2019 - Fellow of the Indian National Academy of Engineering (INAE)
2018 - IEEE Fellow For contributions to image processing algorithms for biological microscopy
Badrinath Roysam spends much of his time researching Artificial intelligence, Computer vision, Tracing, Segmentation and Pattern recognition. His Artificial intelligence study frequently intersects with other fields, such as Algorithm. His research in Computer vision intersects with topics in Biological system, Retinal, Fundus and Robustness.
His study focuses on the intersection of Tracing and fields such as Confocal microscopy with connections in the field of Confocal, Cell type, Divide and conquer algorithms, Graphical user interface and Software. Badrinath Roysam combines subjects such as Block, Wavelet and Fast marching method with his study of Segmentation. His work in Image segmentation addresses subjects such as Cluster analysis, which are connected to disciplines such as Segmentation-based object categorization, Image processing and Object model.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Algorithm. His Artificial intelligence study frequently links to other fields, such as Tracing. His study in Tracing is interdisciplinary in nature, drawing from both Confocal, Microscope, Software system, Confocal microscopy and Visualization.
His Computer vision study integrates concerns from other disciplines, such as Retina and Retinal. His Pattern recognition study combines topics in areas such as Software, Feature and Cluster analysis. His work in Algorithm covers topics such as Massively parallel which are related to areas like Theoretical computer science.
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Software. His Artificial intelligence research focuses on Machine learning and how it connects with Agile software development. The Image processing research he does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Set, therefore creating a link between diverse domains of science.
His Pattern recognition research includes themes of Routing, Superresolution, Feature and Cluster analysis. Badrinath Roysam interconnects Autofluorescence, Deep neural networks, Tracking, Immunostaining and Field of view in the investigation of issues within Segmentation. His Software research focuses on subjects like Biological imaging, which are linked to Software engineering, Information retrieval, Focus and Digital image data.
Badrinath Roysam mainly focuses on Artificial intelligence, Computer vision, Pathology, Software and Image processing. Badrinath Roysam regularly ties together related areas like Pattern recognition in his Artificial intelligence studies. His Computer vision research integrates issues from Cell, Cell signaling, Tracing, Algorithm and Effector.
His Software research is multidisciplinary, relying on both Digital image data, Biological imaging, Focus and Data science. The various areas that Badrinath Roysam examines in his Image processing study include Cytotoxic T cell, Cell morphology, Biomedical engineering and Microscopy. His Segmentation research incorporates elements of Stem cell and Neural stem cell.
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Image change detection algorithms: a systematic survey
R.J. Radke;S. Andra;O. Al-Kofahi;B. Roysam.
IEEE Transactions on Image Processing (2005)
Adult SVZ Stem Cells Lie in a Vascular Niche: A Quantitative Analysis of Niche Cell-Cell Interactions
Qin Shen;Qin Shen;Yue Wang;Erzsebet Kokovay;Erzsebet Kokovay;Gang Lin.
Cell Stem Cell (2008)
Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images
Y. Al-Kofahi;W. Lassoued;W. Lee;B. Roysam.
IEEE Transactions on Biomedical Engineering (2010)
Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms
Ali Can;Hong Shen;J.N. Turner;H.L. Tanenbaum.
international conference of the ieee engineering in medicine and biology society (1999)
Biological imaging software tools
Kevin W. Eliceiri;Michael R. Berthold;Ilya G. Goldberg;Luis Ibáñez.
Nature Methods (2012)
The dual-bootstrap iterative closest point algorithm with application to retinal image registration
C.V. Stewart;Chia-Ling Tsai;B. Roysam.
IEEE Transactions on Medical Imaging (2003)
A hyperfused mitochondrial state achieved at G1-S regulates cyclin E buildup and entry into S phase.
Kasturi Mitra;Christian Wunder;Badrinath Roysam;Gang Lin.
Proceedings of the National Academy of Sciences of the United States of America (2009)
A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks
Gang Lin;Umesh Adiga;Umesh Adiga;Kathy Olson;John F. Guzowski.
Cytometry Part A (2003)
A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina
A. Can;C.V. Stewart;B. Roysam;H.L. Tanenbaum.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion.
C. S. Bjornsson;Seung Jae Oh;Y. A. Al-Kofahi;Y. J. Lim.
Journal of Neural Engineering (2006)
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