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RISS 인기검색어
- 검색키워드
- 저자 : Samayamantula, (검색결과 3 건)
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Discrete Multiwavelet–Based Video Watermarking Scheme Using SURF
Leelavathy Narkedamilly,Srinivas Kumar Samayamantula,Venkateswara Prasad Evani 한국전자통신연구원 2015 ETRI Journal Vol.37 No.3
This paper proposes a robust, imperceptible block-based digital video watermarking algorithm that makes use of the Speeded Up Robust Feature (SURF) technique. The SURF technique is used to extract the most important features of a video. A discrete multiwavelet transform (DMWT) domain in conjunction with a discrete cosine transform is used for embedding a watermark into feature blocks. The watermark used is a binary image. The proposed algorithm is further improved for robustness by an error-correction code to protect the watermark against bit errors. The same watermark is embedded temporally for every set of frames of an input video to improve the decoded watermark correlation. Extensive experimental results demonstrate that the proposed DMWT domain video watermarking using SURF features is robust against common image processing attacks, motion JPEG2000 compression, frame averaging, and frame swapping attacks. The quality of a watermarked video under the proposed algorithm is high, demonstrating the imperceptibility of an embedded watermark.
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Multimodal Medical Image Fusion Based on Sugeno’s Intuitionistic Fuzzy Sets
Talari Tirupal,Bhuma Chandra Mohan,Samayamantula Srinivas Kumar 한국전자통신연구원 2017 ETRI Journal Vol.39 No.2
Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno’s intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno’s intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.
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Acreage estimation of kharif rice crop using Sentinel-1 temporal SAR data
Subbarao Nandepu V. V. S. S. Teja,Mani Jugal Kishore,Shrivastava Ashish,Srinivas Kumar Samayamantula,Varghese A. O. 대한공간정보학회 2021 Spatial Information Research Vol.29 No.4
Rice is one of the most important food crop in India covering about one-fourth of the total cropped area. India is the second largest producer and consumer of rice and accounts for 21% of the world’s total rice production. Rice is fundamentally a kharif season crop and grown in mainly rainfed areas. Recently there is a considerable increase in production, area and yield of rice crop in India. Temporal monitoring of crop area under cultivation is essential for the sustainable management of agricultural activities on both national and global levels. The present study is envisaged to estimate area under kharif rice using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data with dual polarization (VH and VV) in Bhandara district of Maharashtra. The geographical area of Bhandara district is 4087 square kilometres and lies in between 20640 030 ’ to 21600 180 ’ N latitude and 79440 930 ’ to 80080 700 ’ E longitude. The rice area is extracted using Random Forest (RF) classification techniques available in SNAP tool and validated using the ground observation collected from the field. An area of 1760 square kilometres was found under kharif rice out of 4087 square kilometres area of entire Bhandara district. The rice is predominant crop and covered around 43% of the total geographical area of Bhandara district during kharif season. The user accuracy (omission error), producer accuracy (commission error) for rice crop, overall accuracy and Kappa coefficients were 82.7, 90.0, 91% and 0.80, respectively. The study found that SAR data can be successfully used for acreage estimation with RF classifier.
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