Refine your search
Collections
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
Senthil Murugan, J.
- Robust Aircraft Recognition using High Resolution Reconstructed Images
Abstract Views :160 |
PDF Views:0
Authors
Affiliations
1 Department of MCA, Vel Tech High Tech Engineering College, Chennai - 600062, Tamil Nadu, IN
2 Department of CSE, Jaya Engineering College, Chennai - 602024, Tamil Nadu, IN
1 Department of MCA, Vel Tech High Tech Engineering College, Chennai - 600062, Tamil Nadu, IN
2 Department of CSE, Jaya Engineering College, Chennai - 602024, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 3 (2016), Pagination:Abstract
Background: Aircraft Recognition with high resolution space borne images is a challenging task.It is different from other natural object recognition. The ground image of aircraft is complex and disturbing. The target image has to be detected in a cluttered background. Methods: The proposed technique takes into account multiple features such as circular Frequency filter (CF-filter), fuselage symmetry and angle between fuselage axis and wing for candidate selection. Normalized Tree cut algorithm isused for Segmentation of image done and aircraft alignment done using Histogram Oriented Gradient (HOG). Automatic jigsaw puzzle solution is implemented for aircraft reconstruction of pieces. Findings: In the candidate selection stage because of cluttered background the aircraft is not identified. Here the multiple identification techniques such ascircular frequency filter (CF-filter), fuselage symmetry and angle between fuselage axis and wing are considered. Applications: The above said techniques were dealt earlier as independent features. Because of proposed multiple feature consideration accuracy in candidate identification is enhanced.Keywords
Aircraft Recognition, Jigsaw Puzzle Algorithm, Histogram Oriented Gradient- Novel Deep Intelligence Method for the Detection of Environmental Pollutants Using SAR Images on Oceans
Abstract Views :127 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2953-2958Abstract
The decline of marine ecosystems poses a substantial threat to the viability of local economies that are reliant on marine life for their continued survival. Artificial intelligence (AI) and machine learning (ML) are two of the several developing technologies that have the ability to address environmental challenges. In particular, ML may be used to better analyse the oceans, keep track of shipping, maintain track of debris in the ocean, unregulated and unreported (IUU) fishing, ocean mining, reduce coral bleaching, and stop the spread of marine diseases. In this paper, we examine the rising prospects and concerns related with the application of AI in the maritime environment, as well as their potential scalability for larger results, using some use-cases to illustrate our points. The results that were obtained when the model prediction was applied to random images are evidence that the model that was suggested provides better outcomes with fewer data points.Keywords
SAR, Ocean, Pollution, Deep Intelligence, Detection.References
- M. Shaban, R. Salim and A. El-Baz, “A Deep-Learning Framework for the Detection of Oil Spills from SAR Data”, Sensors, Vol. 21, No. 7, pp. 2351-2358, 2021.
- S. Silvia Priscila and M. Ramkumar, “Interactive Artificial Neural Network Model for UX Design”, Proceedings of International Conference on Computing, Communication, Electrical and Biomedical Systems, pp. 277-284, 2022.
- A.S. Dhavalikar, and P.C. Choudhari, “Detection and Quantification of Daily Marine Oil Pollution using Remote Sensing”, Water, Air, and Soil Pollution, Vol. 233, No. 8, pp. 336-345, 2022.
- M. Krestenitis, “Oil Spill Identification from Satellite Images using Deep Neural Networks”, Remote Sensing, Vol. 11, No. 15, pp. 1762-1776, 2019.
- M. Krestenitis, “Early Identification of Oil Spills in Satellite Images using deep CNNs”, Proceedings of International Conference on MultiMedia Modeling, pp. 424-435, 2019.
- T. Blaschke, “Object based Image Analysis for Remote Sensing”, ISPRS Journal of Imagegrammetry and Remote Sensing, Vol. 65, pp. 2-16, 2010.
- Y. Liu, Z. Li, B. Wei, X. Li and B. Fu, “Seismic Vulnerability Assessment at Urban Scale using Data Mining and GIScience Technology: Application to Urumqi (China)”, Geomatics, Natural Hazards Risk, Vol. 10, No. 1, pp. 958-985, 2019.
- Licun Zhou, Guo Cao, Yupeng Li and Yanfeng Shang, “Change Detection based on Conditional Random Field with Region Connection Constraints in High-Resolution Remote Sensing Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 8, pp. 3478-3488, 2016.
- L. Zhang, L. Zhang and B. Du, “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art”, IEEE Geoscience and Remote Sensing Magazine, Vol. 4, No. 2, pp. 22-40, 2016.
- S. Saito, T. Yamashita and Y. Aoki, “Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks”, Electron Imaging, Vol. 60, No. 1, pp. 1-9, 2016.
- S. Cadieux, F. Michaud and F. Lalonde, “Intelligent System for Automated Fish Sorting and Counting”, Proceedings of International Conference on Intelligent Robots and Systems, pp. 1279-1284, 2000.
- H.T. Rauf and Syed Ahmad Chan, “Visual Features based Automated Identification of Fish Species using Deep Convolutional Neural Networks”, Computers and Electronics in Agriculture, Vol. 167, pp. 1-18, 2019.