What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
Lalit Keshav Mestha mainly focuses on Computer vision, Artificial intelligence, Signal, Pixel and Optics.
Lalit Keshav Mestha integrates Computer vision and Color calibration in his research.
Lalit Keshav Mestha frequently studies issues relating to Respiration rate and Artificial intelligence.
His Signal study combines topics from a wide range of disciplines, such as Heart rate variability, Real-time computing and Pulse rate.
His studies deal with areas such as Pattern detection, Ir image, Region of interest, Motion artifacts and Remote sensing as well as Pixel.
His Optics research is multidisciplinary, relying on both Optoelectronics and Filter.
His most cited work include:
- Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. (380 citations)
- Diagnostics for color printer on-line spectrophotometer control system (160 citations)
- Web enabled color management service system and method (117 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Signal, Optics and Pixel.
His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence.
His Computer vision study frequently links to adjacent areas such as Computer graphics.
His Signal research is multidisciplinary, incorporating elements of Speech recognition and Video image.
His research on Optics often connects related topics like Optoelectronics.
His ICC profile research incorporates themes from Color histogram and High color.
He most often published in these fields:
- Artificial intelligence (52.35%)
- Computer vision (49.86%)
- Signal (17.45%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (52.35%)
- Computer vision (49.86%)
- Feature vector (4.71%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Feature vector, Signal and Decision boundary.
His Artificial intelligence study integrates concerns from other disciplines, such as Photoplethysmogram and Pattern recognition.
Region of interest, Pixel, Video camera, Projection and Video image are subfields of Computer vision in which his conducts study.
His work deals with themes such as Node, Node, Data mining and Feature, which intersect with Feature vector.
His research integrates issues of Ventricular premature contraction, Internal medicine and Cardiology in his study of Signal.
His research investigates the connection with Decision boundary and areas like State which intersect with concerns in Aggregate.
Between 2014 and 2020, his most popular works were:
- Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. (380 citations)
- Detection of atrial fibrillation using contactless facial video monitoring (77 citations)
- Threat detection and localization for monitoring nodes of an industrial asset control system (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
Lalit Keshav Mestha spends much of his time researching Artificial intelligence, Feature vector, Computer vision, Decision boundary and Signal.
His work in Artificial intelligence addresses subjects such as Pattern recognition, which are connected to disciplines such as Histogram.
The study incorporates disciplines such as Control system, Pixel, Respiratory pattern and Channel in addition to Feature vector.
In general Computer vision, his work in Region of interest and Projection is often linked to Humanities, Subject and Respiratory function linking many areas of study.
His Decision boundary research incorporates elements of Data mining, Feature, State, Node and Node.
Lalit Keshav Mestha has included themes like Acoustics, Electronic engineering and Photodetector in his Signal study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
