What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Shang-Hong Lai mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Object detection.
His research combines Artificial neural network and Computer vision.
His Principal component analysis, Image segmentation and Segmentation study in the realm of Pattern recognition interacts with subjects such as Scheme.
The various areas that he examines in his Algorithm study include Real image and Mathematical optimization.
His Image processing study incorporates themes from Algorithm design, Template matching, Graphics processing unit and Seam carving.
His most cited work include:
- Fusing generic objectness and visual saliency for salient object detection (325 citations)
- From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model (178 citations)
- Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI (139 citations)
What are the main themes of his work throughout his whole career to date?
Shang-Hong Lai focuses on Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Facial recognition system.
His work in Feature extraction, Feature, Image, Face and Motion estimation is related to Artificial intelligence.
His study in Pixel, Image processing, Segmentation, Optical flow and Face detection falls under the purview of Computer vision.
His research integrates issues of Real image, Template matching and Search algorithm in his study of Image processing.
His studies in Pattern recognition integrate themes in fields like Object-class detection, Facial expression and Robustness.
His Algorithm research includes themes of Linear system and Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (83.17%)
- Computer vision (66.99%)
- Pattern recognition (35.28%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (83.17%)
- Computer vision (66.99%)
- Pattern recognition (35.28%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Image and Deep learning.
Artificial intelligence and Detector are frequently intertwined in his study.
His study in Computer vision is interdisciplinary in nature, drawing from both Robustness and Solid modeling.
His Pattern recognition research integrates issues from RGB color model, Fingerprint, Fingerprint recognition and Fingerprint.
His Image study also includes fields such as
- Object and related Advanced driver assistance systems, Real-time computing and Energy,
- Natural language processing which connect with Subspace topology and Closed captioning.
His Deep learning research includes elements of Spatial analysis, Representation, Pose and Position.
Between 2014 and 2021, his most popular works were:
- AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation (85 citations)
- A Compact Deep Learning Model for Robust Facial Expression Recognition (60 citations)
- Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network (60 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Deep learning.
His study in Object detection, Discriminative model, Matching, Image quality and Convolutional neural network falls within the category of Artificial intelligence.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Artificial neural network, Face and Feature.
His work in the fields of Computer vision, such as Augmented reality, intersects with other areas such as Process.
The study incorporates disciplines such as Image segmentation, RANSAC, Image warping, Interpolation and CUDA in addition to Feature extraction.
His Deep learning study which covers Facial recognition system that intersects with Hidden Markov model and Overfitting.
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