What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Harpreet Sawhney mainly investigates Artificial intelligence, Computer vision, Computer graphics, Image and Visualization.
He usually deals with Artificial intelligence and limits it to topics linked to Pattern recognition and NIST.
His work in the fields of Computer vision, such as Image processing, Video tracking and Image registration, overlaps with other areas such as Mosaic.
His study in Computer graphics is interdisciplinary in nature, drawing from both Variety, Conceptual model and Engineering drawing.
His Image research is multidisciplinary, relying on both Motion and Virtual image.
His Visualization study combines topics from a wide range of disciplines, such as Orientation and Multimedia.
His most cited work include:
- Query by image and video content: the QBIC system (1870 citations)
- Query by image and video content: the QBIC system (1594 citations)
- Efficient color histogram indexing for quadratic form distance functions (759 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Computer graphics and Image.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Object, Motion estimation, Object detection, Feature extraction and Visualization.
His work in Video tracking, Pixel, Tracking, Image processing and Parallax are all subfields of Computer vision research.
His work in Pattern recognition addresses subjects such as Histogram, which are connected to disciplines such as Cluster analysis.
In his research on the topic of Computer graphics, Video compression picture types is strongly related with Video capture.
His Image research includes themes of Algorithm and Representation.
He most often published in these fields:
- Artificial intelligence (83.18%)
- Computer vision (70.09%)
- Pattern recognition (21.96%)
What were the highlights of his more recent work (between 2013-2019)?
- Artificial intelligence (83.18%)
- Computer vision (70.09%)
- Machine learning (6.07%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Machine learning, Occlusion and Computing systems.
As part of the same scientific family, he usually focuses on Artificial intelligence, concentrating on Pattern recognition and intersecting with Generative grammar.
His work on Markov random field, Feature detection and Feature is typically connected to Facade and Baseline as part of general Computer vision study, connecting several disciplines of science.
His Machine learning research incorporates themes from Hidden Markov model, Generative model and Multimodal data.
The study incorporates disciplines such as Video tracking, Tracking system and Computer graphics in addition to Object detection.
His study in the field of Human visual system model also crosses realms of Input device, Intermediate language and Set.
Between 2013 and 2019, his most popular works were:
- Real-time system for multi-modal 3d geospatial mapping, object recognition, scene annotation and analytics (159 citations)
- Identifying, describing, and sharing salient events in images and videos (110 citations)
- Real-time object detection, tracking and occlusion reasoning (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Harpreet Sawhney spends much of his time researching Artificial intelligence, Computer vision, Computing systems, Feature detection and Human–machine system.
Many of his studies on Artificial intelligence apply to Computer graphics as well.
In the field of Computer vision, his study on Tracking system, Tracking and Object detection overlaps with subjects such as Occlusion and Track.
Many of his Computing systems research pursuits overlap with Natural language, Semantic computing, Visual presentation, Information retrieval and Salient.
His Feature detection research integrates issues from Feature, Realization, Cluster analysis and Human visual system model.
His Human–machine system research is multidisciplinary, incorporating elements of Augmented reality and Big data.
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