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, Image segmentation, Pattern recognition and Segmentation are his primary areas of study.
His Artificial intelligence study frequently links to related topics such as Machine learning.
His research on Computer vision often connects related topics like Boundary.
His Image segmentation research integrates issues from Object detection, Geometry and Maxima and minima.
Martin D. Levine combines subjects such as Motion compensation, Inference and Robustness with his study of Pattern recognition.
Martin D. Levine has included themes like Matching, Identification, Cognitive neuroscience of visual object recognition and Knowledge-based systems in his Segmentation study.
His most cited work include:
- Tracking deformable objects in the plane using an active contour model (555 citations)
- Vision in Man and Machine (475 citations)
- Visual Saliency Based on Scale-Space Analysis in the Frequency Domain (463 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, Segmentation and Image segmentation.
His study in Object, Cognitive neuroscience of visual object recognition, Scale-space segmentation, Image processing and Pixel is carried out as part of his Artificial intelligence studies.
Martin D. Levine specializes in Scale-space segmentation, namely Segmentation-based object categorization.
His research is interdisciplinary, bridging the disciplines of Robot and Computer vision.
His Facial expression research extends to the thematically linked field of Pattern recognition.
His Object detection study frequently links to other fields, such as Object-class detection.
He most often published in these fields:
- Artificial intelligence (60.10%)
- Computer vision (44.95%)
- Pattern recognition (19.19%)
What were the highlights of his more recent work (between 2009-2021)?
- Artificial intelligence (60.10%)
- Pattern recognition (19.19%)
- Computer vision (44.95%)
In recent papers he was focusing on the following fields of study:
Martin D. Levine focuses on Artificial intelligence, Pattern recognition, Computer vision, Classifier and Microbiology.
Artificial intelligence and Machine learning are frequently intertwined in his study.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Object detection, Deep learning and Iterative reconstruction.
His Computer vision study integrates concerns from other disciplines, such as Event and Salient.
He interconnects Cognitive neuroscience of visual object recognition, Appearance based, Normalization, Speech recognition and Convolutional neural network in the investigation of issues within Classifier.
The various areas that Martin D. Levine examines in his Microbiology study include Cadaverine, Saliva, Lysine and Biofilm.
Between 2009 and 2021, his most popular works were:
- Visual Saliency Based on Scale-Space Analysis in the Frequency Domain (463 citations)
- Online Dominant and Anomalous Behavior Detection in Videos (123 citations)
- An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions (110 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Codebook and Video tracking.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Matching.
His Computer vision research incorporates themes from Artificial neural network and Robustness.
His research in Pattern recognition intersects with topics in Object detection, Random forest and Facial expression.
His study in Codebook is interdisciplinary in nature, drawing from both Pixel and Inference.
His Video tracking study incorporates themes from Data mining, Interobject, Motion estimation, Histogram and Background subtraction.
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