What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Artificial intelligence, Computer vision, Image processing, Mathematical morphology and Pattern recognition.
His study in Feature, Feature extraction, Pixel, Artificial neural network and Network model falls under the purview of Artificial intelligence.
His biological study spans a wide range of topics, including Motion estimation, Histogram, Residual frame and Shot.
His Image processing research is multidisciplinary, relying on both Algorithm, Document processing and Pattern recognition.
As part of one scientific family, Bhabatosh Chanda deals mainly with the area of Mathematical morphology, narrowing it down to issues related to the Grayscale, and often Point.
While the research belongs to areas of Pattern recognition, Bhabatosh Chanda spends his time largely on the problem of Multilayer perceptron, intersecting his research to questions surrounding Support vector machine and Data mining.
His most cited work include:
- Digital Image Processing and Analysis (278 citations)
- Multiscale morphological segmentation of gray-scale images (145 citations)
- Writer-independent off-line signature verification using surroundedness feature (138 citations)
What are the main themes of his work throughout his whole career to date?
Bhabatosh Chanda mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image processing and Image.
His studies in Feature extraction, Segmentation, Histogram, Pixel and Image segmentation are all subfields of Artificial intelligence research.
His work in Feature extraction addresses subjects such as Iris recognition, which are connected to disciplines such as IRIS.
His Image segmentation research incorporates elements of Document processing, Search engine indexing and Identification.
He focuses mostly in the field of Computer vision, narrowing it down to matters related to Algorithm and, in some cases, Mathematical optimization.
His Pattern recognition research is multidisciplinary, incorporating elements of Artificial neural network and Feature.
He most often published in these fields:
- Artificial intelligence (77.29%)
- Computer vision (48.79%)
- Pattern recognition (44.93%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (77.29%)
- Pattern recognition (44.93%)
- Histogram (11.59%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Histogram, Feature extraction and Image are his primary areas of study.
Much of his study explores Artificial intelligence relationship to Computer vision.
His Computer vision study combines topics from a wide range of disciplines, such as Transmittance and Mural.
His studies deal with areas such as Artificial neural network, Feature, Feature and Robustness as well as Pattern recognition.
The study incorporates disciplines such as Optical flow, Pixel and Invariant in addition to Histogram.
Bhabatosh Chanda works mostly in the field of Image, limiting it down to concerns involving Benchmark and, occasionally, Image segmentation.
Between 2016 and 2020, his most popular works were:
- A novel cancelable iris recognition system based on feature learning techniques (33 citations)
- Local directional ZigZag pattern: A rotation invariant descriptor for texture classification (23 citations)
- Fractal image compression using upper bound on scaling parameter (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Bhabatosh Chanda mainly investigates Artificial intelligence, Pattern recognition, Feature extraction, Sparse approximation and Image resolution.
His Artificial intelligence study is mostly concerned with Feature learning, Texture Descriptor, Histogram, Feature and Pixel.
Bhabatosh Chanda has included themes like Facial recognition system and Word in his Pattern recognition study.
Bhabatosh Chanda combines subjects such as Artificial neural network, Feature, Convolutional neural network and Classifier with his study of Feature extraction.
His Sparse approximation research integrates issues from Pyramid, Iterative reconstruction, Statistics, Image restoration and Kernel.
His Image resolution study combines topics in areas such as Singular value decomposition, Higher-order singular value decomposition, Inpainting, Cluster analysis and Markov random field.
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