What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image, Computer graphics and Algorithm.
The Artificial intelligence study combines topics in areas such as Structure and Scene statistics.
In most of his Computer vision studies, his work intersects topics such as Sketch.
As part of one scientific family, Shi-Min Hu deals mainly with the area of Image, narrowing it down to issues related to the Simple, and often The Internet.
His work in Feature extraction tackles topics such as Object detection which are related to areas like Minimum bounding box, Convolutional neural network, Saliency map and Histogram.
His Segmentation study combines topics from a wide range of disciplines, such as Cognitive neuroscience of visual object recognition, Polygon mesh and Image retrieval.
His most cited work include:
- Global contrast based salient region detection (2666 citations)
- Sketch2Photo: internet image montage (534 citations)
- SalientShape: group saliency in image collections (221 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Algorithm, Image and Computer graphics.
Artificial intelligence is often connected to Pattern recognition in his work.
His Computer vision study typically links adjacent topics like Visualization.
His biological study spans a wide range of topics, including Computer Aided Design, Polygon mesh and Morphing.
Polygon mesh and T-vertices are frequently intertwined in his study.
His Segmentation study incorporates themes from Point cloud and Benchmark.
He most often published in these fields:
- Artificial intelligence (46.52%)
- Computer vision (37.34%)
- Algorithm (14.87%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.52%)
- Computer vision (37.34%)
- Segmentation (8.54%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Segmentation, Point cloud and Pattern recognition.
His Computer vision research includes themes of Frame and Cluster analysis.
His Segmentation study integrates concerns from other disciplines, such as Data mining and Benchmark.
Shi-Min Hu combines subjects such as Salient, Skeleton, Object detection and Gesture with his study of Benchmark.
His Point cloud study deals with Point intersecting with Depth map, Line, Image segmentation and Convolution.
His study looks at the relationship between Pattern recognition and topics such as Feature, which overlap with Position, Contextual image classification, Normalization and Dependency.
Between 2018 and 2021, his most popular works were:
- Example-Guided Style-Consistent Image Synthesis From Semantic Labeling (39 citations)
- Morphing and Sampling Network for Dense Point Cloud Completion (35 citations)
- Combined adjuvant effect of ginseng stem-leaf saponins and selenium on immune responses to a live bivalent vaccine of Newcastle disease virus and infectious bronchitis virus in chickens. (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
Shi-Min Hu mainly investigates Artificial intelligence, Deep learning, Segmentation, Computer vision and Point cloud.
A large part of his Artificial intelligence studies is devoted to Image.
His Deep learning research incorporates elements of Theoretical computer science, Face, Graph and Code.
His research in Segmentation intersects with topics in Data mining and Benchmark.
His study in the field of Feature extraction and Smoothing is also linked to topics like Trajectory and Simultaneous localization and mapping.
Shi-Min Hu has researched Point cloud in several fields, including Function, Cognitive neuroscience of visual object recognition, Image segmentation and Morphing.
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