His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Real image and Image restoration. His research related to Singular value decomposition, Deblurring, Artificial neural network, Feature extraction and Visualization might be considered part of Artificial intelligence. Paolo Favaro combines subjects such as Sequence and Microlens with his study of Computer vision.
His work in the fields of Pattern recognition, such as Feature learning and Range segmentation, intersects with other areas such as Constant. His Real image research integrates issues from 3D reconstruction and Iterative reconstruction. His 3D reconstruction research incorporates themes from Image resolution and Image formation.
Paolo Favaro mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Image and Deblurring. Image restoration, Iterative reconstruction, Real image, Pixel and Artificial neural network are the core of his Artificial intelligence study. In Computer vision, Paolo Favaro works on issues like Blind deconvolution, which are connected to Kernel.
His Pattern recognition study combines topics from a wide range of disciplines, such as Gradient descent, Autoencoder and Minification. His work deals with themes such as Motion, Convolutional neural network and Variation, which intersect with Image. His study focuses on the intersection of Deblurring and fields such as Prior probability with connections in the field of Mathematical optimization.
His primary areas of study are Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Feature learning. His study in Image, Deep learning, Face, Motion and Ground truth are all subfields of Artificial intelligence. His work on Pixel as part of general Computer vision research is frequently linked to Process, thereby connecting diverse disciplines of science.
His research in Artificial neural network intersects with topics in Amplitude, Image sensor, Boosting and Frame rate. His Pattern recognition study frequently draws parallels with other fields, such as Similarity. The study incorporates disciplines such as Rigid transformation, 3D pose estimation, Pose and Supervised learning in addition to Feature learning.
His primary scientific interests are in Artificial intelligence, Computer vision, Deep learning, Generative model and Pattern recognition. Paolo Favaro has included themes like Generator and Receiver operating characteristic in his Artificial intelligence study. The concepts of his Computer vision study are interwoven with issues in Representation, Encoder, Autoencoder and Feature vector.
The Generative model study combines topics in areas such as Triangle mesh, Differentiable function, Texture mapping and Ground truth. His Pattern recognition study combines topics in areas such as Visualization, Interpretability and Modality. His Artificial neural network research is multidisciplinary, incorporating elements of Motion estimation, Image sensor, Slow motion and Interpolation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi;Paolo Favaro.
european conference on computer vision (2016)
Structure from motion causally integrated over time
A. Chiuso;P. Favaro;Hailin Jin;S. Soatto.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution
T. E. Bishop;P. Favaro.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Low rank subspace clustering (LRSC)
René Vidal;Paolo Favaro.
Pattern Recognition Letters (2014)
A geometric approach to shape from defocus
P. Favaro;S. Soatto.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
A closed form solution to robust subspace estimation and clustering
Paolo Favaro;Rene Vidal;Avinash Ravichandran.
computer vision and pattern recognition (2011)
Light field superresolution
Tom E. Bishop;Sara Zanetti;Paolo Favaro.
international conference on computational photography (2009)
Real-time feature tracking and outlier rejection with changes in illumination
Hailin Jin;P. Favaro;S. Soatto.
international conference on computer vision (2001)
Total Variation Blind Deconvolution: The Devil Is in the Details
Daniele Perrone;Paolo Favaro.
computer vision and pattern recognition (2014)
Dynamic Texture Segmentation
Gianfranco Doretto;Daniel Cremers;Paolo Favaro;Stefano Soatto.
international conference on computer vision (2003)
Profile was last updated on December 6th, 2021.
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