What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Human–computer interaction.
His work blends Artificial intelligence and Computer programming studies together.
His Pattern recognition research is multidisciplinary, incorporating elements of Pixel, Linear combination and Data point.
Many of his research projects under Machine learning are closely connected to Parametric model with Parametric model, tying the diverse disciplines of science together.
The Human–computer interaction study which covers Language translation that intersects with Gesture and Gesture recognition.
The various areas that Jie Yang examines in his Linear discriminant analysis study include Optical character recognition, Affine transformation, Clustering high-dimensional data and Kernel Fisher discriminant analysis, Facial recognition system.
His most cited work include:
- A direct LDA algorithm for high-dimensional data — with application to face recognition (1371 citations)
- A real-time face tracker (565 citations)
- Skin-Color Modeling and Adaptation (369 citations)
What are the main themes of his work throughout his whole career to date?
Jie Yang mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Human–computer interaction.
His Gaze, Feature, Facial recognition system, Object detection and Discriminative model investigations are all subjects of Artificial intelligence research.
While the research belongs to areas of Pattern recognition, he spends his time largely on the problem of Categorization, intersecting his research to questions surrounding Cognitive neuroscience of visual object recognition.
His Machine learning research includes themes of Training set and Hidden Markov model.
The study incorporates disciplines such as Handwriting recognition, Focus and Handwriting in addition to Human–computer interaction.
His Tracking system study combines topics from a wide range of disciplines, such as Panorama and Frame grabber.
He most often published in these fields:
- Artificial intelligence (69.29%)
- Computer vision (40.16%)
- Pattern recognition (22.83%)
What were the highlights of his more recent work (between 2006-2013)?
- Artificial intelligence (69.29%)
- Pattern recognition (22.83%)
- Computer vision (40.16%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Categorization.
Artificial intelligence is represented through his Object detection, Cognitive neuroscience of visual object recognition, Segmentation, Object and Object model research.
His Discriminative model, Similarity measure and Discriminant study in the realm of Pattern recognition interacts with subjects such as Graph.
His work investigates the relationship between Computer vision and topics such as Classifier that intersect with problems in Color histogram and Histogram.
His Machine learning research incorporates elements of Frame, Facial recognition system, Face detection, Generative grammar and Saliency map.
His Facial recognition system research incorporates themes from Training set, Linear discriminant analysis, Dimensionality reduction and Biometrics.
Between 2006 and 2013, his most popular works were:
- PFID: Pittsburgh fast-food image dataset (164 citations)
- Sparsity induced similarity measure for label propagation (150 citations)
- Tools for protecting the privacy of specific individuals in video (64 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algebra
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Calorie and Food intake.
In his works, Jie Yang conducts interdisciplinary research on Artificial intelligence and Graph.
His work in the fields of Pattern recognition, such as Similarity measure, Classifier and Image segmentation, overlaps with other areas such as Sampling bias.
His study in Similarity measure is interdisciplinary in nature, drawing from both Object model, Linear combination, Data point, Sparse approximation and Euclidean distance.
His research in Classifier intersects with topics in Histogram and Object detection.
Jie Yang has researched Computer vision in several fields, including Machine learning and Pattern recognition.
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