What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
James J. Little mainly investigates Artificial intelligence, Computer vision, Mobile robot, Robot and Robustness.
His Artificial intelligence study frequently draws connections to other fields, such as Pattern recognition.
His work in Computer vision tackles topics such as Simultaneous localization and mapping which are related to areas like Hough transform and RANSAC.
His Mobile robot research is multidisciplinary, incorporating perspectives in Motion estimation and Motion planning.
His Robustness research is multidisciplinary, relying on both Histogram, Sensory cue, Linear subspace and Early vision.
His Mobile robot navigation study which covers Computer stereo vision that intersects with Occupancy grid mapping.
His most cited work include:
- A Boosted Particle Filter: Multitarget Detection and Tracking (944 citations)
- Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks (740 citations)
- A Simple Yet Effective Baseline for 3d Human Pose Estimation (488 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, Robot, Mobile robot and Pattern recognition.
His Artificial intelligence research focuses on Machine learning and how it relates to Training set.
His research is interdisciplinary, bridging the disciplines of Robustness and Computer vision.
His research investigates the link between Robot and topics such as Human–computer interaction that cross with problems in Obstacle avoidance and Machine vision.
His research on Mobile robot frequently connects to adjacent areas such as Landmark.
His Particle filter study integrates concerns from other disciplines, such as Simultaneous localization and mapping, Tracking system and Eye tracking.
He most often published in these fields:
- Artificial intelligence (82.08%)
- Computer vision (63.21%)
- Robot (16.51%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (82.08%)
- Computer vision (63.21%)
- Machine learning (9.91%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Image and Pose.
His Artificial intelligence study frequently links to other fields, such as Pattern recognition.
Many of his studies involve connections with topics such as Event and Computer vision.
The concepts of his Machine learning study are interwoven with issues in Codebook and Quantization.
In the field of Pose, his study on 3D pose estimation overlaps with subjects such as Pan tilt zoom.
His 3D pose estimation course of study focuses on Ground truth and Artificial neural network.
Between 2016 and 2021, his most popular works were:
- A Simple Yet Effective Baseline for 3d Human Pose Estimation (488 citations)
- Exploiting Temporal Information for 3D Human Pose Estimation (146 citations)
- A simple yet effective baseline for 3d human pose estimation (97 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
James J. Little focuses on Artificial intelligence, Pose, 3D pose estimation, Computer vision and Benchmark.
His work on Ground truth, Pixel and Robotics as part of general Artificial intelligence research is frequently linked to Constraint and Set, bridging the gap between disciplines.
His Pixel study combines topics in areas such as Image segmentation, Motion blur, Point, Line segment and Virtual reality.
The various areas that James J. Little examines in his Robotics study include Scheme, Image, Tree and Backtracking.
His Computer vision study incorporates themes from Field and Synthetic data.
His work on Deep learning and Random forest as part of general Machine learning study is frequently linked to Regression, bridging the gap between disciplines.
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