What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
His primary scientific interests are in Artificial intelligence, Computer vision, Filter, Algorithm and Pattern recognition.
His Artificial intelligence and Digital image processing, Binary image, Analog image processing, Digital image and Microscope image processing investigations all form part of his Artificial intelligence research activities.
His Computer vision study frequently draws connections between adjacent fields such as Noise.
His biological study spans a wide range of topics, including Image processing, Edge detection, Orientation and Adaptive filter.
The various areas that L.J. van Vliet examines in his Algorithm study include Gaussian random field, Gaussian function, Gaussian filter and Filter design.
In the subject of general Pattern recognition, his work in Linear discriminant analysis is often linked to Computed Tomography Colonography, thereby combining diverse domains of study.
His most cited work include:
- Fundamentals of Image Processing (292 citations)
- Separable bilateral filtering for fast video preprocessing (207 citations)
- A quantitative comparison of image restoration methods for confocal microscopy (145 citations)
What are the main themes of his work throughout his whole career to date?
L.J. van Vliet focuses on Artificial intelligence, Computer vision, Algorithm, Image processing and Optics.
His work carried out in the field of Artificial intelligence brings together such families of science as Filter and Pattern recognition.
His research investigates the connection between Algorithm and topics such as Curvature that intersect with issues in Measure.
In his study, Meniscus is inextricably linked to Evaporation, which falls within the broad field of Optics.
His Edge detection research integrates issues from Smoothing and Thresholding.
His Orientation research is multidisciplinary, incorporating perspectives in Space, Image and Anisotropy.
He most often published in these fields:
- Artificial intelligence (43.54%)
- Computer vision (32.65%)
- Algorithm (20.41%)
What were the highlights of his more recent work (between 2009-2020)?
- Artificial intelligence (43.54%)
- Computer vision (32.65%)
- Radiology (4.08%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Radiology, Diffusion MRI and Coronary arteries.
His Artificial intelligence research includes elements of Anatomy and Pattern recognition.
Much of his study explores Computer vision relationship to Biomechanics.
His research integrates issues of Reduction and Thresholding in his study of Radiology.
His Diffusion MRI research includes themes of Statistics and Nuclear magnetic resonance.
His Medical imaging research is multidisciplinary, incorporating elements of Filter, Image processing, Inpainting, Angiography and Pattern recognition.
Between 2009 and 2020, his most popular works were:
- Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. (127 citations)
- Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow (66 citations)
- Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
His primary areas of study are Artificial intelligence, Computer vision, Image processing, Radiology and Coronary arteries.
As part of his studies on Artificial intelligence, L.J. van Vliet frequently links adjacent subjects like Pattern recognition.
His Computer vision research incorporates elements of Carpal bones, Wrist, Ligament, Biomechanics and Surgical planning.
His Image processing research incorporates themes from Medical imaging, Microscopy, Image registration, Angiography and Angiocardiography.
His study in the field of Computed tomography angiography and Stenosis also crosses realms of Coronary artery disease and Triage.
The study incorporates disciplines such as Principal curvature, Mahalanobis distance and Feature vector in addition to False positive paradox.
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