What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Image processing.
His Artificial intelligence study incorporates themes from Machine learning, Signal processing and Identification.
His Computer vision study frequently intersects with other fields, such as Color filter array.
His work on Image texture is typically connected to Context as part of general Pattern recognition study, connecting several disciplines of science.
His Facial recognition system research is multidisciplinary, incorporating elements of Multilinear principal component analysis, Principal component analysis and Linear discriminant analysis.
His Image processing research is multidisciplinary, relying on both Vector processor, Noise and Euclidean distance.
His most cited work include:
- Color Image Processing and Applications (818 citations)
- MPCA: Multilinear Principal Component Analysis of Tensor Objects (658 citations)
- Face recognition using kernel direct discriminant analysis algorithms (567 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, Pattern recognition, Algorithm and Color image.
The study incorporates disciplines such as Machine learning and Filter in addition to Artificial intelligence.
In Computer vision, Konstantinos N. Plataniotis works on issues like Color filter array, which are connected to Image sensor.
His studies in Pattern recognition integrate themes in fields like Pixel and Histogram.
Konstantinos N. Plataniotis combines subjects such as Kalman filter and Mathematical optimization with his study of Algorithm.
His Color image research incorporates elements of Binary image and Image texture.
He most often published in these fields:
- Artificial intelligence (61.86%)
- Computer vision (39.37%)
- Pattern recognition (24.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (61.86%)
- Pattern recognition (24.78%)
- Deep learning (4.57%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Bluetooth Low Energy.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Sensitivity.
Konstantinos N. Plataniotis has researched Pattern recognition in several fields, including Image and Set.
His Bluetooth Low Energy study also includes fields such as
- Real-time computing which connect with Wireless, Inertial measurement unit and Tracking,
- Particle filter, which have a strong connection to Filter and Scheme,
- Sample and Algorithm most often made with reference to Kalman filter.
His Filter study improves the overall literature in Computer vision.
His studies deal with areas such as Convolution and Finite impulse response as well as Computer vision.
Between 2018 and 2021, his most popular works were:
- COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. (109 citations)
- From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and opportunities (55 citations)
- PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Real-time computing are his primary areas of study.
His Artificial intelligence research incorporates themes from Field and Computer vision.
The various areas that he examines in his Computer vision study include Convolution and Finite impulse response.
His Deep learning research includes themes of Transfer of learning, Biometrics and Rotation.
His Pattern recognition study combines topics in areas such as Artificial neural network and Set.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Image segmentation, Sensitivity, Class, Discriminative model and Principal component analysis.
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