Lika, M.[Miroslav]
* 2001: Material Identification Using Laser Spectroscopy and Pattern Recognition Algorithms
Likamwa, R.[Robert]
* 2023: Software-Defined Imaging: A Survey
Likar, B.[Bostjan]
* 2000: Comparison of Retrospective Shading Correction Techniques, A
* 2001: hierarchical approach to elastic registration based on mutual information, A
* 2003: 3-D/2-D registration of CT and MR to X-ray images
* 2003: Real-Time automated visual inspection of color tablets in pharmaceutical blisters
* 2004: Evaluation of nine similarity measures used in rigid registration
* 2006: 3-D/2-D Registration by Integrating 2-D Information in 3-D
* 2006: Evaluation of Similarity Measures for Non-rigid Registration
* 2006: Protocol for Evaluation of Similarity Measures for Rigid Registration, A
* 2007: Review of Methods for Correction of Intensity Inhomogeneity in MRI, A
* 2008: Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images
* 2010: Automated Tracking of Vesicles in Phase Contrast Microscopy Images
* 2010: New Image Database for 3D/2D Registration Based on the Visible Human Data Set, A
* 2011: Image registration for visual inspection of imprinted pharmaceutical tablets
* 2011: Real-time image segmentation for visual inspection of pharmaceutical tablets
* 2012: Game-Theoretic Framework for Landmark-Based Image Segmentation, A
* 2012: Groupwise Registration of Multimodal Images by an Efficient Joint Entropy Minimization Scheme
* 2012: Quad-tree Based Entropy Estimator for Fast and Robust Brain Image Registration
* 2013: 3D-2D Registration of Cerebral Angiograms: A Method and Evaluation on Clinical Images
* 2013: Automated visual inspection of imprint quality of pharmaceutical tablets
* 2013: Robust Mixture-Parameter Estimation for Unsupervised Segmentation of Brain MR Images
* 2014: Fast and Robust 3D to 2D Image Registration by Backprojection of Gradient Covariances
* 2014: Shape Representation for Efficient Landmark-Based Segmentation in 3-D
* 2015: Automated visual inspection of pharmaceutical tablets in heavily cluttered dynamic environments
* 2015: Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation, A
* 2015: Pose estimation of textureless objects in cluttered environments
* 2015: Real-time pose estimation of rigid objects in heavily cluttered environments
* 2015: Robust Estimation of Unbalanced Mixture Models on Samples with Outliers
* 2016: Automatic cutting plane identification for computer-aided analysis of intracranial aneurysms
* 2016: Computer-Assisted Screw Size and Insertion Trajectory Planning for Pedicle Screw Placement Surgery
* 2016: Enhancement of Vascular Structures in 3D and 2D Angiographic Images
* 2016: Print registration for automated visual inspection of transparent pharmaceutical capsules
* 2017: 2D Sub-pixel Point Spread Function Measurement Using a Virtual Point-Like Source
* 2017: In-line recognition of agglomerated pharmaceutical pellets with density-based clustering and convolutional neural network
* 2017: Model based visual inspection of pharmaceutical tablets with photometric stereo
* 2017: Segmentation of Pathological Structures by Landmark-Assisted Deformable Models
Includes: Likar, B.[Bostjan] Likar, B. Likar, B.[Boštjan]
35 for Likar, B.
Likas, A.[Aristidis]
* 2003: variational method for bayesian blind image deconvolution, A
* 2004: Probabilistic shape-based image indexing and retrieval
* 2005: Mixture model analysis of DNA microarray images
* 2007: Bayesian Kernel Methods for Analysis of Functional Neuroimages
* 2007: Rigid Image Registration based on Pixel Grouping
* 2007: Robust Image Registration using Mixtures of t-distributions
* 2007: Variational Bayesian Blind Image Deconvolution with Student-T Priors
* 2009: mixtures of Student's t-distributions as a robust framework for rigid registration, The
* 2009: Probabilistic relevance feedback approach for content-based image retrieval based on gaussian mixture models
* 2009: Variational Bayesian Sparse Kernel-Based Blind Image Deconvolution With Student's-t Priors
* 2010: Bayesian Framework for Image Segmentation With Spatially Varying Mixtures, A
* 2010: Identifying touching and overlapping chromosomes using the watershed transform and gradient paths
* 2011: Relevance feedback approach for image retrieval combining support vector machines and adapted gaussian mixture models
* 2011: Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
* 2012: Fast and efficient vanishing point detection in indoor images
* 2014: Global sampling of image edges
* 2014: MinMax k-Means clustering algorithm, The
* 2015: Elimination of Outliers from 2-D Point Sets Using the Helmholtz Principle
* 2015: Visual tracking using spatially weighted likelihood of Gaussian mixtures
* 2017: Real time visual tracking using a spatially weighted von Mises mixture model
* 2018: Multi-Threshold LIP Contour Detection
* 2022: UU-test for statistical modeling of unimodal data, The
Includes: Likas, A.[Aristidis] Likas, A.
22 for Likas, A.
Likas, A.C.
* 2003: Class conditional density estimation using mixtures with constrained component sharing
* 2003: global k-means clustering algorithm, The
* 2004: clustering method based on boosting, A
* 2004: Non stationary Bayesian image restoration
* 2005: Maximum a Posteriori Image Restoration Based on a New Directional Continuous Edge Image Prior
* 2006: Bayesian Feature and Model Selection for Gaussian Mixture Models
* 2006: Bayesian Restoration Using a New Nonstationary Edge-Preserving Image Prior
* 2007: Class-Adaptive Spatially Variant Mixture Model for Image Segmentation, A
* 2007: Image Modeling and Segmentation Using Incremental Bayesian Mixture Models
* 2008: Variational Bayesian Image Restoration Based on a Product of t-Distributions Image Prior
* 2009: Movie segmentation into scenes and chapters using locally weighted bag of visual words
* 2009: Scene Detection in Videos Using Shot Clustering and Sequence Alignment
* 2009: Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines
* 2014: Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering
* 2016: Weighted multi-view key-frame extraction
Includes: Likas, A.C. Likas, A.C.[Aristidis C.]
15 for Likas, A.C.