What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
Ariel Shamir mainly focuses on Artificial intelligence, Computer vision, Seam carving, Retargeting and Polygon mesh.
His Artificial intelligence research includes elements of Optimization problem and Pattern recognition.
His research integrates issues of Arc, Spatial relation and Computation in his study of Computer vision.
His biological study spans a wide range of topics, including Multimedia and Image warping.
His Retargeting research includes themes of Video editing, Data mining, Feature and Cut.
His study in Polygon mesh is interdisciplinary in nature, drawing from both Animation, Segmentation, Maxima and minima, Computer graphics and Algorithm.
His most cited work include:
- Seam carving for content-aware image resizing (1413 citations)
- Improved seam carving for video retargeting (677 citations)
- Sketch2Photo: internet image montage (534 citations)
What are the main themes of his work throughout his whole career to date?
Ariel Shamir spends much of his time researching Artificial intelligence, Computer vision, Image, Computer graphics and Object.
Cluster analysis is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Artificial intelligence.
His Computer vision study incorporates themes from Structure and Polygon mesh.
The concepts of his Image study are interwoven with issues in Simple and Retargeting.
In Object, Ariel Shamir works on issues like Context, which are connected to Human–computer interaction and Data mining.
His Image warping study integrates concerns from other disciplines, such as Seam carving, Binary image, Feature detection and Image texture.
He most often published in these fields:
- Artificial intelligence (56.41%)
- Computer vision (41.03%)
- Image (16.67%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (56.41%)
- Computer vision (41.03%)
- Artificial neural network (7.69%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Artificial neural network, Object and Image.
When carried out as part of a general Artificial intelligence research project, his work on Image processing is frequently linked to work in Non-photorealistic rendering, therefore connecting diverse disciplines of study.
Within one scientific family, he focuses on topics pertaining to Frame under Computer vision, and may sometimes address concerns connected to Representation and Structure.
The study incorporates disciplines such as Encoding, Motion analysis, Feature vector, Translation and Convolutional neural network in addition to Artificial neural network.
The Object study combines topics in areas such as Context, Design tool, Structural robustness and Complement.
Many of his research projects under Image are closely connected to Key with Key, tying the diverse disciplines of science together.
Between 2015 and 2021, his most popular works were:
- GRAINS: Generative Recursive Autoencoders for INdoor Scenes (71 citations)
- Inverse Kinematics Techniques in Computer Graphics: A Survey (58 citations)
- Learning how objects function via co-analysis of interactions (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Ariel Shamir mainly focuses on Artificial intelligence, Computer vision, Machine learning, Context and Artificial neural network.
His research in the fields of Motion capture, Image and Method overlaps with other disciplines such as Snapshot and Spatial configuration.
His work on Image processing as part of general Image study is frequently linked to Key and Benchmarking, therefore connecting diverse disciplines of science.
His Computer vision research incorporates themes from Portrait and Real-time computing.
His work investigates the relationship between Context and topics such as Object that intersect with problems in Series and Human–computer interaction.
His work deals with themes such as Frame, Image warping, Encoding and Pattern recognition, which intersect with Artificial neural network.
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