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ORCIDKees Joost Batenburg
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- affiliation: Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherland
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2020 – today
- 2024
[j62]Serban Vadineanu
, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. J. Imaging 10(7): 172 (2024)- [j61]Ajinkya Kadu, Felix Lucka, Kees Joost Batenburg:
Single-Shot Tomography of Discrete Dynamic Objects. IEEE Trans. Computational Imaging 10: 941-952 (2024) - [i32]Jiayang Shi, Junyi Zhu, Daniël Maria Pelt, Kees Joost Batenburg, Matthew B. Blaschko:
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. CoRR abs/2405.02509 (2024) - [i31]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. CoRR abs/2405.02515 (2024) - [i30]Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb, Kees Joost Batenburg, Felix Lucka:
Learned denoising with simulated and experimental low-dose CT data. CoRR abs/2408.08115 (2024) - 2023
- [j60]Francien G. Bossema, Paul J. C. van Laar, Kimberly Meechan, Daniel O'Flynn, Joanne Dyer, Tristan van Leeuwen, Suzan Meijer, Erma Hermens, Kees Joost Batenburg:
Inside out: Fusing 3D imaging modalities for the internal and external investigation of multi-material museum objects. Digit. Appl. Archaeol. Cult. Heritage 31: 00296 (2023) - [j59]Richard Arnoud Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg:
Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels. IEEE Trans. Evol. Comput. 27(3): 550-564 (2023) - [c36]Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. MILLanD@MICCAI 2023: 3-13 - [c35]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. MLMI@MICCAI (1) 2023: 52-61 - [i29]Timothy M. Craig, Ajinkya Kadu, Kees Joost Batenburg, Sara Bals:
Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D. CoRR abs/2304.01221 (2023) - [i28]Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg:
Quantifying the effect of X-ray scattering for data generation in real-time defect detection. CoRR abs/2305.12822 (2023) - [i27]Maximilian B. Kiss, Sophia Bethany Coban, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka:
2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning. CoRR abs/2306.05907 (2023) - [i26]Jiayang Shi, Daniël Maria Pelt, Kees Joost Batenburg:
Multi-stage Deep Learning Artifact Reduction for Computed Tomography. CoRR abs/2309.00494 (2023) - [i25]Ajinkya Kadu, Felix Lucka, Kees Joost Batenburg:
Single-shot Tomography of Discrete Dynamic Objects. CoRR abs/2311.05269 (2023) - 2022
- [j58]Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg:
A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Syst. Appl. 206: 117768 (2022) - [j57]Dirk Elias Schut, Kees Joost Batenburg, Robert van Liere, Tristan van Leeuwen:
TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography. IEEE Trans. Computational Imaging 8: 598-608 (2022) - [j56]Poulami Somanya Ganguly, Felix Lucka, Holger Kohr, Erik Franken, Hermen Jan Hupkes, Kees Joost Batenburg:
SparseAlign: A Grid-Free Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography. IEEE Trans. Computational Imaging 8: 651-665 (2022) - [c34]Serban Vadineanu, Daniël Maria Pelt, Oleh Dzyubachyk, Kees Joost Batenburg:
An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. MIDL 2022: 1251-1267 - [c33]Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg:
Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning. PMBS@SC 2022: 48-59 - [i24]Poulami Somanya Ganguly, Felix Lucka, Holger Kohr, Erik Franken, Hermen Jan Hupkes, Kees Joost Batenburg:
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography. CoRR abs/2201.08706 (2022) - [i23]Mathé T. Zeegers, Tristan van Leeuwen, Daniël Maria Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg:
