What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Qinghua Huang mainly investigates Artificial intelligence, Computer vision, Ultrasound, Segmentation and Image segmentation.
His study brings together the fields of Pattern recognition and Artificial intelligence.
In Computer vision, Qinghua Huang works on issues like 3D ultrasound, which are connected to Interpolation, Algorithm, Data set and Visualization.
His Ultrasound study integrates concerns from other disciplines, such as Isometric exercise, Contraction, Muscle force, Anatomy and Biomedical engineering.
Qinghua Huang has included themes like RGB color model, Computer-aided diagnosis, Radiology and Speckle noise in his Segmentation study.
The study incorporates disciplines such as Cancer, Breast cancer, Pixel, Imaging phantom and Voxel in addition to Speckle pattern.
His most cited work include:
- An optical coherence tomography (OCT)-based air jet indentation system for measuring the mechanical properties of soft tissues. (296 citations)
- Sonomyography: Monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis (108 citations)
- A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition (102 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Ultrasound and Biomedical engineering.
His study in Imaging phantom extends to Artificial intelligence with its themes.
His Computer vision study combines topics in areas such as 3D ultrasound and Interpolation.
Qinghua Huang combines subjects such as Artificial neural network, Cluster analysis and Fuzzy logic with his study of Pattern recognition.
His work carried out in the field of Ultrasound brings together such families of science as Cartilage, Anatomy, Ultrasonography, Robotic arm and Ultrasonic sensor.
His studies deal with areas such as Elasticity, Indentation, Transducer and Soft tissue as well as Biomedical engineering.
He most often published in these fields:
- Artificial intelligence (81.48%)
- Computer vision (37.04%)
- Pattern recognition (40.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (81.48%)
- Pattern recognition (40.12%)
- Feature extraction (16.67%)
In recent papers he was focusing on the following fields of study:
Qinghua Huang mostly deals with Artificial intelligence, Pattern recognition, Feature extraction, Biclustering and Computer vision.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Imaging phantom.
He is studying Segmentation, which is a component of Pattern recognition.
His work on Image segmentation as part of general Segmentation study is frequently linked to Context, bridging the gap between disciplines.
Qinghua Huang has researched Feature extraction in several fields, including Pixel, Representation, Skeleton, Convolutional neural network and AdaBoost.
Qinghua Huang interconnects Scoliosis, Robotic arm, Ultrasound and Vertebra in the investigation of issues within Computer vision.
Between 2018 and 2021, his most popular works were:
- Robotic Arm Based Automatic Ultrasound Scanning for Three-Dimensional Imaging (36 citations)
- On Combining Biclustering Mining and AdaBoost for Breast Tumor Classification (32 citations)
- Segmentation of breast ultrasound image with semantic classification of superpixels. (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Qinghua Huang mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Feature extraction and Image segmentation.
His Artificial intelligence research incorporates elements of Three dimensional imaging and Ultrasound.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Region of interest, Histogram equalization, Skeleton, Robustness and Mean-shift.
The concepts of his Segmentation study are interwoven with issues in Convolution, Fuzzy logic and Bilateral filter.
His work deals with themes such as Orientation, Focus and Convolutional neural network, which intersect with Feature extraction.
His Image segmentation research integrates issues from Artificial neural network, Image quality, Feature and Encoding.
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