What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Artificial intelligence, Computer vision, Pattern recognition, Image texture and Image processing are his primary areas of study.
Image segmentation, Feature extraction, Segmentation, Edge detection and Gabor wavelet are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
His biological study deals with issues like Search engine indexing, which deal with fields such as Motion estimation, Server and Multimedia.
His studies in Pattern recognition integrate themes in fields like Contextual image classification, Pixel, Color quantization and Feature.
His Image texture research incorporates elements of Similarity, Content-based image retrieval, Image retrieval, Texture and Similitude.
B.S. Manjunath has researched Image processing in several fields, including Video tracking, Biological imaging, Data mining and Focus.
His most cited work include:
- Texture features for browsing and retrieval of image data (3401 citations)
- Color and texture descriptors (1541 citations)
- Multisensor image fusion using the wavelet transform (1418 citations)
What are the main themes of his work throughout his whole career to date?
B.S. Manjunath mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Segmentation.
Artificial intelligence is a component of his Image texture, Feature extraction, Feature, Image processing and Scale-space segmentation studies.
His study looks at the intersection of Image texture and topics like Image retrieval with Information retrieval.
His Video tracking, Image registration, Object, Edge detection and Object detection investigations are all subjects of Computer vision research.
The various areas that B.S. Manjunath examines in his Pattern recognition study include Contextual image classification, Image, Pixel and Deep learning.
B.S. Manjunath works on Image segmentation which deals in particular with Region growing.
He most often published in these fields:
- Artificial intelligence (74.43%)
- Computer vision (45.21%)
- Pattern recognition (42.47%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (74.43%)
- Pattern recognition (42.47%)
- Deep learning (4.11%)
In recent papers he was focusing on the following fields of study:
B.S. Manjunath spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Feature extraction and Convolutional neural network.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision.
B.S. Manjunath has included themes like Leverage and Automatic summarization in his Computer vision study.
The Pattern recognition study combines topics in areas such as Digital image, Feature, Representation, Image and Resampling.
His Feature extraction research incorporates themes from Voxel, Noise reduction and Feature selection.
His Convolutional neural network study incorporates themes from Smoothing, Nonlinear dimensionality reduction, Visualization, Data mining and Embedding.
Between 2015 and 2021, his most popular works were:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Exploiting Spatial Structure for Localizing Manipulated Image Regions (82 citations)
- Weakly Supervised Localization Using Deep Feature Maps (69 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
B.S. Manjunath spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Convolutional neural network and Segmentation.
His Artificial intelligence study frequently links to adjacent areas such as Machine learning.
The study incorporates disciplines such as Cognitive neuroscience of visual object recognition, Digital image, Pixel, Computer vision and Co-occurrence in addition to Pattern recognition.
His studies deal with areas such as Hypergraph, Ranking and Representation as well as Computer vision.
His Feature extraction study integrates concerns from other disciplines, such as Spatial intelligence, Image segmentation and Data mining.
His work carried out in the field of Convolutional neural network brings together such families of science as Nonlinear dimensionality reduction, Correspondence problem, Embedding, Feature vector and Atlas.
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