What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Kannappan Palaniappan mainly investigates Artificial intelligence, Computer vision, Video tracking, Image segmentation and Tracking.
He interconnects Data mining and Pattern recognition in the investigation of issues within Artificial intelligence.
Computer vision and Live cell imaging are commonly linked in his work.
His Video tracking research is multidisciplinary, incorporating elements of Image sensor, Feature, Cluster analysis, Vehicle tracking system and Sensor fusion.
As a part of the same scientific study, Kannappan Palaniappan usually deals with the Image segmentation, concentrating on Multispectral image and frequently concerns with Scientific visualization, Zoom, Computer graphics, Glyph and Computer graphics.
Kannappan Palaniappan focuses mostly in the field of Tracking, narrowing it down to matters related to Object and, in some cases, Thermal infrared and Ground truth.
His most cited work include:
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
- The Visual Object Tracking VOT2017 Challenge Results (285 citations)
- Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration (246 citations)
What are the main themes of his work throughout his whole career to date?
Kannappan Palaniappan spends much of his time researching Artificial intelligence, Computer vision, Image segmentation, Pattern recognition and Segmentation.
His work in Video tracking, Feature, Feature extraction, Robustness and Object detection are all subfields of Artificial intelligence research.
He interconnects Histogram, Frame rate and Automatic summarization in the investigation of issues within Video tracking.
His Image segmentation study frequently links to other fields, such as Algorithm.
His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Local binary patterns and Deep learning.
His Tracking research focuses on subjects like Object, which are linked to Benchmark.
He most often published in these fields:
- Artificial intelligence (70.59%)
- Computer vision (54.12%)
- Image segmentation (21.18%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (70.59%)
- Computer vision (54.12%)
- Deep learning (7.06%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Deep learning, Video tracking and Feature extraction.
His research ties Pattern recognition and Artificial intelligence together.
His work on Feature, Object detection and Aerial video as part of general Computer vision study is frequently linked to Pipeline, therefore connecting diverse disciplines of science.
His work deals with themes such as Facial expression recognition, Speech recognition and Facial expression, which intersect with Deep learning.
The various areas that he examines in his Video tracking study include Annotation, Edge computing and Eye tracking.
The study incorporates disciplines such as Radon transform and Robustness in addition to Feature extraction.
Between 2018 and 2021, his most popular works were:
- Multiscale Structure Tensor for Improved Feature Extraction and Image Regularization (9 citations)
- VisDrone-MOT2019: The Vision Meets Drone Multiple Object Tracking Challenge Results (6 citations)
- Facial expression recognition for monitoring neurological disorders based on convolutional neural network (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Kannappan Palaniappan focuses on Artificial intelligence, Computer vision, Drone, Deep learning and Feature extraction.
His Artificial intelligence study focuses mostly on Random forest, Convolutional neural network, Image segmentation, Medical imaging and Classifier.
He combines Computer vision and Pipeline in his research.
His research integrates issues of Video tracking, Object and Infrared in his study of Drone.
His Deep learning study incorporates themes from Facial expression recognition, Speech recognition and Facial expression.
His Feature extraction study combines topics in areas such as Anisotropic diffusion, Classifier and Algorithm.
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