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Aleksandra Pizurica

Aleksandra Pizurica

D-Index & Metrics

Computer Science

D-Index
41
Citations
8478
World Ranking
8728
National Ranking
84

Overview

Aleksandra Pizurica is affiliated with Ghent University in Belgium and has a significant body of research in computer science and engineering. Their work is primarily focused on the subfields of computer vision and pattern recognition, media technology, radiology, nuclear medicine and imaging, biomedical engineering, and atmospheric science.

The main topics covered in their research include:

  • Remote-Sensing Image Classification
  • Image Retrieval and Classification Techniques
  • Remote Sensing and Land Use
  • Image and Signal Denoising Methods
  • Advanced Image Fusion Techniques
  • Advanced Image and Video Retrieval Techniques
  • Sparse and Compressive Sensing Techniques

Aleksandra Pizurica has published extensively in various venues, with notable frequent publication outlets including:

  • IEEE Transactions on Geoscience and Remote Sensing
  • arXiv (Cornell University)
  • Sensors
  • Ghent University Academic Bibliography (Ghent University)
  • Remote Sensing

Their recent papers illustrate a focus on hyperspectral imaging, deep learning applications, and biomedical image segmentation. Selected examples include:

  • "Subspace Clustering for Hyperspectral Images via Dictionary Learning With Adaptive Regularization" (2021, IEEE Transactions on Geoscience and Remote Sensing)
  • "Crack Detection in Paintings Using Convolutional Neural Networks" (2020, IEEE Access)
  • "Overview of the Whole Heart and Heart Chamber Segmentation Methods" (2020, Cardiovascular Engineering and Technology)
  • "Understanding Skin Color Bias in Deep Learning-Based Skin Lesion Segmentation" (2024, Computer Methods and Programs in Biomedicine)
  • "Heterogeneous Regularization-Based Tensor Subspace Clustering for Hyperspectral Band Selection" (2022, IEEE Transactions on Neural Networks and Learning Systems)

The scientist has collaborated frequently with several researchers, indicating an active role in collaborative studies. Among the frequent coauthors are:

  • Shaoguang Huang
  • Irena Galić
  • Xian Li
  • Emmanuel Audenaert
  • Hongyan Zhang

Overall, Aleksandra Pizurica's research spans multiple technical domains within computer science and engineering, emphasizing remote sensing, image analysis, and biomedical applications. Their publications reflect a commitment to advancing methods in image classification, segmentation, and fusion techniques across various scientific and technological contexts.

Best Publications

  • A versatile wavelet domain noise filtration technique for medical imaging

    A. Pizurica;W. Philips;I. Lemahieu;M. Acheroy

  • Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

    Christian Debes;Andreas Merentitis;Roel Heremans;Jürgen T. Hahn

  • Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising

    A. Pizurica;W. Philips

  • A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

    A. Pizurica;W. Philips;I. Lemahieu;M. Acheroy

  • Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

    Wenzhi Liao;Xin Huang;Frieke Van Coillie;Sidharta Gautama

  • A review of wavelet denoising in MRI and ultrasound brain imaging

    Aleksandra Pizurica;Alle M. Wink;Ewout Vansteenkiste;Wilfried Philips

  • Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images

    Wenzhi Liao;A. Pizurica;P. Scheunders;W. Philips

  • Context-Aware Patch-Based Image Inpainting Using Markov Random Field Modeling

    Tijana Ruzic;Aleksandra Pizurica

  • Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features

    Wenzhi Liao;Aleksandra Pizurica;Rik Bellens;Sidharta Gautama

  • An improved non-local denoising algorithm

    Bart Goossens;Hiep Luong;Aleksandra Pizurica;Wilfried Philips

  • Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification

    Xian Li;Mingli Ding;Aleksandra Pizurica

  • Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

    Junfeng Gao;Wenzhi Liao;David Nuyttens;Peter Lootens

  • The effect of Gibbs ringing artifacts on measures derived from diffusion MRI.

    Daniele Perrone;Jan Aelterman;Aleksandra Pižurica;Ben Jeurissen

  • Crack detection and inpainting for virtual restoration of paintings: The case of the Ghent Altarpiece

    B. Cornelis;T. Ruić;E. Gezels;A. Dooms

  • Wavelet-Domain Video Denoising Based on Reliability Measures

    V. Zlokolica;A. Pizurica;W. Philips

  • Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction

    Wenzhi Liao;R. Bellens;A. Pizurica;W. Philips

  • An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images

    Jing-Hao Xue;Aleksandra Pizurica;Wilfried Philips;Etienne Kerre

  • Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

    Rui Wang;Heng-Chao Li;Aleksandra Pizurica;Jun Li

  • Extending the Depth of Field in Microscopy Through Curvelet-Based Frequency-Adaptive Image Fusion

    L. Tessens;A. Ledda;A. Pizurica;W. Philips

  • Rate Allocation Algorithm for Pixel-Domain Distributed Video Coding Without Feedback Channel

    M. Morbee;J. Prades-Nebot;A. Pizurica;W. Philips

Frequent Co-Authors

Wilfried Philips
Wilfried Philips Ghent University
Wenzhi Liao
Wenzhi Liao Ghent University
Ingrid Daubechies
Ingrid Daubechies Duke University
Hongyan Zhang
Hongyan Zhang China University of Geosciences
Etienne Kerre
Etienne Kerre Ghent University
Paul Scheunders
Paul Scheunders University of Antwerp
Jan Sijbers
Jan Sijbers University of Antwerp
Peter Schelkens
Peter Schelkens Vrije Universiteit Brussel
D. De Zutter
D. De Zutter Ghent University
Niko E. C. Verhoest
Niko E. C. Verhoest Ghent University

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