Bryan C. Russell mainly investigates Artificial intelligence, Pattern recognition, Object, Image segmentation and Cognitive neuroscience of visual object recognition. Bryan C. Russell combines subjects such as Computer vision and Natural language processing with his study of Artificial intelligence. His specific area of interest is Pattern recognition, where Bryan C. Russell studies Segmentation.
His study looks at the relationship between Object and topics such as Probabilistic latent semantic analysis, which overlap with Contextual image classification. His Cognitive neuroscience of visual object recognition research integrates issues from Object detection and LabelMe. The study incorporates disciplines such as Data mining, WordNet, Information retrieval, Automatic image annotation and Supervised learning in addition to Object detection.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Object and Image. In his study, Face and Projection is strongly linked to Polygon mesh, which falls under the umbrella field of Artificial intelligence. His work on Pixel, Object detection and 3D single-object recognition as part of general Computer vision research is frequently linked to Process, thereby connecting diverse disciplines of science.
His research investigates the connection between Pattern recognition and topics such as Margin that intersect with problems in Shape matching, Base, Pooling and Training set. His Object study integrates concerns from other disciplines, such as Artificial neural network, LabelMe, Database and Image retrieval. His study looks at the relationship between Image and fields such as Surface, as well as how they intersect with chemical problems.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, RGB color model, Object and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Information retrieval. In general Computer vision study, his work on Image, Background image and Vanishing point often relates to the realm of Process, thereby connecting several areas of interest.
His Image research includes themes of Surface and Task. His Object research is multidisciplinary, incorporating perspectives in Artificial neural network and Field. His work in the fields of Pattern recognition, such as Segmentation, intersects with other areas such as Deformation.
His primary areas of investigation include Artificial intelligence, Pattern recognition, RGB color model, Deformation and Computer vision. Artificial intelligence is often connected to Speech recognition in his work. His Pattern recognition research is multidisciplinary, incorporating elements of Object, Generalization, Benchmark and Sample.
His RGB color model research includes elements of 3D reconstruction, Polygon mesh, Face and Piecewise. Shape matching, Margin, Training set, SIGNAL and Segmentation are fields of study that intersect with his Deformation study. When carried out as part of a general Computer vision research project, his work on Image is frequently linked to work in Collision, therefore connecting diverse disciplines of study.
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.
LabelMe: A Database and Web-Based Tool for Image Annotation
Bryan C. Russell;Antonio Torralba;Kevin P. Murphy;William T. Freeman.
International Journal of Computer Vision (2008)
Discovering objects and their location in images
J. Sivic;B.C. Russell;A.A. Efros;A. Zisserman.
international conference on computer vision (2005)
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
B.C. Russell;W.T. Freeman;A.A. Efros;J. Sivic.
computer vision and pattern recognition (2006)
Discovering object categories in image collections
Josef Sivic;Bryan C. Russell;Alexei A. Efros;Andrew Zisserman.
(2005)
A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell.
computer vision and pattern recognition (2018)
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models
Mathieu Aubry;Daniel Maturana;Alexei A. Efros;Alexei A. Efros;Bryan C. Russell.
computer vision and pattern recognition (2014)
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell.
computer vision and pattern recognition (2018)
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification
Rohit Girdhar;Rohit Girdhar;Deva Ramanan;Abhinav Gupta;Josef Sivic.
computer vision and pattern recognition (2017)
Localizing Moments in Video with Natural Language
Lisa Anne Hendricks;Lisa Anne Hendricks;Oliver Wang;Eli Shechtman;Josef Sivic.
international conference on computer vision (2017)
BodyNet: Volumetric Inference of 3D Human Body Shapes
Gül Varol;Duygu Ceylan;Bryan C. Russell;Jimei Yang.
european conference on computer vision (2018)
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