2019 - Fellow of the Royal Academy of Engineering (UK)
2012 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to robust computer vision
Philip H. S. Torr focuses on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Machine learning. His work in Image segmentation, Video tracking, Deep learning, Object detection and Artificial neural network is related to Artificial intelligence. His work deals with themes such as Robustness and Source code, which intersect with Computer vision.
His research on Segmentation also deals with topics like
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Machine learning. His Object, Image segmentation, Image, Artificial neural network and Pixel investigations are all subjects of Artificial intelligence research. His Computer vision study frequently draws connections between adjacent fields such as Robustness.
His Pattern recognition research is multidisciplinary, relying on both Cognitive neuroscience of visual object recognition and Feature. His Segmentation study incorporates themes from CRFS, Conditional random field, Pascal and Inference. Philip H. S. Torr has researched Machine learning in several fields, including Structure, Probabilistic logic and Generative grammar.
His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Segmentation. His study connects Computer vision and Artificial intelligence. His Machine learning research incorporates elements of Generative grammar and Training set.
His research integrates issues of Encoder, Pascal, Invariant and Pooling in his study of Pattern recognition. The concepts of his Artificial neural network study are interwoven with issues in Algorithm, Formal verification, Theoretical computer science, Pruning and Initialization. The various areas that Philip H. S. Torr examines in his Segmentation study include Object, Object detection, Minimum bounding box and Pixel.
Philip H. S. Torr mostly deals with Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Artificial neural network. His Artificial intelligence research incorporates themes from Machine learning and Source code. His Computer vision research includes elements of Set and Robustness.
His work in the fields of Segmentation, such as Image segmentation, overlaps with other areas such as Fusion. His Pattern recognition research includes themes of Pascal, Iterative reconstruction and Existential quantification. His research investigates the connection with Artificial neural network and areas like Algorithm which intersect with concerns in Differentiable function.
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.
Struck: Structured Output Tracking with Kernels
Sam Hare;Stuart Golodetz;Amir Saffari;Vibhav Vineet.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Philip H. S. Torr;Andrew Zisserman.
Computer Vision and Image Understanding (2000)
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng;Sadeep Jayasumana;Bernardino Romera-Paredes;Vibhav Vineet.
international conference on computer vision (2015)
Struck: Structured output tracking with kernels
Sam Hare;Amir Saffari;Philip H. S. Torr.
international conference on computer vision (2011)
Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto;Jack Valmadre;João F. Henriques;Andrea Vedaldi.
european conference on computer vision (2016)
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)
The Visual Object Tracking VOT2017 Challenge Results
Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg.
international conference on computer vision (2017)
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Ming-Ming Cheng;Ziming Zhang;Wen-Yan Lin;Philip Torr.
computer vision and pattern recognition (2014)
Staple: Complementary Learners for Real-Time Tracking
Luca Bertinetto;Jack Valmadre;Stuart Golodetz;Ondrej Miksik.
computer vision and pattern recognition (2016)
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
P. H. S. Torr;D. W. Murray.
International Journal of Computer Vision (1997)
Profile was last updated on December 6th, 2021.
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