What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Pixel and Machine learning.
His research on Artificial intelligence frequently connects to adjacent areas such as Natural language processing.
Chew Lim Tan has researched Pattern recognition in several fields, including Text mining, Object detection and Sobel operator.
His work deals with themes such as Segmentation, Feature detection and Hausdorff distance, which intersect with Pixel.
Chew Lim Tan has included themes like Data mining, Data set and Coreference in his Machine learning study.
His biological study spans a wide range of topics, including Graphics and Pattern recognition.
His most cited work include:
- Discretization: An Enabling Technique (775 citations)
- Supervised and Traditional Term Weighting Methods for Automatic Text Categorization (418 citations)
- A case study on using neural networks to perform technical forecasting of forex (321 citations)
What are the main themes of his work throughout his whole career to date?
Chew Lim Tan spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Natural language processing and Feature extraction.
Artificial intelligence is closely attributed to Machine learning in his research.
His research integrates issues of Feature, Word, Cluster analysis, Contextual image classification and Histogram in his study of Pattern recognition.
His study in Image processing, Orientation, Image restoration, Edge detection and Image are all subfields of Computer vision.
His work deals with themes such as Character, Speech recognition, Task and Information retrieval, which intersect with Natural language processing.
His research in Pixel intersects with topics in Wavelet and Sobel operator.
He most often published in these fields:
- Artificial intelligence (85.54%)
- Pattern recognition (48.38%)
- Computer vision (40.15%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (85.54%)
- Pattern recognition (48.38%)
- Computer vision (40.15%)
In recent papers he was focusing on the following fields of study:
Chew Lim Tan mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Pixel and Feature extraction.
His work is connected to Image, Histogram, Segmentation, Orientation and Text segmentation, as a part of Artificial intelligence.
Chew Lim Tan combines subjects such as Sobel operator and Graphics with his study of Pattern recognition.
His work on Wavelet as part of general Computer vision research is frequently linked to Line, bridging the gap between disciplines.
Chew Lim Tan focuses mostly in the field of Pixel, narrowing it down to topics relating to Robustness and, in certain cases, Similarity, Spectral clustering, The Internet, Born-digital and Linkage.
His study in Feature extraction is interdisciplinary in nature, drawing from both Delaunay triangulation, Categorization, Task and Keyword spotting.
Between 2013 and 2020, his most popular works were:
- A robust arbitrary text detection system for natural scene images (194 citations)
- Text Flow: A Unified Text Detection System in Natural Scene Images (166 citations)
- Scene text extraction based on edges and support vector regression (83 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Pixel and Feature extraction.
His Artificial intelligence research incorporates themes from Data mining and Graphics.
His work carried out in the field of Pattern recognition brings together such families of science as Orientation and Histogram.
His Computer vision study combines topics from a wide range of disciplines, such as Neural coding and k-nearest neighbors algorithm.
As part of the same scientific family, Chew Lim Tan usually focuses on Pixel, concentrating on Font and intersecting with Image resolution, Contrast and Text mining.
The various areas that Chew Lim Tan examines in his Support vector machine study include Representation and Automatic image annotation.
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