ICTACT Journal on Image and Video Processing

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An Innovation Development to Eliminate the Red Eye Effects in Visual Image Processing Using Color Scheme Deep Learning Model


Affiliations
1 Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, India
2 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, India
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical Universityin
4 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra College of Agriculture, India
     

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Visual Image processing is seriously used to motion photograph, modeling, printed thing and placing articles on the Internet. There is a wide mass of options, methods, tools and implementing this process. The processing task of the image processing is to give them the most clearly and clearly real value or type, in which they are distorted. The preparation of the films from the image allows you to remove unwanted items brightly in the eyes. It is mainly to eliminate the effect of the red eye effect and drag the figure. In this paper an innovation model was proposed to eliminate the “Red Eye Effect (REE)”. This proposed method is based on the adjustment that is raised. The visual image processing is that the clarity of the image objects has increased. The film is mostly digital cameras default or threaded transfer color. White balance adjustment sliders can be used by heat. Some image processing programs and make this separate treatment is a purpose. Setting up different digital cameras will allow you to set the best expression in shooting. However, this is always possible. So it should be adjusted by a subsequent visual image processing.

Keywords

Visual Image Processing, Motion Photograph, Red Eye Effect, Digital Cameras.
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  • N. Mathur, S. Mathur and D. Mathur, “A Novel Approach to Improve Sobel Edge Detector”, Procedia Computer Science, Vol. 93, pp. 431-438, 2016.

  • M.G. Raman, N. Somu and A.P. Mathur, “Anomaly Detection in Critical Infrastructure using Probabilistic Neural Network”, Proceedings of International Conference on Applications and Techniques in Information Security, pp. 129-141, 2019.

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  • S.P. Rajan, “Recognition of Cardiovascular Diseases through Retinal Images using Optic Cup to Optic Disc Ratio”, Pattern Recognition and Image Analysis, Vol. 30, No. 2, pp. 256-263, 2020.

  • S. Mishra and M. Banerjee, “Automatic Caption Generation of Retinal Diseases with Self-trained RNN Merge Model”, Proceedings of International Conference on Advanced Computing and Systems for Security, pp. 1-10, 2020.

  • E. Tuba, L. Mrkela and M. Tuba, “Retinal Blood Vessel Segmentation by Support Vector Machine Classification”, Proceedings of International Conference on Radioelektronika, pp. 1-6, 2017.

  • M. Ramzan, H.U. Khan and S.M. Awan, “A Survey on State-of-the-Art Drowsiness Detection Techniques”, IEEE Access, Vol. 7, pp. 61904-61919, 2019.

  • G.D. Finlayson, “Colour and Illumination in Computer Vision”, Interface Focus, Vol. 8, No. 4, pp. 1-12, 2018.

  • J. Ma, X. Fan and S. Yang, “Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 7, pp. 1-14, 2018.

  • Y. Ji, S. Wang and Y. Zhao, “Eye and Mouth State Detection Algorithm based on Contour Feature Extraction”, Journal of Electronic Imaging, Vol. 27, No. 5, pp. 1-13, 2018.

  • Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Addison-Wesley, 1993.

  • Yousra Ben Jemaa and Sana Khanfir, “Automatic Local Gabor Features Extraction for Face Recognition”, International Journal of Computer Science and Information Security, Vol. 3, No. 1, pp. 1-14, 2009.

  • A. Azmoodeh, A. Dehghantanha and K.K.R. Choo, “Robust Malware Detection for Internet of (Battlefield) Things Devices using deep Eigenspace Learning”, IEEE Transactions on Sustainable Computing, Vol. 4, No. 1, pp. 88-95, 2018.

  • Richard Yew Fatt Ng, Yong Hour Tay and Kai Ming Mok, “A Review of Iris Recognition Algorithm”, Proceedings of International Symposium on Information Technology, pp. 26-32, 2008.

  • Seifedine Kadry and Khaled Smaili, “A Design and Implementation of a Wireless Iris Recognition Attendance Management System”, Information Technology and Control, Vol. 36, No. 3, pp. 323-329, 2007.

  • M. Mattam, S.R.M. Karumuri and S.R. Meda, “Architecture for Automated Student Attendance”, Proceedings IEEE International Conference on Technology for Education, pp. 164-167, 2012.

  • Iris Recognition, Available at: http://en.wikipedia.org/wiki/Iris_recognition, Accessed at 2021.

  • Ujwalla Gawande, Mukesh Zaveri and Avichal Kapur, “Improving Iris Recognition Accuracy by Score Based Fusion Method”, International Journal of Advancements in Technology, Vol. 1, No. 1, pp. 1-8, 2010.

  • N. Sudha, N.B. Puhan, H. Xia and X. Jiang, “Iris Recognition on Edge Maps”, IET Computer Vision, Vol. 3, No. 1, pp. 1-7, 2009.


Abstract Views: 146

PDF Views: 1




  • An Innovation Development to Eliminate the Red Eye Effects in Visual Image Processing Using Color Scheme Deep Learning Model

Abstract Views: 146  |  PDF Views: 1

Authors

N. Prakash
Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, India
Sangram Patil
Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, India
Bhagatsing Jitkar
Department of Computer Science and Engineering, D Y Patil Agricultural and Technical Universityin
Suresh Kumar Sharma
Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra College of Agriculture, India

Abstract


Visual Image processing is seriously used to motion photograph, modeling, printed thing and placing articles on the Internet. There is a wide mass of options, methods, tools and implementing this process. The processing task of the image processing is to give them the most clearly and clearly real value or type, in which they are distorted. The preparation of the films from the image allows you to remove unwanted items brightly in the eyes. It is mainly to eliminate the effect of the red eye effect and drag the figure. In this paper an innovation model was proposed to eliminate the “Red Eye Effect (REE)”. This proposed method is based on the adjustment that is raised. The visual image processing is that the clarity of the image objects has increased. The film is mostly digital cameras default or threaded transfer color. White balance adjustment sliders can be used by heat. Some image processing programs and make this separate treatment is a purpose. Setting up different digital cameras will allow you to set the best expression in shooting. However, this is always possible. So it should be adjusted by a subsequent visual image processing.

Keywords


Visual Image Processing, Motion Photograph, Red Eye Effect, Digital Cameras.

References