What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Daniel P. Huttenlocher spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Structure and Social network.
His work deals with themes such as Machine learning and Hausdorff dimension, which intersect with Artificial intelligence.
His work on Image, Feature, Structure from motion and Markov random field as part of general Computer vision study is frequently linked to Continuous optimization, bridging the gap between disciplines.
His studies deal with areas such as Image processing and Hausdorff distance as well as Pattern recognition.
The concepts of his Social network study are interwoven with issues in Social relation, Social psychology, Social psychology and Cognitive psychology.
His Image texture research is multidisciplinary, incorporating elements of Scale-space segmentation and Segmentation-based object categorization.
His most cited work include:
- Efficient Graph-Based Image Segmentation (4870 citations)
- Comparing images using the Hausdorff distance (3150 citations)
- Pictorial Structures for Object Recognition (2033 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 Algorithm.
His study explores the link between Artificial intelligence and topics such as Natural language processing that cross with problems in Speech recognition.
His work on Image texture as part of general Pattern recognition study is frequently connected to Maximum a posteriori estimation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
While the research belongs to areas of Image, he spends his time largely on the problem of Information retrieval, intersecting his research to questions surrounding Code and Character.
His Algorithm research incorporates elements of Affine arithmetic, Affine shape adaptation, Affine combination, Geometric hashing and Topology.
His work carried out in the field of Object brings together such families of science as Transformation, Motion and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (56.73%)
- Computer vision (29.82%)
- Pattern recognition (23.98%)
What were the highlights of his more recent work (between 2008-2016)?
- Artificial intelligence (56.73%)
- Computer vision (29.82%)
- Social media (4.68%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Social media, Data science and Social network are his primary areas of study.
In his works, Daniel P. Huttenlocher performs multidisciplinary study on Artificial intelligence and Global Positioning System.
In his study, which falls under the umbrella issue of Computer vision, Orientation, Kernel and Image restoration is strongly linked to Computer graphics.
His Data science study combines topics in areas such as Scale and Presentation.
His Social network study also includes fields such as
- Social psychology that connect with fields like Social computing,
- Social psychology and related Perspective, Cognitive psychology and Structure.
Daniel P. Huttenlocher studied Feature and Pattern recognition that intersect with Image retrieval, Automatic image annotation, Visual Word and Image registration.
Between 2008 and 2016, his most popular works were:
- Predicting positive and negative links in online social networks (1043 citations)
- Signed networks in social media (822 citations)
- Mapping the world's photos (733 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Social media, Artificial intelligence, Structure, Data science and Social psychology.
His Social media research includes themes of Key and Human–computer interaction.
His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Pattern recognition.
The study incorporates disciplines such as Geotagging, Information retrieval and Geolocation in addition to Structure.
His Data science study combines topics in areas such as Question answering, World Wide Web and Product.
In his work, Social relation, Friendship and Variety is strongly intertwined with Social network, which is a subfield of Social psychology.
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