His primary areas of investigation include Cut, Artificial intelligence, Graph cuts in computer vision, Algorithm and Image segmentation. He performs multidisciplinary study in the fields of Cut and Energy minimization via his papers. Yuri Boykov combines subjects such as Graph theory and Computer vision with his study of Artificial intelligence.
His Graph cuts in computer vision research is multidisciplinary, incorporating perspectives in Hypersurface, Maximum flow problem and Discrete mathematics. His study in Algorithm is interdisciplinary in nature, drawing from both Randomized algorithms as zero-sum games and Graph. His Minimum cut research focuses on subjects like Computational complexity theory, which are linked to Simulated annealing, Standard algorithms, Approximation algorithm and Probabilistic analysis of algorithms.
Yuri Boykov spends much of his time researching Artificial intelligence, Segmentation, Algorithm, Cut and Image segmentation. His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition. His work carried out in the field of Segmentation brings together such families of science as Object, Normalization, Deep learning and Prior probability.
His Algorithm study integrates concerns from other disciplines, such as Vector field, Cluster analysis and Markov model. Yuri Boykov works on Cut which deals in particular with Graph cuts in computer vision. In his research, Pairwise comparison and Maxima and minima is intimately related to Mathematical optimization, which falls under the overarching field of Image segmentation.
The scientist’s investigation covers issues in Artificial intelligence, Segmentation, Algorithm, Deep learning and Pattern recognition. Artificial intelligence is represented through his Artificial neural network, Leverage, Entropy, Normalization and Kernel research. Yuri Boykov studies Segmentation, namely Image segmentation.
The study incorporates disciplines such as Augmented reality, Machine learning and Image compression in addition to Image segmentation. His research integrates issues of Geodesic and Pairwise comparison in his study of Algorithm. His work deals with themes such as Vector field, Curvature, Spectral clustering and Kernel clustering, which intersect with Regularization.
His main research concerns Image segmentation, Segmentation, Deep learning, Artificial intelligence and Machine learning. His research combines Algorithm and Artificial intelligence. The concepts of his Algorithm study are interwoven with issues in Leverage, Pixel, Invariant, Supervised learning and Differentiable function.
His biological study spans a wide range of topics, including Augmented reality and Image compression. His Pyramid study combines topics from a wide range of disciplines, such as Image processing, Range and Pyramid. Key and Image are two areas of study in which Yuri Boykov engages in interdisciplinary work.
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.
Fast approximate energy minimization via graph cuts
Y. Boykov;O. Veksler;R. Zabih.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Y.Y. Boykov;M.-P. Jolly.
international conference on computer vision (2001)
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Y. Boykov;V. Kolmogorov.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Graph Cuts and Efficient N-D Image Segmentation
Yuri Boykov;Gareth Funka-Lea.
International Journal of Computer Vision (2006)
Markov random fields with efficient approximations
Y. Boykov;O. Veksler;R. Zabih.
computer vision and pattern recognition (1998)
Fast approximate energy minimization with label costs
Andrew Delong;Anton Osokin;Hossam N. Isack;Yuri Boykov.
computer vision and pattern recognition (2010)
Superpixels and supervoxels in an energy optimization framework
Olga Veksler;Yuri Boykov;Paria Mehrani.
european conference on computer vision (2010)
Interactive Organ Segmentation Using Graph Cuts
Yuri Boykov;Marie-Pierre Jolly.
medical image computing and computer assisted intervention (2000)
Energy-Based Geometric Multi-model Fitting
Hossam Isack;Yuri Boykov.
International Journal of Computer Vision (2012)
Graph Cuts in Vision and Graphics: Theories and Applications
Yuri Boykov;Olga Veksler.
Handbook of Mathematical Models in Computer Vision (2006)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: