What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Allen R. Hanson mainly focuses on Artificial intelligence, Computer vision, Human–computer interaction, Pattern recognition and Image processing.
As part of his studies on Artificial intelligence, he often connects relevant subjects like Pattern recognition.
His Computer vision research is multidisciplinary, incorporating perspectives in Surface and Mobile robot.
His Human–computer interaction research includes elements of Mobile robot navigation, Robotics and Navigation system.
His Pattern recognition study integrates concerns from other disciplines, such as Image, Information retrieval and Blackboard.
His work deals with themes such as Pixel, Geometry and Data set, which intersect with Image processing.
His most cited work include:
- Computer Vision Systems (734 citations)
- Extracting Straight Lines (660 citations)
- The Schema System (201 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, Image and Mobile robot.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pixel, Image processing, Segmentation, Object and Feature.
His work is dedicated to discovering how Computer vision, Robot are connected with Landmark and other disciplines.
His Pattern recognition research incorporates themes from Matching, Orientation and Principle of maximum entropy.
His Image research includes themes of Representation, Computer architecture and Theoretical computer science.
His Mobile robot study incorporates themes from Calibration, Robotics and Human–computer interaction.
He most often published in these fields:
- Artificial intelligence (75.97%)
- Computer vision (48.70%)
- Pattern recognition (13.64%)
What were the highlights of his more recent work (between 2006-2015)?
- Artificial intelligence (75.97%)
- Computer vision (48.70%)
- Pixel (9.09%)
In recent papers he was focusing on the following fields of study:
Allen R. Hanson spends much of his time researching Artificial intelligence, Computer vision, Pixel, Presentation and Whiteboard.
His Artificial intelligence research is multidisciplinary, relying on both Speech recognition and Pattern recognition.
His Computer vision study combines topics in areas such as Discrete mathematics and Computer graphics.
His studies examine the connections between Presentation and genetics, as well as such issues in Multimedia, with regards to Software deployment and Usability.
Allen R. Hanson has researched Whiteboard in several fields, including Image processing, Content creation, Thumbnail and Modality.
Orientation, Translation and Motion is closely connected to Image plane in his research, which is encompassed under the umbrella topic of Segmentation.
Between 2006 and 2015, his most popular works were:
- Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation (155 citations)
- Aging in place: fall detection and localization in a distributed smart camera network (90 citations)
- Coherent Motion Segmentation in Moving Camera Videos Using Optical Flow Orientations (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Speech recognition, Lexicon and Text mining.
Allen R. Hanson studied Artificial intelligence and Pattern recognition that intersect with Local ternary patterns.
His work on Object detection, Pixel and Image sensor as part of general Computer vision research is often related to Visual sensor network, thus linking different fields of science.
As a part of the same scientific family, he mostly works in the field of Speech recognition, focusing on Natural language processing and, on occasion, Pruning, Similitude and Image retrieval.
His studies in Pattern recognition integrate themes in fields like Language model, Optical character recognition, Conditional random field, Image processing and Graphical model.
His Image segmentation research incorporates elements of Feature extraction and Text segmentation.
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