A tomographic workflow to enable deep learning for X-ray based foreign object detection. CoRR abs/2201.12184 (2022) - [i22]Patrick Echtenbruck, Martina Echtenbruck, Kees Joost Batenburg, Thomas Bäck, Boris Naujoks, Michael Emmerich:
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications. CoRR abs/2206.11263 (2022) - [i21]Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg:
Benchmarking optimization algorithms for auto-tuning GPU kernels. CoRR abs/2210.01465 (2022) - [i20]Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg:
Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning. CoRR abs/2211.07260 (2022) - 2021
- [j55]Jordi Minnema, Jan Wolff, Juha Koivisto, Felix Lucka, Kees Joost Batenburg, Tymour Forouzanfar, Maureen van Eijnatten:
Comparison of convolutional neural network training strategies for cone-beam CT image segmentation. Comput. Methods Programs Biomed. 207: 106192 (2021) - [j54]Maureen van Eijnatten, Leonardo Rundo, Kees Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek:
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Comput. Methods Programs Biomed. 208: 106261 (2021) - [j53]Johannes Leuschner, Maximilian Schmidt, Poulami Somanya Ganguly, Vladyslav Andriiashen, Sophia Bethany Coban, Alexander Denker, Dominik F. Bauer, Amir Hadjifaradji, Kees Joost Batenburg, Peter Maass, Maureen van Eijnatten:
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. J. Imaging 7(3): 44 (2021) - [j52]Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg:
Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry. J. Imaging 7(7): 104 (2021) - [j51]Dzemila Sero, Isabelle Garachon, Erma Hermens, Robert van Liere, Kees Joost Batenburg:
The Study of Three-Dimensional Fingerprint Recognition in Cultural Heritage: Trends and Challenges. ACM Journal on Computing and Cultural Heritage 14(4): 51:1-51:20 (2021) - [j50]Marinus J. Lagerwerf, Allard A. Hendriksen, Jan-Willem Buurlage, Kees Joost Batenburg:
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography. Mach. Learn. Sci. Technol. 2(1): 15012 (2021) - [i19]Poulami Somanya Ganguly, Daniël Maria Pelt, Doga Gürsoy, Francesco De Carlo, Kees Joost Batenburg:
Improving reproducibility in synchrotron tomography using implementation-adapted filters. CoRR abs/2103.08288 (2021) - [i18]Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg:
Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry. CoRR abs/2104.05326 (2021) - [i17]Mathé T. Zeegers, Ajinkya Kadu, Tristan van Leeuwen, Kees Joost Batenburg:
ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography. CoRR abs/2112.11406 (2021) - 2020
- [j49]Marinus J. Lagerwerf, Willem Jan Palenstijn, Folkert Bleichrodt, Kees Joost Batenburg:
An Efficient Interpolation Approach for Exploring the Parameter Space of Regularized Tomography Algorithms. Fundam. Informaticae 172(2): 143-167 (2020) - [j48]Sophia Bethany Coban, Felix Lucka, Willem Jan Palenstijn, Denis Van Loo, Kees Joost Batenburg:
Explorative Imaging and Its Implementation at the FleX-ray Laboratory. J. Imaging 6(4): 18 (2020) - [j47]Mathé T. Zeegers, Daniël Maria Pelt, Tristan van Leeuwen, Robert van Liere, Kees Joost Batenburg:
Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. J. Imaging 6(12): 132 (2020) - [j46]Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg:
A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. J. Imaging 6(12): 135 (2020) - [j45]Nicola Viganò, Felix Lucka, Ombeline de La Rochefoucauld, Sophia Bethany Coban, Robert van Liere, Marta Fajardo, Philippe Zeitoun, Kees Joost Batenburg:
Emulation of X-ray Light-Field Cameras. J. Imaging 6(12): 138 (2020) - [j44]A. Kostenko, Willem Jan Palenstijn, Sophia Bethany Coban, Allard A. Hendriksen, Robert van Liere, Kees Joost Batenburg:
Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11: 100364 (2020) - [j43]Marinus J. Lagerwerf, Willem Jan Palenstijn, Holger Kohr, Kees Joost Batenburg:
Automated FDK-Filter Selection for Cone-Beam CT in Research Environments. IEEE Trans. Computational Imaging 6: 739-748 (2020) - [j42]Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Trans. Computational Imaging 6: 1320-1335 (2020) - [c32]Poulami Somanya Ganguly, Felix Lucka, Hermen Jan Hupkes, Kees Joost Batenburg:
Atomic Super-Resolution Tomography. IWCIA 2020: 45-61 - [c31]Richard Schoonhoven, Jan-Willem Buurlage, Daniël Maria Pelt, Kees Joost Batenburg:
Real-time segmentation for tomographic imaging. MLSP 2020: 1-6 - [c30]Jan-Willem Buurlage, Rob H. Bisseling, Willem Jan Palenstijn, Kees Joost Batenburg:
A projection-based data partitioning method for distributed tomographic reconstruction. PP 2020: 58-68 - [i16]Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging. CoRR abs/2001.11801 (2020) - [i15]Poulami Somanya Ganguly, Felix Lucka, Hermen Jan Hupkes, Kees Joost Batenburg:
Atomic Super-Resolution Tomography. CoRR abs/2002.00710 (2020) - [i14]Maureen van Eijnatten, Leonardo Rundo, Kees Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek:
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. CoRR abs/2005.07545 (2020) - [i13]Georgios Pilikos, Lars Horchens, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka:
Fast ultrasonic imaging using end-to-end deep learning. CoRR abs/2009.02194 (2020) - [i12]Georgios Pilikos, Lars Horchens, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka:
Deep data compression for approximate ultrasonic image formation. CoRR abs/2009.02293 (2020) - [i11]Marinus J. Lagerwerf, Daniël Maria Pelt, Willem Jan Palenstijn, Kees Joost Batenburg:
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks. CoRR abs/2010.00421 (2020) - [i10]Richard Schoonhoven, Allard A. Hendriksen, Daniël Maria Pelt, Kees Joost Batenburg:
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains. CoRR abs/2011.06923 (2020) - [i9]Ajinkya Kadu, Tristan van Leeuwen, Kees Joost Batenburg:
CoShaRP: A Convex Program for Single-shot Tomographic Shape Sensing. CoRR abs/2012.04551 (2020) - [i8]Sophia Bethany Coban, Vladyslav Andriiashen, Poulami Somanya Ganguly, Maureen van Eijnatten, Kees Joost Batenburg:
Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning. CoRR abs/2012.13346 (2020)
2010 – 2019
- 2019
- [j41]Jan-Willem Buurlage, Rob H. Bisseling, Kees Joost Batenburg:
A geometric partitioning method for distributed tomographic reconstruction. Parallel Comput. 81: 104-121 (2019) - [i7]Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg:
A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning. CoRR abs/1905.04787 (2019) - 2018
- [j40]Henri Der Sarkissian, Nicola Viganò, Kees Joost Batenburg:
A Data Consistent Variational Segmentation Approach Suitable for Real-time Tomography. Fundam. Informaticae 163(1): 1-20 (2018) - [j39]Daniël Maria Pelt, Kees Joost Batenburg, James A. Sethian:
Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. J. Imaging 4(11): 128 (2018) - [c29]Mathé T. Zeegers, Felix Lucka, Kees Joost Batenburg:
A Multi-channel DART Algorithm. IWCIA 2018: 164-178 - [i6]Mathé T. Zeegers, Felix Lucka, Kees Joost Batenburg:
A Multi-channel DART Algorithm. CoRR abs/1808.09170 (2018) - [i5]Kees Joost Batenburg, Robert van Liere:
Computational 3D X-ray Imaging for the Food Industry. ERCIM News 2018(113) (2018) - 2017
- [j38]Daniil Kazantsev, Folkert Bleichrodt, Tristan van Leeuwen, Anders Kaestner, Philip J. Withers, Kees Joost Batenburg, Peter D. Lee:
