Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Saisubramaniam Gopalakrishnan"'
Autor:
Qiao ZhongZheng, ArulMurugan Ambikapathi, Pranshu Ranjan Singh, Savitha Ramasamy, Saisubramaniam Gopalakrishnan, Ponnuthurai Nagaratnam Suganthan
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7dad934926b7b6685aadab66915dfe1a
https://doi.org/10.1109/icip42928.2021.9506504
https://doi.org/10.1109/icip42928.2021.9506504
Autor:
Richard Chang, Ren Qin, Vempati Srinivasa Rao, Jens Timo Neumann, Ramanpreet Singh Pahwa, Ramani Pichumani, Saisubramaniam Gopalakrishnan, Wang Jie, Haiwen Dai, Oo Zaw Min, David Ho Soon Wee, Tom Gregorich, Ma Tin Lay Nwe
Publikováno v:
2021 IEEE 71st Electronic Components and Technology Conference (ECTC).
Deep Learning is being widely used to identify and segment various structures in 2D and 3D scans in fields such as robotics and medical imaging. We leverage this exciting technology to train state-of-the-art models for 3D object detection and segment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d54f9bfe2de9f5dfb74f4f03209da7eb
https://doi.org/10.1109/ectc32696.2021.00345
https://doi.org/10.1109/ectc32696.2021.00345
Autor:
Ren Qin, Ong Ee Ping, David Ho Soon Wee, Huang Su, Haiwen Dai, Vempati Srinivasa Rao, Saisubramaniam Gopalakrishnan, Ramanpreet Singh Pahwa
Publikováno v:
2021 IEEE 71st Electronic Components and Technology Conference (ECTC).
Yield improvement is a critical component of semiconductor manufacturing. It is done by collecting, analyzing, identifying the causes of defects, and then coming up with a practical solution to resolve the root causes. Semiconductor components such a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c222c7fc6fd8130725c7d16165c29918
https://doi.org/10.1109/ectc32696.2021.00143
https://doi.org/10.1109/ectc32696.2021.00143
Autor:
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek, Savitha Ramasamy, ArulMurugan Ambikapathi
Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilization of encoded knowledge representations are vital when data is no lon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4634e6469e3c21f5e960713482e29882
http://arxiv.org/abs/2012.06789
http://arxiv.org/abs/2012.06789
Autor:
Sheng Dong, Ramanpreet Singh Pahwa, Oo Zaw Min, Tin Lay Nwe, Shitala Prasad, Saisubramaniam Gopalakrishnan, Dongyun Lin, Yiqun Li
Publikováno v:
ICIP
This paper demonstrates the use of 3D Anisotropic Convolutional Neural Network (CNN) with predict-refine mechanism for 3D brain tumor segmentation. We propose two networks that utilize multi-scale feedback and saliency maps respectively to segment th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb319a367c9255a12cffc614ee652e50
https://doi.org/10.1109/icip40778.2020.9190806
https://doi.org/10.1109/icip40778.2020.9190806
Publikováno v:
ICIP
Quantitative measurements obtained from medical images guide clinicians in several use cases but manually obtaining such measurements are both laborious and subject to inter-observer variations. We develop a hybrid deep reinforced regression framewor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d8403783588c774d1454e30267f52a9
https://doi.org/10.1109/icip40778.2020.9191287
https://doi.org/10.1109/icip40778.2020.9191287
Autor:
Pranshu Ranjan Singh, Vijay Chandrasekhar, Yasin Yazici, ArulMurugan Ambikapathi, Chuan-Sheng Foo, Saisubramaniam Gopalakrishnan
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative representations are rich in class specific features but are too sparse f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8c62718d4e766896c450f9aff0f21f7