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Jitkar, Bhagatsing
- An Innovation Development to Eliminate the Red Eye Effects in Visual Image Processing Using Color Scheme Deep Learning Model
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PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, IN
2 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
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, IN
1 Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, IN
2 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
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, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2775-2780Abstract
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
- S. Israni and S. Jain, “Edge Detection of License Plate using Sobel Operator”, Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques, pp. 3561-3563, 2016.
- 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.
- Y. Sun and J. Chen, “Indoor Sound Source Localization with Probabilistic Neural Network”, IEEE Transactions on Industrial Electronics, Vol. 65, No. 8, pp. 6403-6413, 2017.
- P. Yadav and N.P. Singh, “Classification of Normal and Abnormal Retinal Images by using Feature-Based Machine Learning Approach”, Proceedings of International Conference on Recent Trends in Communication, Computing, and Electronics, pp. 387-396, 2019.
- 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.
- The Enhancement of Image Quality in Visual Image Processing by Using Pixel based Digital Filters
Abstract Views :125 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Electronics and Communication Engineering, G Pulla Reddy Engineering College, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
4 Department of Computer Science and Engineering, D Y Patil College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Electronics and Communication Engineering, G Pulla Reddy Engineering College, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
4 Department of Computer Science and Engineering, D Y Patil College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2781-2785Abstract
Digital image processing makes it possible to use the most sophisticated methods, so it can provide both more complex performance in simple tasks and the implementation of impossible methods. Digital cameras usually have a special hardware for image processing (specially added to the circuits or other chips) to convert the original data into a standard image file format in the form of a standard image file changes. The Digital filters are used to blur and sharpen visual based digital images. These filters can be done in the space sphere through the frequency field by covering specially designed cores or certain frequency bands. The images are usually stacked before the Fourier is transferred and the overlay filter images show the results of different layer techniques. In this paper, an innovation filter model was proposed interms of color pixels in the images. The proposed model has a high-pass filter shows extra edges when zero is compared to the repeat edge. The visual transformations make the basic changes of the film, including scaling, rotation, exchange, reflection and cultivation.Keywords
Digital Image Processing, Digital Camera, Digital Filters, Visual, Frequency Bands, Filter Model, Color Pixel.References
- T. Brox, L. Bourdev and S. Maji, “Object Segmentation by Alignment of Poselet Activations to Image Contours”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp 2225–2232, 2015.
- G. Dhiman, A.V. Kumar, R. Nirmalan and S. Sujitha, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 89, pp. 1-25, 2022.
- M. Ramkumar, N. Basker, D. Pradeep and R. Prajapati, “Healthcare Biclustering-Based Prediction on Gene Expression Dataset”, BioMed Research International, Vol. 2022, pp. 1-8, 2022.
- S. Hannah, A.J. Deepa, V.S. Chooralil and S. Brilly Sangeetha, “Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data”, BioMed Research International, Vol. 2022, pp. 1-9, 2022.
- G. Csurka, L. Fan and C. Bray, “Visual Categorization with Bags of Keypoints”, Proceedings of Workshop on Statistical Learning in Computer Vision, pp. 1-2, 2004.
- CVonline: Image Databases, Available at http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm, Accessed at 2021.
- N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
- S. Kumar, “Neighborhood Pixels Weights-A New Feature Extractor”, International Journal of Computer Theory and Engineering, Vol. 2, No. 1, pp. 1793-8201, 2010.
- Il-Seok Oh and Ching Y. Suen, “Distance Features for Neural Network-based Recognition of Handwritten Characters”, International Journal on Document Analysis and Recognition, Vol. 1, No. 2, pp. 73-88, 1998.
- S. Kumar, “Recognition of Pre-Segmented Devanagari Handwritten Characters using Multiple Features and Neural Network Classifier”, Ph.D. Thesis, Panjab University, 2008.
- Richard E. Woods and Rafael C. Gonzalez, “Digital Image Processing”, Prentice Hall Professional Technical Reference, 1992.
- Philip D. Wasserman, “Advanced Methods in Neural Computing”, Van Nostrand Reinhold, 1993.
- Vikas J. Dongre and Vijay H. Mankar, “A Review of Research on Devanagari Character Recognition”, International Journal of Computer Applications, Vol. 12, No. 2, pp. 8-15, 2010.
- J. Tou and R. Gonzalez, “Pattern Recognition Principles”, Addison-Wesley, 1974.
- The Berkeley Segmentation Dataset and Benchmark, Available:http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
- Marco Antonio Garcia Carvalho and André Luis Costa, “Combining hierarchical structures on graphs and normalized cut for image segmentation”, New Frontiers in Graph Theory, pp. 389-406, 2012.
- G. Anbarjafari and H. Demirel, “Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Sub-bands and the Spatial Domain Input Image”, Electronics and Telecommunications Research Institutes Journal, Vol. 32, No. 3, pp. 390-394, 2010.
- G. Anbarjafari and H. Demirel, “Image Resolution enhancement by using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, Vol. 20, No. 5, pp. 1458-1460, 2011.
- M.B. Chappalli and N.K. Bose, “Simultaneous Noise Filtering and Super-Resolution with Second-Generation Wavelets”, IEEE Signal Processing Letters, Vol. 12, No. 11, pp.772-775, 2005.
- D. L. Ward, “Redundant Discrete Wavelet transform based Super-Resolution using Sub-Pixel Image Registration”, Storming Media Publisher, 2003.
- A.K. Naik and R.S. Holambe, “Design of Low-Complexity High-Performance Wavelet Filters for Image Analysis”, IEEE Transactions on Image Processing, Vol. 22, No. 5, pp. 1848-1858, 2013.
- P.P. Vaidyanathan, “Multirate Systems and Filter Banks”, Pearson Education, India, 1993.