity Learning”, IEEE Transactions on Neural Network Learning System, Vol. 24, No. 10, pp. 1648-1659, 2013.
- C.E. Rasmussen and C.K.I. Williams, “Gaussian Processes for Machine Learning”, MIT Press, 2006.
- C.E. Rasmussen and H. Nickisch, “Gaussian Processes for Machine Learning (GPML) Toolbox”, Journal of Machine Learning Research, Vol. 11, pp. 3011-3015, 2010.
- K. Ashish, G. Kristan, U. Raphel, D. Trevor, “Gaussian Process for Object Categorization”, International Journal Computer Vision, Vol. 88, No. 2, pp. 169-188, 2010.
- R. Urtasun, D.J. Fleet, A. Hertzman and P. Fua, “Priors for People Tracking from Small Training Sets”, Proceedings of 10th IEEE International Conference on Computer Vision, pp. 403-410, 2005.
- R. Urtasun, D.J. Fleet, A. Hertzman and P. Fua, “3D People Tracking with Gaussian Process Dynamical Models”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 17-22, 2006.
- O. Williams, “A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo”, Proceedings of IEEE International Conference on Advances in Neural Information Processing Systems, pp. 1497-1504, 2006.
- H. He and W.C. Siu, “Single Image Super-Resolution using Gaussian Process Regression”, Proceedings of IEEE conference proceedings on Pattern Recognition, pp. 449-456, 2011.
- Fang Xie, Cheng Deng and Jie Xu, “Image Super Resolution using Gaussian Process Regression with Patch Clustering”, Proceedings of 5th International Conference on Internet Multimedia Computing and Service, pp. 109-112, 2013.
- J. Li, Y. Qu, C. Li, Y. Xie, Y. Wu and J. Fan, “Learning Local Gaussian process Regression for Image Super-Resolution”, Neurocomputing, Vol. 154, pp. 284-295, 2015.
- Yu Zhu, Yanning Zhang and Alan L. Yuille, “Single Image Super-Resolution using Deformable Patches”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 166-175, 2014.
- Chinh Dang, Mohammad Aghagolzadeh and Hayder Radha, “Image Super-Resolution via Local Self-Learning Manifold Approximation”, IEEE Signal Processing Letters, Vol. 21, No. 10, pp. 1123-1138, 2014.
- X. Li, J. Chen, Z. Cui, M. Wu and X. Zhu, “Single Image Super-Resolution Based on Sparse Representation with Adaptive Dictionary Selection”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 30, No. 7, pp. 11-17, 2016.
- Anand Deshpande and Prashant Patavardhan, (Eds.), “Super Resolution of Long Range Captured Iris Image Using Deep Convolutional Network”, Deep Learning for Image Processing Applications, IOS Press, 2017.