What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Pixel and Quality.
His study in Image, Stereopsis, Matching, Bilateral filter and Histogram is carried out as part of his studies in Artificial intelligence.
His Computer vision study often links to related topics such as Benchmark.
His studies in Pattern recognition integrate themes in fields like Object, Object detection, Distance transform and Representation.
His study on Pixel is mostly dedicated to connecting different topics, such as Algorithm.
His Image resolution research is multidisciplinary, incorporating perspectives in Feature and Superresolution.
His most cited work include:
- Spatial-Depth Super Resolution for Range Images (669 citations)
- Detailed Real-Time Urban 3D Reconstruction from Video (650 citations)
- Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling (482 citations)
What are the main themes of his work throughout his whole career to date?
Qingxiong Yang spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Pixel and Bilateral filter.
As a member of one scientific family, Qingxiong Yang mostly works in the field of Artificial intelligence, focusing on Filter and, on occasion, Low-pass filter.
His research in the fields of Image resolution, Face, Video tracking and Image processing overlaps with other disciplines such as Invariant.
Qingxiong Yang combines subjects such as Histogram, Noise reduction and Feature with his study of Pattern recognition.
His studies deal with areas such as Algorithm, Minimum spanning tree, Chromaticity and Feature vector as well as Pixel.
His research integrates issues of Kernel, Kernel adaptive filter, Median filter and Grayscale in his study of Bilateral filter.
He most often published in these fields:
- Artificial intelligence (89.02%)
- Computer vision (70.73%)
- Pattern recognition (30.49%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (89.02%)
- Computer vision (70.73%)
- Image (17.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Image, Face and Pattern recognition.
Artificial intelligence is a component of his Convolutional neural network, Pattern recognition, Segmentation, Robustness and Tracking studies.
His research brings together the fields of Joint and Computer vision.
The study incorporates disciplines such as Artificial neural network and Feature extraction in addition to Image.
His Face study integrates concerns from other disciplines, such as Markov random field and Filter.
Qingxiong Yang has included themes like Video tracking and Pixel in his Pattern recognition study.
Between 2016 and 2020, his most popular works were:
- RGBD Salient Object Detection via Deep Fusion (175 citations)
- Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy (47 citations)
- Learning to hallucinate face images via component generation and enhancement (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Image and Tracking.
His work on Pixel, Convolutional neural network and Discriminative model as part of general Artificial intelligence research is often related to Face hallucination and Component, thus linking different fields of science.
The various areas that Qingxiong Yang examines in his Pixel study include Optical flow, Minimum spanning tree, Mixture model and Robustness.
His Convolutional neural network research integrates issues from Object detection, Feature vector, Salience, Feature extraction and Saliency map.
Qingxiong Yang has researched Pattern recognition in several fields, including Spatial consistency, Artificial neural network and Spanning tree.
His work carried out in the field of Tracking brings together such families of science as Segmentation, Image segmentation, Stereo matching and Benchmark.
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