What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Computer vision, Inertial frame of reference, Pattern recognition and Image processing.
His Artificial intelligence study frequently links to related topics such as Process.
His Computer vision study combines topics in areas such as Artificial neural network and Set.
He interconnects String metric, Approximate string matching, Matching, Curvature and Semantic similarity in the investigation of issues within Pattern recognition.
The various areas that Nasser Kehtarnavaz examines in his Image processing study include Video tracking, Video post-processing, Multimedia and Video processing.
His Sensor fusion study combines topics from a wide range of disciplines, such as Modality, Inertial measurement unit and Data set.
His most cited work include:
- UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (326 citations)
- Brain Functional Localization: A Survey of Image Registration Techniques (224 citations)
- Real-time human action recognition based on depth motion maps (204 citations)
What are the main themes of his work throughout his whole career to date?
Nasser Kehtarnavaz mainly investigates Artificial intelligence, Computer vision, Image processing, Pattern recognition and Digital signal processing.
His study in Deep learning, Image, Feature extraction, Image segmentation and Image registration is done as part of Artificial intelligence.
Nasser Kehtarnavaz performs multidisciplinary study on Computer vision and Inertial frame of reference in his works.
As part of his studies on Image processing, he frequently links adjacent subjects like Algorithm.
Nasser Kehtarnavaz is involved in the study of Pattern recognition that focuses on Classifier in particular.
His Digital signal processing research is under the purview of Computer hardware.
He most often published in these fields:
- Artificial intelligence (54.25%)
- Computer vision (42.50%)
- Image processing (15.50%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (54.25%)
- Computer vision (42.50%)
- Deep learning (6.25%)
In recent papers he was focusing on the following fields of study:
Nasser Kehtarnavaz spends much of his time researching Artificial intelligence, Computer vision, Deep learning, Speech recognition and Convolutional neural network.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
Nasser Kehtarnavaz performs integrative Computer vision and Inertial frame of reference research in his work.
He has researched Deep learning in several fields, including Speech denoising, Software deployment and Panchromatic film, Multispectral image, Remote sensing.
His Speech recognition study integrates concerns from other disciplines, such as Signal-to-noise ratio and Hearing aid.
His biological study spans a wide range of topics, including Inference, Video camera and Spectrogram.
Between 2015 and 2021, his most popular works were:
- Real-time human action recognition based on depth motion maps (204 citations)
- A survey of depth and inertial sensor fusion for human action recognition (190 citations)
- Image Segmentation Using Deep Learning: A Survey (107 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
Nasser Kehtarnavaz mainly focuses on Artificial intelligence, Computer vision, Inertial frame of reference, Convolutional neural network and Wearable computer.
Nasser Kehtarnavaz interconnects Android and Pattern recognition in the investigation of issues within Artificial intelligence.
His Pattern recognition research incorporates themes from Extreme learning machine, Histogram, Invariant and Kernel.
As a part of the same scientific family, Nasser Kehtarnavaz mostly works in the field of Computer vision, focusing on Modality and, on occasion, Video camera.
His Convolutional neural network study combines topics in areas such as Inference and Spectrogram.
His studies deal with areas such as Motion, Human–computer interaction and Random order as well as Wearable computer.
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