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Leo, Sathesh Abraham
- Image Representation and Rendering From Low-Resolution Surveillance Videos Using Densenet
Abstract Views :134 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3155-3160Abstract
In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems.Keywords
Surveillance, Low-Resolution Videos, DenseNet, Image Representation, Rendering
References
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- Z. Chen and X. Xie, “CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution”, Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 21185-21195, 2023.
- Z.T. Jahromi, S.M.T. Hasheminejad and S.V. Shojaedini, “Deep Learning Semantic Image Synthesis: A Novel Method for Unlimited Capacity, High Noise Resistance Coverless Video Steganography”, Multimedia Tools and Applications, Vol. 89, pp. 1-19, 2023.
- Z. Zhao and W. Gao, “Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation”, Electronics, Vol. 12, No. 13, pp. 2773-2779, 2023.
- Y. Zhang and C. Busch, “NTIRE 2023 Challenge on Image Super-Resolution (X4): Methods and Results”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1864-1883, 2023.
- C. Niu and D. Tarapore, “An Embarrassingly Simple Approach for Visual Navigation of Forest Environments”, Frontiers in Robotics and AI, Vol. 67, pp. 10-19, 2023.
- D.C. Lepcha and V. Goyal, “Image Super-Resolution: A Comprehensive Review, Recent Trends, Challenges and Applications”, Information Fusion, Vol. 91, pp. 230-260, 2023.
- K. Chauhan and R. Sharma, “Deep Learning-based Single-Image Super-resolution: A Comprehensive Review”, IEEE Access, Vol. 9, pp. 1-12, 2023.
- A. Greco, M. Vento and V. Vigilante, “Benchmarking Deep Networks for Facial Emotion Recognition in the Wild”, Multimedia Tools and Applications, Vol. 82, No. 8, pp. 11189-11220, 2023.
- T.A. Kadhim and D. Ben Aissa, “A Face Recognition Application for Alzheimer’s Patients using ESP32-CAM and Raspberry Pi”, Journal of Real-Time Image Processing, Vol. 20, No. 5, pp. 100-114, 2023.
- A Hybrid Machine Learning Approach for Early Detection of Paddy Blight Disease
Abstract Views :88 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3068-3074Abstract
Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.Keywords
Paddy Blight, Disease Detection, Machine Learning, Fuzzy Logic, Deep Learning, Treatment Recommendation.References
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- A. Chug and D. Singh, “A Novel Framework for Image-Based Plant Disease Detection using Hybrid Deep Learning Approach”, Soft Computing, Vol. 74, No. 1, pp. 1-26, 2022.
- J. Gowrishankar and N. Narmadha, “Convolutional Neural Network
- Classification on 2D Craniofacial Images”, International Journal of Grid and Distributed Computing, Vol. 13, No. 1, pp. 1026-1032, 2020.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-12, 2022.
- K. Anandhan and A.S. Singh, “Detection of Paddy Crops Diseases and Early Diagnosis using Faster Regional Convolutional Neural Networks”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 898-902, 2021.
- S. Lamba, S. Rani and S.H. Ahmed, “A Novel Hybrid Severity Prediction Model for Blast Paddy Disease using Machine Learning”, Sustainability, Vol. 15, No. 2, pp. 1502-1512, 2023.
- A. Sirohi and A. Malik, “A Hybrid Model for the Classification of Sunflower Diseases using Deep Learning”, Proceedings of International Conference on Intelligent Engineering and Management, pp. 58-62, 2021.
- S. Chidambaram and D. Shreecharan, “Hyperspectral Image Classification using Denoised Stacked Auto Encoder-Based Restricted Boltzmann Machine Classifier”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 213-221, 2022.
- R. Sharma, A. Bansal and A. Kaur, “Rice Leaf blight Disease Detection using Multi-Classification Deep Learning Model”, Proceedings of International Conference on Reliability, Infocom Technologies and Optimization Trends and Future Directions, pp. 1-5, 2022.
- Z. Liu, M. Tausif and Q. Umer, “Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction-
- Blister Blight for Tea Plant”, IEEE Access, Vol. 10, pp. 44934-44944, 2022.
- G. Dhiman and K. Srihari, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 82, No. 4, pp. 5343-5367, 2023.
- R. Sangeetha and J. Lloret, “An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves”, Agri Engineering, Vol. 5, No. 2, pp. 660-679, 2023.
- N. Jiwani and M. Alibakhshikenari, “Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) using Computational Deep Learning”, IEEE Access, Vol. 11, pp. 29541-29553, 2023.
- J. Zhang and Y. He, “Rice Bacterial Blight Resistant Cultivar Selection based on Visible/Near-Infrared Spectrum and Deep Learning”, Plant Methods, Vol. 18, No. 1, pp. 1-16, 2022.
- M.H. Tunio and I. Memon, “Identification and Classification of Rice Plant Disease using Hybrid Transfer Learning”, Proceedings of International Conference on Computer on Wavelet Active Media Technology and Information Processing, pp. 525-529, 2021.