Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Santosh Kumar Vipparthi"'
Publikováno v:
IET Computer Vision, Vol 13, Iss 6, Pp 578-587 (2019)
During the last decade, there has been profound progress in the field of visual saliency. However, there still exist various major challenges that hinder the detection performance for scenes with complex composition, presence of additive noise, objec
Externí odkaz:
https://doaj.org/article/a71b789bd22e4a01aebfc705f01b5ae7
Autor:
Murari Mandal, Mallika Chaudhary, Santosh Kumar Vipparthi, Subrahmanyam Murala, Anil Balaji Gonde, Shyam Krishna Nagar
Publikováno v:
IET Computer Vision, Vol 13, Iss 1, Pp 31-43 (2019)
In this study, new feature descriptors are designed for medical image retrieval and change detection applications, respectively. Inspired by isomerism, the authors propose a novel feature descriptor named antithetic isomeric cluster pattern (ANTIC).
Externí odkaz:
https://doaj.org/article/b366be63bca744a7bef3682c2420f80f
Publikováno v:
IET Computer Vision, Vol 10, Iss 3, Pp 182-192 (2016)
This study proposes a new feature descriptor, local directional mask maximum edge pattern, for image retrieval and face recognition applications. Local binary pattern (LBP) and LBP variants collect the relationship between the centre pixel and its su
Externí odkaz:
https://doaj.org/article/0ec05cd688c842a384675441842b2106
Autor:
Monu Verma, M Satish Kumar Reddy, Murari Mandal, Santosh Kumar Vipparthi, Yashwanth Reddy Meedimale
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:6116-6128
Facial microexpressions offer useful insights into subtle human emotions. This unpremeditated emotional leakage exhibits the true emotions of a person. However, the minute temporal changes in the video sequences are very difficult to model for accura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e4d4d6322ed82ebda248909000cc3ee
https://doi.org/10.1109/tnnls.2021.3072290
https://doi.org/10.1109/tnnls.2021.3072290
Autor:
Santosh Kumar Vipparthi, Murari Mandal
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:2031-2044
Visual change detection in video is one of the essential tasks in computer vision applications. Recently, a number of supervised deep learning methods have achieved top performance over the benchmark datasets for change detection. However, inconsiste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e58c149f2ef6669efc965c9d1e9976f3
https://doi.org/10.1109/tits.2020.3030801
https://doi.org/10.1109/tits.2020.3030801
Publikováno v:
Multimedia Tools and Applications. 82:4863-4882
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd1d361db24d886fa2a394899f3d2210
https://doi.org/10.1007/s11042-021-11623-3
https://doi.org/10.1007/s11042-021-11623-3
Publikováno v:
IEEE Transactions on Artificial Intelligence. :1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e9cffffb4d023a02597a2bcaa53d93c
https://doi.org/10.1109/tai.2022.3204732
https://doi.org/10.1109/tai.2022.3204732
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d09f4952a403c5c424ad445946e6b93
https://doi.org/10.1109/wacv56688.2023.00475
https://doi.org/10.1109/wacv56688.2023.00475
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fed9cbe51983b30c6735b53cea69dee9
https://doi.org/10.1109/icip46576.2022.9897970
https://doi.org/10.1109/icip46576.2022.9897970
Publikováno v:
The Visual Computer. 38:3853-3866
In this paper, a lightweighted Intensive Feature Extrication Deep Network (ExtriDeNet) is proposed for precise hand gesture recognition (HGR). ExtriDeNet primarily consists of two blocks: Intensive Feature Fusion Block (IFFB) and Intensive Feature As
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a186db7f60be38c58fd665d160bc5c92
https://doi.org/10.1007/s00371-021-02225-z
https://doi.org/10.1007/s00371-021-02225-z