What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Aleksandra Pizurica mainly investigates Artificial intelligence, Pattern recognition, Wavelet, Wavelet transform and Computer vision.
Her work on Artificial intelligence is being expanded to include thematically relevant topics such as Random field.
Her work on Markov random field as part of general Pattern recognition study is frequently linked to Additive white Gaussian noise, bridging the gap between disciplines.
Her biological study spans a wide range of topics, including Noise reduction, Video denoising, Filter and Thresholding.
She has researched Wavelet transform in several fields, including Adaptive filter, Statistical model, Image noise, Noise measurement and Image restoration.
Her studies link Hyperspectral imaging with Computer vision.
Her most cited work include:
- A versatile wavelet domain noise filtration technique for medical imaging (446 citations)
- Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising (389 citations)
- A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising (259 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Computer vision, Pattern recognition, Wavelet and Algorithm.
Her work in Noise reduction, Wavelet transform, Image, Hyperspectral imaging and Video denoising is related to Artificial intelligence.
The study of Computer vision is intertwined with the study of Noise in a number of ways.
Her Pattern recognition research incorporates elements of Pixel, Gaussian noise and Image noise.
Her Wavelet study incorporates themes from Speech recognition and Filter.
Aleksandra Pizurica combines subjects such as Inverse scattering problem and Mathematical optimization with her study of Algorithm.
She most often published in these fields:
- Artificial intelligence (73.76%)
- Computer vision (46.81%)
- Pattern recognition (35.82%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (73.76%)
- Pattern recognition (35.82%)
- Hyperspectral imaging (11.35%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Artificial intelligence, Pattern recognition, Hyperspectral imaging, Image and Algorithm.
Artificial intelligence and Painting are two areas of study in which Aleksandra Pizurica engages in interdisciplinary work.
The various areas that Aleksandra Pizurica examines in her Pattern recognition study include Matching, Pixel, Subspace topology and Robustness.
Her Hyperspectral imaging research includes themes of Optimization problem, Sparse matrix, Cluster analysis and Hyperspectral image classification.
Her work deals with themes such as Markov random field, Graph and Auto encoders, which intersect with Algorithm.
Her biological study deals with issues like Computer vision, which deal with fields such as Angiography.
Between 2016 and 2021, her most popular works were:
- Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation (38 citations)
- Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification (36 citations)
- Image denoising algorithms : from wavelet shrinkage to nonlocal collaborative filtering (11 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Aleksandra Pizurica mainly focuses on Artificial intelligence, Pattern recognition, Hyperspectral imaging, Sparse matrix and Cluster analysis.
When carried out as part of a general Artificial intelligence research project, her work on Deep learning, Wavelet and Compressed sensing is frequently linked to work in Painting and Visual arts, therefore connecting diverse disciplines of study.
Her work on Wavelet shrinkage as part of general Pattern recognition study is frequently linked to Collaborative filtering, therefore connecting diverse disciplines of science.
Her biological study spans a wide range of topics, including Subspace topology, Spatial analysis, Feature extraction, Regularization and Optimization problem.
Her studies deal with areas such as Segmentation, Image segmentation, Feature, Computer vision and Support vector machine as well as Spatial analysis.
Her research on Cluster analysis also deals with topics like
- Coefficient matrix which is related to area like Constraint and Data point,
- Remote sensing which is related to area like Digital image processing, Sensor fusion, Data set, Binary tree and Interaction information.
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