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, Feature extraction and Object detection are his primary areas of study.
His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning.
His Pattern recognition study incorporates themes from Artificial neural network, Histogram and Feature.
His Feature extraction research is multidisciplinary, incorporating perspectives in Detector, Face detection, Partial least squares regression, Pascal and Convolutional neural network.
The study incorporates disciplines such as Video tracking, Pose, Kernel and Robustness in addition to Object detection.
His studies deal with areas such as Algorithm and Motion analysis as well as Image processing.
His most cited work include:
- W/sup 4/: real-time surveillance of people and their activities (2555 citations)
- Non-parametric Model for Background Subtraction (1956 citations)
- Background and foreground modeling using nonparametric kernel density estimation for visual surveillance (1358 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, Machine learning and Object detection.
His works in Object, Image, Segmentation, Image processing and Feature extraction are all subjects of inquiry into Artificial intelligence.
His work in Image segmentation, Pixel, Tracking, Background subtraction and Motion estimation are all subfields of Computer vision research.
Larry S. Davis mostly deals with Scale-space segmentation in his studies of Image segmentation.
His research in Pattern recognition intersects with topics in Contextual image classification, Facial recognition system and Feature.
His Object detection study integrates concerns from other disciplines, such as Detector and Robustness.
He most often published in these fields:
- Artificial intelligence (80.96%)
- Computer vision (45.12%)
- Pattern recognition (30.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (80.96%)
- Pattern recognition (30.90%)
- Machine learning (13.72%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Object detection.
As part of his studies on Artificial intelligence, Larry S. Davis often connects relevant areas like Detector.
His research investigates the link between Pattern recognition and topics such as Visualization that cross with problems in Search engine indexing.
His Machine learning research is multidisciplinary, relying on both Frame, Embedding, Inference and Adversarial system.
Larry S. Davis has included themes like Artificial neural network, Process and Test set in his Computer vision study.
He has researched Object detection in several fields, including Minimum bounding box, Contextual image classification, Benchmark, Focus and Pyramid.
Between 2017 and 2021, his most popular works were:
- NISP: Pruning Networks Using Neuron Importance Score Propagation (354 citations)
- An Analysis of Scale Invariance in Object Detection - SNIP (331 citations)
- Adversarial training for free (268 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Larry S. Davis mainly focuses on Artificial intelligence, Pattern recognition, Object detection, Machine learning and Computer vision.
Image resolution is closely connected to Detector in his research, which is encompassed under the umbrella topic of Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Visualization and Image.
The various areas that Larry S. Davis examines in his Object detection study include Contextual image classification, Noise, Bilinear interpolation and Code.
His Machine learning research includes elements of Adversarial system, Frame and Forgetting.
His work on Minimum bounding box, Inpainting and Image synthesis as part of general Computer vision study is frequently linked to Compatibility and Clothing, bridging the gap between disciplines.
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