Zobrazeno 1 - 10
of 35
pro vyhledávání: '"ArulMurugan Ambikapathi"'
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Autor:
Yanpeng Zhou, Maosen Wang, Manas Gupta, Arulmurugan Ambikapathi, Ponnuthurai Nagaratnam Suganthan, Savitha Ramasamy
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
Neurocomputing. 417:155-166
Accurate heart rate is vital to acquiring critical physical data of human subjects. For this reason, facial video-based heart rate estimation has recently received tremendous attention owing to its simplicity and convenience. However, its accuracy, r
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).
Publikováno v:
2021 IEEE International Conference on Multimedia and Expo (ICME).
Steganalysis can be characterized as detecting a weak noise signal (hidden information) in textured regions of naturally occurring images. These noise signals are typically not perceptible to human eyes, which renders steganalysis a challenging task.
Publikováno v:
ICASSP
Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. Hebbian learning, a completely unsupervised and feedback free learning technique is a strong contender for a biologically plausible
Over the past few decades, the field of machine learning has made remarkable strides, surpassing human performance in tasks like voice and object recognition, as well as mastering various complex games. Despite these accomplishments, a critical chall
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
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