2023 - Research.com Computer Science in China Leader Award
Song-Chun Zhu mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Parsing and Graph. The concepts of his Artificial intelligence study are interwoven with issues in Algorithm and Machine learning, Markov chain. His Pattern recognition study incorporates themes from Contextual image classification, Parse tree, Edge detection and Cluster analysis.
In his research, Bayes' theorem and Minimum description length is intimately related to Region growing, which falls under the overarching field of Edge detection. His biological study deals with issues like Grammar, which deal with fields such as Rule-based machine translation, Set, Event and Pose. His Graph research includes elements of Context-sensitive grammar, Theoretical computer science, Training set, Facial recognition system and Graph.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Graph and Parsing. His research integrates issues of Machine learning and Natural language processing in his study of Artificial intelligence. His study in Pattern recognition is interdisciplinary in nature, drawing from both Cognitive neuroscience of visual object recognition and Statistical model.
His Computer vision research is multidisciplinary, incorporating elements of Sketch and Representation. The study incorporates disciplines such as Robot, Theoretical computer science and Graph in addition to Graph. His Parsing research is multidisciplinary, incorporating perspectives in Inference and Grammar.
His scientific interests lie mostly in Artificial intelligence, Markov chain Monte Carlo, Algorithm, Human–computer interaction and Parsing. His Artificial intelligence study combines topics from a wide range of disciplines, such as Pattern recognition, Computer vision and Natural language processing. His Computer vision research includes themes of Perspective and Representation.
Song-Chun Zhu has included themes like Sampling, Energy, Markov chain, Posterior probability and Generative model in his Markov chain Monte Carlo study. His Algorithm study combines topics in areas such as Latent variable, Noise, Maximum likelihood, Generator and Function. His biological study spans a wide range of topics, including Context and Grammar.
His primary scientific interests are in Artificial intelligence, Markov chain Monte Carlo, Algorithm, Inference and Energy. His work carried out in the field of Artificial intelligence brings together such families of science as Natural language processing, Graph, Computer vision and Pattern recognition. His work deals with themes such as Domain, Probability distribution and Image warping, which intersect with Pattern recognition.
His Markov chain Monte Carlo research is multidisciplinary, relying on both Machine learning and Generative model. His Algorithm research incorporates elements of Transformation, Image, Noise, Maximum likelihood and Function. His Inference study integrates concerns from other disciplines, such as Contrast, Field, Cognitive science, Divergence and Raven's Progressive Matrices.
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.
Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
Song Chun Zhu;A. Yuille.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1996)
Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation
S.C. Zhu;T.S. Lee;A.L. Yuille.
international conference on computer vision (1995)
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
Song Chun Zhu;Yingnian Wu;David Mumford.
International Journal of Computer Vision (1998)
Image segmentation by data-driven Markov chain Monte Carlo
Zhuowen Tu;Song-Chun Zhu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Image segmentation by data driven Markov chain Monte Carlo
Zhuowen Tu;Song-Chun Zhu;Heung-Yeung Shum.
international conference on computer vision (2001)
Image Parsing: Unifying Segmentation, Detection, and Recognition
Zhuowen Tu;Xiangrong Chen;Alan L. Yuille;Song Chun Zhu.
International Journal of Computer Vision (2005)
On Advances in Statistical Modeling of Natural Images
A. Srivastava;A. B. Lee;E. P. Simoncelli;S.-C. Zhu.
Journal of Mathematical Imaging and Vision (2003)
A Stochastic Grammar of Images
Song-Chun Zhu;David Mumford.
Minimax Entropy Principle and Its Application to Texture Modeling
Song Chun Zhu;Ying Nian Wu;David Mumford.
Neural Computation (1997)
Visual interpretability for deep learning: a survey
Quan-shi Zhang;Song-chun Zhu.
Journal of Zhejiang University Science C (2018)
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: