Refine your search
Collections
Year
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
Logeshwaran, J.
- SVPA - The Segmentation Based Visual Processing Algorithm (SVPA) For Illustration Enhancements In Digital Video Processing (DVP)
Abstract Views :79 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
2 Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, K.L.N. College of Engineering, IN
4 Department of Automation Control and Robotics, Sheffield Hallam University, GB
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
2 Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, K.L.N. College of Engineering, IN
4 Department of Automation Control and Robotics, Sheffield Hallam University, GB
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2669-2673Abstract
At the present time photographic visual processing is rapidly moving towards the next stage. In addition, a variety of visual processing technologies are evolving, such as splitting image dimensions, calibration, pixel beautification, and high-resolution images. The impact of this digital visual processing technology has now greatly enhanced the opportunities for digital video processing technology and the source of its evolution. The vast industry of converting color images from black and white enables it to present even historical videos of the earlier period in a contemporary manner. In this paper, the segmentation based visual processing algorithm is proposed. The algorithm is designed to enhance resolution and clarity to a certain extent with multi-visual enhanced pixels. It also enhances the contrast, brightness and sharpness enhancement as it is much improved over the previous methods. This algorithm works on each image frame and enhances the overall visual function.Keywords
Visual Processing, Visual Processing, Image Dimension, Calibration, Pixel, Segmentation, Resolution, Contrast, Brightness, SharpnessReferences
- E.H. Adelson, C.H. Anderson and J.M. Ogden, “Pyramid Methods in Image Processing”, RCA Engineer, Vol. 29, No. 6, pp. 33-41, 1984.
- L. Barghout, “Visual Taxometric Approach to Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions”, Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 163-173, 2014.
- V. Maheshwari, M.R. Mahmood and S. Sravanthi, “Nanotechnology-Based Sensitive Biosensors for COVID19 Prediction Using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-7, 2021.
- L. Barghout, “Perceptual Information Processing System”, US Patent App, No. 10/618, pp. 543, 2003.
- H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, “SpeededUp Robust Features (Surf)”, Computer Vision and Image Understanding, Vol 110, No. 3, pp. 346-359, 2008.
- J. Mohana, B. Yakkala, S. Vimalnath and P.M. Benson Mansingh, “Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing”, Journal of Healthcare Engineering, Vol. 2022, pp. 1-7, 2022.
- Y. Bengio “Learning Deep Architectures for Ai”, Foundations and Machine Learning, Vol. 2, No. 1, pp. 120127, 2009.
- L. Bourdev, S. Maji and T. Brox T, Malik, “Detecting People using Mutually Consistent Poselet Activations”, Computer Vision, Vol. 23, No. 2, pp. 168-181, 2010.
- 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.
- L.C. Chen, J.T. Barron and G. Papandreou, “Semantic Image Segmentation with Task Specific Edge Detection using CNNS and a Discriminatively Trained Domain Transform”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 4545-4554, 2016.
- 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.
- L.C. Chen, Y. Yang and J. Wang, “Attention to Scale: ScaleAware Semantic Image Segmentation”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640-3649, 2016.
- A. Cohen, E. Rivlin and I. Shimshoni, “Memory Based Active Contour Algorithm using Pixel-Level Classified Images for Colon Crypt Segmentation”, Computerized Medical Imaging and Graphics, Vol. 43, pp. 150-164, 2019.
- M. Cordts, M. Omran, S. Ramos and T. Rehfeld, “The Cityscapes Dataset for Semantic Urban Scene
- Understanding”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213-3223, 2016.
- 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.ht
- m, 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.
- The Management and Reduction of Digital Noise in Video Image Processing by Using Transmission based Noise Elimination Scheme
Abstract Views :9 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, K.L.N. College of Engineering, IN
2 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
3 Department of Electronics and Communication Engineering, SNS College of Technology, IN
4 SPC Free Zone, AE
1 Department of Information Technology, K.L.N. College of Engineering, IN
2 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
3 Department of Electronics and Communication Engineering, SNS College of Technology, IN
4 SPC Free Zone, AE
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2797-2801Abstract
Digital noise is an image defect that is approximately close to the pixel size and differs in brightness or color from the original image. Noise reduction plays an important role in the transmission, processing and compression of video footage and images. There are a large number of methods for removing noise from images, and they can be used not only by special processing programs, but also in some photo and video cameras. Despite this, there is still no universal filtering algorithm, because when processing an image, there is always a need to choose between preserving small details with properties such as size and noise to eliminate unwanted effects. In this paper, a management and reduction of digital noise in video image processing was discussed in the basis of transmission based noise elimination. In addition, that the proposed scheme easily overcomes the various types of noise. It will identify the spoil the image with another type of noise. Hence the noise affected part will eliminated and reduce the effects of noise.Keywords
Digital Noise, Pixel Size, Brightness, Color, Original Image, Transmission, Processing.References
- Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, “Digital Image Processing Using MATLAB”, Pearson Prentice Hall, 2003.
- Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Prentice Hall, 2008.
- A.K. Jain, “Fundamentals of Digital Image Processing”, Prentice Hall, 1989.
- Pitas I and A.N. Venetsanopoulos, “Nonlinear Mean Filters in Image Processing”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 34, No.3, pp. 573-584, 1986.
- S. Sudha, G.R. Suresh and R. Sukanesh, “Speckle Noise Reduction in Ultrasound Images using Context - based Adaptive Wavelet Thresholding”, IETE Journal of Research, Vol. 55, No. 3, pp. 135, 2009.
- Anil K. Jain, “Fundamentals of Digital Image Processing”, Prentice-Hall, 1989.
- Manish Goyal and Gianetan Singh Sekhon, “Hybrid Threshold Technique for Speckle Noise Reduction using Wavelets for Grey Scale Images”, International Journal of Computer Science and Technology, Vol. 2, No. 2, pp. 620-625, 2011.
- David L. Donoho, “De-Noising by Soft-Thresholding”, IEEE Transactions on Information Theory, Vol. 41, No. 3, pp. 613- 627, 1995.
- R. Sivakumar and D. Nedumaran, “Performance Study of Wavelet Denoising Techniques in Ultrasound Images”, Journal of Instrument Society of India, Vol. 39, No. 3, pp. 194-197, 2009.
- D. Fabijanska and D. Sankowski, “Noise Adaptive Switching Median-Based Filter for Impulse Noise Removal from Extremely Corrupted Images”, IET Image Processing, Vol. 5, No.5, pp. 472-480, 2011.
- Bo Xiong and Zhouping Yin, “A Universal De-Noising Framework with a New Impulse Detector and Non-Local Means”, IEEE Transactions Image Processing, Vol. 21, No. 4, pp. 1663-1675, 2012.
- R. Gayathri and R.S. Sabeenian, “Fast Impulse Noise Removal Algorithm for Medical Images using Improved Weighted Averaging Filtering”, International Journal of Printing, Packaging and Allied Sciences, Vol. 4, No. 1, pp. 661-668, 2016.
- Xuming Zhang and Youlun Xiong, “Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter”, IEEE Signal Processing Letters, Vol. 16, No. 4, pp. 295-298, 2009.
- R. Gayathri and R.S. Sabeenian, “An Independent EdgePreserving Algorithm for Multiple Noises”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, No. 12, pp. 625-632, 2013.
- R. Gayathri and R.S. Sabeenian, “Weighted Square Masking Filter for Efficient Removal of impulse Noise”, International Journal of Electronics and Communication and Computer Engineering, Vol. 5, No. 1, pp. 249-253, 2014.