What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Turgay Celik mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Pixel and Change detection.
Turgay Celik brings together Artificial intelligence and Fire detection to produce work in his papers.
His research integrates issues of Contextual image classification and Color histogram in his study of Pattern recognition.
The concepts of his Computer vision study are interwoven with issues in Entropy and Spatial distribution.
Turgay Celik interconnects Histogram, Multispectral pattern recognition, Thresholding and Complex wavelet transform in the investigation of issues within Pixel.
His Change detection research is multidisciplinary, incorporating elements of Image resolution, Multispectral image, Feature vector, Synthetic aperture radar and Feature extraction.
His most cited work include:
- Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$ -Means Clustering (455 citations)
- Fire detection in video sequences using a generic color model (282 citations)
- Contextual and Variational Contrast Enhancement (205 citations)
What are the main themes of his work throughout his whole career to date?
Turgay Celik mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Pixel and Change detection.
All of his Artificial intelligence and Discrete wavelet transform, Image processing, Complex wavelet transform, Feature vector and Wavelet investigations are sub-components of the entire Artificial intelligence study.
His research in Feature vector intersects with topics in Cluster analysis and Image texture.
His research in Pattern recognition tackles topics such as Image fusion which are related to areas like Resolution.
His Pixel study combines topics in areas such as Histogram, Histogram matching, Image histogram and Adaptive histogram equalization.
Turgay Celik studied Change detection and Mixture model that intersect with Bayesian probability and Feature detection.
He most often published in these fields:
- Artificial intelligence (94.44%)
- Computer vision (56.48%)
- Pattern recognition (55.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (94.44%)
- Remote sensing (7.41%)
- Convolutional neural network (8.33%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Remote sensing, Convolutional neural network, Pattern recognition and Machine learning.
Turgay Celik merges Artificial intelligence with Nonlinear correlation in his research.
His Remote sensing research is multidisciplinary, relying on both Construct, Convolution and Object detection.
Turgay Celik works mostly in the field of Convolutional neural network, limiting it down to topics relating to Feature and, in certain cases, Channel, Robustness, Generalization and Discriminative model.
Turgay Celik has included themes like Pixel, Outlier and Aster in his Pattern recognition study.
His work in the fields of Machine learning, such as Logistic regression and Artificial neural network, intersects with other areas such as Risk management tools, Bottleneck and Data modeling.
Between 2019 and 2021, his most popular works were:
- Toward a Smart Cloud: A Review of Fault-Tolerance Methods in Cloud Systems (19 citations)
- Statistical and machine learning models in credit scoring: A systematic literature survey (18 citations)
- A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Image fusion, Pattern recognition, Lens and Computer vision.
The study incorporates disciplines such as Machine learning and Logistic regression in addition to Artificial intelligence.
His Image fusion research is multidisciplinary, incorporating perspectives in Sparse approximation, Filter and Mutual information.
The study incorporates disciplines such as Histogram, Bilateral filter, Outlier and Radiometry in addition to Pattern recognition.
His research is interdisciplinary, bridging the disciplines of Convolutional neural network and Lens.
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