D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 4,335 193 World Ranking 9352 National Ranking 939

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

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.

His most cited work include:

  • An interpretable mortality prediction model for COVID-19 patients (258 citations)
  • Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan (176 citations)
  • A fast and robust local descriptor for 3D point cloud registration (101 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (82.32%)
  • Computer vision (41.41%)
  • Pattern recognition (40.91%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (82.32%)
  • Pattern recognition (40.91%)
  • Computer vision (41.41%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • An interpretable mortality prediction model for COVID-19 patients (258 citations)
  • Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan (176 citations)
  • AIM 2020 Challenge on Rendering Realistic Bokeh (15 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

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.

Best Publications

An interpretable mortality prediction model for COVID-19 patients

Li Yan;Hai Tao Zhang;Jorge Goncalves;Yang Xiao.
Nature Machine Intelligence (2020)

723 Citations

A machine learning-based model for survival prediction in patients with severe COVID-19 infection

Yan L;Zhang H;Goncalves J;Xiao Y.
medRxiv (2020)

277 Citations

A fast and robust local descriptor for 3D point cloud registration

Jiaqi Yang;Zhiguo Cao;Qian Zhang.
Information Sciences (2016)

162 Citations

TasselNet: counting maize tassels in the wild via local counts regression network

Hao Lu;Zhiguo Cao;Yang Xiao;Bohan Zhuang.
Plant Methods (2017)

148 Citations

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)

131 Citations

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)

127 Citations

Monocular Relative Depth Perception with Web Stereo Data Supervision

Ke Xian;Chunhua Shen;Zhiguo Cao;Hao Lu.
computer vision and pattern recognition (2018)

111 Citations

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Haipeng Xiong;Hao Lu;Chengxin Liu;Liang Liu.
international conference on computer vision (2019)

101 Citations

TOLDI: An effective and robust approach for 3D local shape description

Jiaqi Yang;Qian Zhang;Yang Xiao;Zhiguo Cao.
Pattern Recognition (2017)

95 Citations

In-field automatic observation of wheat heading stage using computer vision

Yanjun Zhu;Zhiguo Cao;Hao Lu;Yanan Li.
Biosystems Engineering (2016)

80 Citations

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