Zhiguo Cao mostly deals with Artificial intelligence, Computer vision, Feature, Pattern recognition and Segmentation. In most of his Artificial intelligence studies, his work intersects topics such as Machine learning. In general Computer vision, his work in Image processing is often linked to Field linking many areas of study.
His Feature research is multidisciplinary, incorporating perspectives in Point cloud, Noise and Feature detection. The study incorporates disciplines such as Pixel, Histogram, Random walk and Task in addition to Pattern recognition. In general Segmentation study, his work on Image segmentation often relates to the realm of Stage and Precision agriculture, thereby connecting several areas of interest.
Zhiguo Cao mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature and Image. His Artificial intelligence study frequently links to related topics such as Machine learning. The Pattern recognition study combines topics in areas such as Histogram, Cognitive neuroscience of visual object recognition and Robustness.
His Histogram research incorporates themes from Data mining and Thresholding. His research in Feature tackles topics such as Point cloud which are related to areas like Noise and Point. His Convolutional neural network research incorporates elements of Feature learning and Categorization.
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object. His study involves RGB color model, Pose, Discriminative model, Deep learning and Feature, a branch of Artificial intelligence. His Feature research focuses on Outlier and how it relates to Multilayer perceptron.
His work in the fields of Pattern recognition, such as Feature extraction, overlaps with other areas such as Operator. His Feature extraction research includes themes of Focus and Image. His Segmentation study combines topics in areas such as Encoder decoder, Aerial imagery, Multispectral image and Encoding.
His primary areas of study are Artificial intelligence, Object, Pattern recognition, Metric and RGB color model. Zhiguo Cao interconnects Point, Machine learning and Computer vision in the investigation of issues within Artificial intelligence. The Object study which covers Benchmark that intersects with Segmentation, Ranking, Sampling and Affine transformation.
His Pattern recognition study combines topics from a wide range of disciplines, such as Ground truth and Solid modeling. Zhiguo Cao has researched RGB color model in several fields, including Voxel and Feature learning. He has included themes like Leverage, Feature, Video tracking, Tracking and Robustness in his Point cloud 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.
An interpretable mortality prediction model for COVID-19 patients
Li Yan;Hai Tao Zhang;Jorge Goncalves;Yang Xiao.
Nature Machine Intelligence (2020)
A machine learning-based model for survival prediction in patients with severe COVID-19 infection
Yan L;Zhang H;Goncalves J;Xiao Y.
A fast and robust local descriptor for 3D point cloud registration
Jiaqi Yang;Zhiguo Cao;Qian Zhang.
Information Sciences (2016)
Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage
Zhenghong Yu;Zhiguo Cao;Xi Wu;Xiaodong Bai.
Agricultural and Forest Meteorology (2013)
TasselNet: counting maize tassels in the wild via local counts regression network
Hao Lu;Zhiguo Cao;Yang Xiao;Bohan Zhuang.
Plant Methods (2017)
Crop segmentation from images by morphology modeling in the CIE L*a*b* color space
X. D. Bai;Z. G. Cao;Y. Wang;Z. H. Yu.
Computers and Electronics in Agriculture (2013)
Monocular Relative Depth Perception with Web Stereo Data Supervision
Ke Xian;Chunhua Shen;Zhiguo Cao;Hao Lu.
computer vision and pattern recognition (2018)
TOLDI: An effective and robust approach for 3D local shape description
Jiaqi Yang;Qian Zhang;Yang Xiao;Zhiguo Cao.
Pattern Recognition (2017)
A real-time embedded architecture for SIFT
Sheng Zhong;Jianhui Wang;Luxin Yan;Lie Kang.
Journal of Systems Architecture (2013)
Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter
Biyin Zhang;Tianxu Zhang;Zhiguo Cao;Kun Zhang.
Optical Engineering (2007)
Profile was last updated on December 6th, 2021.
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