His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, RGB color model and Cognitive neuroscience of visual object recognition. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Multivariate statistics. In Pattern recognition, Liefeng Bo works on issues like Contextual image classification, which are connected to Iterative reconstruction.
Liefeng Bo has included themes like Kernel and Feature vector in his Computer vision study. His RGB color model research focuses on subjects like Scene labeling, which are linked to Point cloud. His Cognitive neuroscience of visual object recognition research is multidisciplinary, incorporating elements of Visualization and Feature.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Support vector machine and Machine learning. His Cognitive neuroscience of visual object recognition, Feature extraction, Image, RGB color model and Object investigations are all subjects of Artificial intelligence research. His work deals with themes such as Contextual image classification, Kernel and Kernel, which intersect with Pattern recognition.
In his study, which falls under the umbrella issue of Computer vision, Coherence, Point cloud and Segmentation is strongly linked to Code. His studies deal with areas such as Algorithm, Mathematical optimization and Selection as well as Support vector machine. Many of his research projects under Machine learning are closely connected to Scalability with Scalability, tying the diverse disciplines of science together.
Liefeng Bo focuses on Artificial intelligence, Computer vision, Process, Image and Data mining. His Artificial intelligence study is mostly concerned with Object detection, Convolutional neural network, Feature extraction, Language model and Artificial neural network. His work on Feature as part of general Computer vision research is often related to Drone, thus linking different fields of science.
His Image study combines topics from a wide range of disciplines, such as Dimension and Depth mapping. His studies in Data mining integrate themes in fields like Confidence value and Sensor fusion. Discriminative model is a subfield of Pattern recognition that he tackles.
His primary scientific interests are in Artificial intelligence, Computer vision, Drone, Object detection and Process. His study deals with a combination of Artificial intelligence and Scratch. His Drone investigation overlaps with other areas such as Video tracking, Field, Tracking, Visualization and Crowd counting.
His Object detection research includes themes of Machine learning and Image. His Process studies intersect with other subjects such as Feature, Segmentation, Code, Object and Coherence. His research integrates issues of Crowds, Perspective and Discriminative model in his study of Feature.
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.
A large-scale hierarchical multi-view RGB-D object dataset
Kevin Lai;Liefeng Bo;Xiaofeng Ren;Dieter Fox.
international conference on robotics and automation (2011)
RGB-(D) scene labeling: Features and algorithms
Xiaofeng Ren;Liefeng Bo;Dieter Fox.
computer vision and pattern recognition (2012)
Multiobjective immune algorithm with nondominated neighbor-based selection
Maoguo Gong;Licheng Jiao;Haifeng Du;Liefeng Bo.
Evolutionary Computation (2008)
Unsupervised Feature Learning for RGB-D Based Object Recognition
Liefeng Bo;Xiaofeng Ren;Dieter Fox.
international symposium on experimental robotics (2013)
Kernel Descriptors for Visual Recognition
Liefeng Bo;Xiaofeng Ren;Dieter Fox.
neural information processing systems (2010)
Depth kernel descriptors for object recognition
Liefeng Bo;Xiaofeng Ren;Dieter Fox.
intelligent robots and systems (2011)
Efficient Match Kernel between Sets of Features for Visual Recognition
Liefeng Bo;Cristian Sminchisescu.
neural information processing systems (2009)
Unsupervised feature learning for 3D scene labeling
Kevin Lai;Liefeng Bo;Dieter Fox.
international conference on robotics and automation (2014)
Object recognition with hierarchical kernel descriptors
Liefeng Bo;Kevin Lai;Xiaofeng Ren;Dieter Fox.
computer vision and pattern recognition (2011)
Twin Gaussian Processes for Structured Prediction
Liefeng Bo;Cristian Sminchisescu.
International Journal of Computer Vision (2010)
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.
University of Washington
Xidian University
Alibaba Group (China)
General Electric (United States)
Google (United States)
University of Washington
Tianjin University
Xidian University
Chinese Academy of Sciences
Tianjin University
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: