What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Object detection and Object.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning.
His research in Pattern recognition intersects with topics in Contextual image classification and Feature.
His Object detection research incorporates themes from Anomaly detection, Support vector machine, Deep learning, Robustness and Hidden Markov model.
His work deals with themes such as Camera auto-calibration, Subspace topology and Brightness, which intersect with Object.
His study in the field of Motion detection is also linked to topics like Clutter.
His most cited work include:
- Object tracking: A survey (4207 citations)
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild (2918 citations)
- Shape-from-shading: a survey (1475 citations)
What are the main themes of his work throughout his whole career to date?
Mubarak Shah mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Object detection and Machine learning.
His study in Segmentation, Object, Motion, Feature extraction and Tracking is carried out as part of his studies in Artificial intelligence.
His work is connected to Pixel, Motion estimation, Video tracking, Image segmentation and Optical flow, as a part of Computer vision.
The Pattern recognition study combines topics in areas such as Contextual image classification, Image and Feature.
His study ties his expertise on Convolutional neural network together with the subject of Contextual image classification.
His Machine learning study frequently links to adjacent areas such as Task.
He most often published in these fields:
- Artificial intelligence (81.86%)
- Computer vision (53.05%)
- Pattern recognition (24.07%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (81.86%)
- Machine learning (13.05%)
- Pattern recognition (24.07%)
In recent papers he was focusing on the following fields of study:
Mubarak Shah mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Segmentation.
His Artificial intelligence study focuses mostly on Deep learning, Object detection, Convolutional neural network, Object and Contextual image classification.
His work on Unsupervised learning as part of general Machine learning study is frequently connected to Action recognition, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as Matching, Image, Feature and Electroencephalography in addition to Pattern recognition.
When carried out as part of a general Computer vision research project, his work on Motion, Video tracking, Tracking and Pixel is frequently linked to work in Time activity, therefore connecting diverse disciplines of study.
His work is dedicated to discovering how Segmentation, Routing are connected with Optical flow and other disciplines.
Between 2016 and 2021, his most popular works were:
- Real-World Anomaly Detection in Surveillance Videos (296 citations)
- Human Semantic Parsing for Person Re-identification (207 citations)
- Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (196 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Mubarak Shah focuses on Artificial intelligence, Pattern recognition, Computer vision, Object detection and Feature extraction.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
Mubarak Shah combines subjects such as Visualization, Minimum bounding box and Feature with his study of Pattern recognition.
His work on Object and Tracking as part of general Computer vision research is frequently linked to Temporal context, bridging the gap between disciplines.
The concepts of his Object study are interwoven with issues in Motion and Electroencephalography.
The various areas that Mubarak Shah examines in his Feature extraction study include Video tracking and Semantics.
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