What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Her scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Image processing and Image texture.
Her Surface research extends to the thematically linked field of Artificial intelligence.
Her studies in Computer vision integrate themes in fields like Ceramic, Ceramic tiles and Pattern recognition.
Her Pattern recognition research incorporates elements of Pixel, Facial recognition system, Robustness and Affine transformation.
Her Image processing study integrates concerns from other disciplines, such as Quality, Image registration, Anisotropic diffusion and Algorithm.
Her Image texture research includes themes of Standard illuminant, Classifier, Probabilistic description and Trigonometric functions.
Her most cited work include:
- Structural matching in computer vision using probabilistic relaxation (477 citations)
- Image processing : dealing with texture (435 citations)
- Image Processing: The Fundamentals (413 citations)
What are the main themes of her work throughout her whole career to date?
Maria Petrou mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Pixel and Image processing.
Her study in Image texture, Feature extraction, Photometric stereo, Texture and Image segmentation falls under the purview of Artificial intelligence.
The various areas that Maria Petrou examines in her Computer vision study include Surface and Pattern recognition.
As a part of the same scientific family, Maria Petrou mostly works in the field of Pattern recognition, focusing on Hough transform and, on occasion, Statistical hypothesis testing.
Her Pixel research includes elements of Image resolution, Remote sensing and Outlier.
Her Image processing research focuses on subjects like Algorithm, which are linked to Probabilistic logic.
She most often published in these fields:
- Artificial intelligence (69.11%)
- Computer vision (43.09%)
- Pattern recognition (30.35%)
What were the highlights of her more recent work (between 2008-2017)?
- Artificial intelligence (69.11%)
- Computer vision (43.09%)
- Pattern recognition (30.35%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Photometric stereo.
Her study in Feature extraction, Pixel, Facial recognition system, Face and Pattern recognition is done as part of Artificial intelligence.
Her work on Image segmentation, Image, Object detection and Iterative reconstruction as part of general Computer vision study is frequently linked to Frame, bridging the gap between disciplines.
Her study explores the link between Pattern recognition and topics such as Contextual image classification that cross with problems in Markov process and Cluster analysis.
Maria Petrou combines subjects such as Image processing, Tomographic reconstruction, Probabilistic logic and Mathematical analysis with her study of Algorithm.
Her studies in Photometric stereo integrate themes in fields like Albedo and Development.
Between 2008 and 2017, her most popular works were:
- On the choice of the parameters for anisotropic diffusion in image processing (142 citations)
- Sparse representations for facial expressions recognition via l 1 optimization (65 citations)
- Nonlinear Non-Negative Component Analysis Algorithms (56 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Maria Petrou mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Pixel.
Her work is connected to Facial recognition system, Fuzzy set, Fuzzy logic, Interpretation and Rule of inference, as a part of Artificial intelligence.
Her Handwriting recognition study in the realm of Pattern recognition connects with subjects such as Tracing.
Photometric stereo, Image texture, Image segmentation, Wavelet transform and Mathematical morphology are the primary areas of interest in her Computer vision study.
Her Feature extraction research integrates issues from Outlier, Spectral line, Endmember, Affine shape adaptation and Character recognition.
Her Pixel study integrates concerns from other disciplines, such as Luminance, Brightness, Linear combination, Convex hull and Surface.
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