What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary scientific interests are in Artificial intelligence, Shape analysis, Computer vision, Algorithm and Point cloud.
His research in Artificial intelligence intersects with topics in Surface reconstruction and Pattern recognition.
His Shape analysis research is multidisciplinary, incorporating perspectives in Geometric modeling, 3d shapes, Active shape model, 3d geometry and Machine learning.
His studies in Computer vision integrate themes in fields like Manifold, Process and Polygon.
His Algorithm research incorporates elements of Iterative closest point, Set, Outlier and Robustness.
As part of one scientific family, Niloy J. Mitra deals mainly with the area of Cognitive neuroscience of visual object recognition, narrowing it down to issues related to the Image retrieval, and often Kadir–Brady saliency detector, Feature extraction and Image resolution.
His most cited work include:
- Global contrast based salient region detection (2666 citations)
- Robust global registration (492 citations)
- Partial and approximate symmetry detection for 3D geometry (439 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Object, Algorithm and Shape analysis.
His Artificial intelligence study frequently intersects with other fields, such as Pattern recognition.
His Computer vision research is multidisciplinary, relying on both Range and Process.
His Object research incorporates themes from Contrast and Engineering drawing.
His Algorithm study integrates concerns from other disciplines, such as Set and Feature.
His studies deal with areas such as Geometry processing and Data mining as well as Shape analysis.
He most often published in these fields:
- Artificial intelligence (49.45%)
- Computer vision (30.18%)
- Object (15.27%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (49.45%)
- Pattern recognition (10.91%)
- Computer vision (30.18%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Point cloud and Artificial neural network.
His research brings together the fields of Identity and Artificial intelligence.
Niloy J. Mitra combines subjects such as Pixel, Robustness and Image editing with his study of Pattern recognition.
His biological study spans a wide range of topics, including Motion and Hallucinating.
Noise, Differentiable function and Manifold is closely connected to Surface in his research, which is encompassed under the umbrella topic of Point cloud.
His study explores the link between Artificial neural network and topics such as Geometry processing that cross with problems in Encoding, Shape analysis, Surface reconstruction and Outlier.
Between 2019 and 2021, his most popular works were:
- PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (44 citations)
- StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows (28 citations)
- BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Niloy J. Mitra mostly deals with Artificial intelligence, Pattern recognition, Generative model, Computer vision and Identity.
Generative grammar, Representation, Deep learning, Shape analysis and Convolution are the subjects of his Artificial intelligence studies.
The study incorporates disciplines such as Sampling, Point cloud, Face and Robustness in addition to Pattern recognition.
His Generative model study incorporates themes from Texture, Interpolation, Procedural texture and Set.
When carried out as part of a general Computer vision research project, his work on Shadow is frequently linked to work in Object, therefore connecting diverse disciplines of study.
His research in Outlier tackles topics such as Artificial neural network which are related to areas like Algorithm.
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