What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Contextual image classification are his primary areas of study.
David W. Jacobs has included themes like Algorithm and Linear subspace in his Artificial intelligence study.
His work deals with themes such as Representation and Curvature, which intersect with Computer vision.
His Pattern recognition research includes elements of Salient, Machine learning and Communication.
David W. Jacobs has researched Facial recognition system in several fields, including Expression and Discriminative model.
His study in Contextual image classification is interdisciplinary in nature, drawing from both Supervised learning and Prior probability.
His most cited work include:
- Lambertian reflectance and linear subspaces (1352 citations)
- A search engine for 3D models (940 citations)
- Shape Classification Using the Inner-Distance (859 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Representation.
His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Real image and Linear subspace.
His biological study spans a wide range of topics, including Affine transformation, Mathematical optimization, Convex optimization, Point and Search engine indexing.
His Facial recognition system study combines topics from a wide range of disciplines, such as Subspace topology and Similarity.
The concepts of his Photometric stereo study are interwoven with issues in Spherical harmonics and Rank.
He most often published in these fields:
- Artificial intelligence (72.68%)
- Computer vision (47.80%)
- Pattern recognition (24.39%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (72.68%)
- Computer vision (47.80%)
- Pattern recognition (24.39%)
In recent papers he was focusing on the following fields of study:
David W. Jacobs mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Machine learning.
All of his Artificial intelligence and Image, Real image, Object, Representation and Convolutional neural network investigations are sub-components of the entire Artificial intelligence study.
Many of his studies involve connections with topics such as Reflectivity and Computer vision.
His Pattern recognition research includes elements of Contextual image classification, Deep learning, Measure and Generative grammar.
His research in Artificial neural network intersects with topics in Adversarial system, Algorithm, Kernel and Robustness.
He has included themes like Focus, Invariant and Inference in his Machine learning study.
Between 2013 and 2021, his most popular works were:
- End-to-End Recovery of Human Shape and Pose (660 citations)
- Frontal to profile face verification in the wild (228 citations)
- Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds (159 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
David W. Jacobs focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Iterative reconstruction.
He undertakes multidisciplinary investigations into Artificial intelligence and Scale in his work.
His Computer vision study frequently links to related topics such as Polygon mesh.
His Polygon mesh study deals with Set intersecting with Sequence and Algorithm.
His work carried out in the field of Pattern recognition brings together such families of science as Object, Photometric stereo, Deep learning and Measure.
David W. Jacobs interconnects 3D reconstruction, Active shape model, Heat kernel signature, Topological skeleton and Surface reconstruction in the investigation of issues within Iterative reconstruction.
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