Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Chanda, Sukalpa"'
Autor:
Ferrer, Miguel A., Chanda, Sukalpa, Diaz, Moises, Banerjee, Chayan Kr., Majumdar, Anirban, Carmona-Duarte, Cristina, Acharya, Parikshit, Pal, Umapada
Publikováno v:
IEEE Transactions on Cybernetics, v. 48(10), p. 2896-2907, 2018
Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the m
Externí odkaz:
http://arxiv.org/abs/2401.17026
Recent advancements in large-scale pre-training of visual-language models on paired image-text data have demonstrated impressive generalization capabilities for zero-shot tasks. Building on this success, efforts have been made to adapt these image-ba
Externí odkaz:
http://arxiv.org/abs/2312.08010
Autor:
Srivastava, Abhishek, Chanda, Sukalpa, Jha, Debesh, Riegler, Michael A., Halvorsen, Pål, Johansen, Dag, Pal, Umapada
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of disease can play a vital role in treatment and decision
Externí odkaz:
http://arxiv.org/abs/2111.10618
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp se
Externí odkaz:
http://arxiv.org/abs/2111.10614
Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier writer identification relied on handcrafted features to reveal pieces of di
Externí odkaz:
http://arxiv.org/abs/2111.10605
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination
The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive
Externí odkaz:
http://arxiv.org/abs/2111.10591
Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc. In order to aid the training process, existing methods either use a hard mining strategy to extract the most in
Externí odkaz:
http://arxiv.org/abs/2108.09335
Faces generated using generative adversarial networks (GANs) have reached unprecedented realism. These faces, also known as "Deep Fakes", appear as realistic photographs with very little pixel-level distortions. While some work has enabled the traini
Externí odkaz:
http://arxiv.org/abs/2107.10756
Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not f
Externí odkaz:
http://arxiv.org/abs/2105.15093
Autor:
Srivastava, Abhishek, Jha, Debesh, Chanda, Sukalpa, Pal, Umapada, Johansen, Håvard D., Johansen, Dag, Riegler, Michael A., Ali, Sharib, Halvorsen, Pål
Publikováno v:
IEEE Journal of Biomedical and Health Informatics, 2022
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are commo
Externí odkaz:
http://arxiv.org/abs/2105.07451