A Novel Tomographic Reconstruction Method Based on the Robust Student's t Function For Suppressing Data Outliers. IEEE Trans. Computational Imaging 3(4): 682-693 (2017) - [c28]Axel Ringh, Xiaodong Zhuge, Willem Jan Palenstijn, Kees Joost Batenburg, Ozan Öktem:
High-Level Algorithm Prototyping: An Example Extending the TVR-DART Algorithm. DGCI 2017: 109-121 - [c27]Ajinkya Kadu, Tristan van Leeuwen, Kees Joost Batenburg:
A Parametric Level-Set Method for Partially Discrete Tomography. DGCI 2017: 122-134 - [i4]Ajinkya Kadu, Tristan van Leeuwen, Kees Joost Batenburg:
A parametric level-set method for partially discrete tomography. CoRR abs/1704.00568 (2017) - [i3]Kees Joost Batenburg, Tamás Szirányi:
Computational Imaging - Introduction to the Special Theme. ERCIM News 2017(108) (2017) - 2016
- [j37]Nicola Viganò, Kees Joost Batenburg, Wolfgang Ludwig:
An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography. Fundam. Informaticae 146(2): 219-230 (2016) - [j36]Folkert Bleichrodt, Tristan van Leeuwen, Willem Jan Palenstijn, Wim van Aarle, Jan Sijbers, Kees Joost Batenburg:
Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators. Numer. Algorithms 71(3): 673-697 (2016) - [j35]Xiaodong Zhuge, Willem Jan Palenstijn, Kees Joost Batenburg:
TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation. IEEE Trans. Image Process. 25(1): 455-468 (2016) - [c26]Kees Joost Batenburg, Leon S. Helwerda, Walter A. Kosters, Tim van der Meij:
Mobile Radio Tomography: Agent-Based Imaging. BNCAI 2016: 63-77 - [i2]Daniël Maria Pelt, Kees Joost Batenburg:
A method for locally approximating regularized iterative tomographic reconstruction methods. CoRR abs/1604.02292 (2016) - 2015
- [j34]Wagner Fortes, Kees Joost Batenburg:
Quality bounds for binary tomography with arbitrary projection matrices. Discret. Appl. Math. 183: 42-58 (2015) - [j33]Linda Plantagie, Kees Joost Batenburg:
Algebraic filter approach for fast approximation of nonlinear tomographic reconstruction methods. J. Electronic Imaging 24(1): 013026 (2015) - [j32]Geert Van Eyndhoven, Kees Joost Batenburg, Daniil Kazantsev, Vincent Van Nieuwenhove, Peter D. Lee, Katherine J. Dobson, Jan Sijbers:
An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging. IEEE Trans. Image Process. 24(11): 4446-4458 (2015) - [c25]Geert Van Eyndhoven, Kees Joost Batenburg, Jan Sijbers:
Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT. ICIP 2015: 113-117 - [c24]Linda Plantagie, Wim van Aarle, Jan Sijbers, Kees Joost Batenburg:
Filtered backprojection using algebraic filters; application to biomedical micro-CT data. ISBI 2015: 1596-1599 - 2014
- [j31]Tom Roelandts, Kees Joost Batenburg, Arnold J. den Dekker, Jan Sijbers:
The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography. Comput. Vis. Image Underst. 126: 28-37 (2014) - [j30]Folkert Bleichrodt, Frank Tabak, Kees Joost Batenburg:
SDART: An algorithm for discrete tomography from noisy projections. Comput. Vis. Image Underst. 129: 63-74 (2014) - [j29]Linda Plantagie, Kees Joost Batenburg:
Approximating Algebraic Tomography Methods by Filtered Backprojection: A Local Filter Approach. Fundam. Informaticae 135(1-2): 1-19 (2014) - [j28]Folkert Bleichrodt, Jan De Beenhouwer, Jan Sijbers, Kees Joost Batenburg:
Aligning Projection Images from Binary Volumes. Fundam. Informaticae 135(1-2): 21-42 (2014) - [j27]Geert Van Eyndhoven, Kees Joost Batenburg, Jan Sijbers:
Region-Based Iterative Reconstruction of Structurally Changing Objects in CT. IEEE Trans. Image Process. 23(2): 909-919 (2014) - [j26]Wim van Aarle, Kees Joost Batenburg, Gert Van Gompel, Elke Van de Casteele, Jan Sijbers:
Super-Resolution for Computed Tomography Based on Discrete Tomography. IEEE Trans. Image Process. 23(3): 1181-1193 (2014) - [j25]Daniël Maria Pelt, Kees Joost Batenburg:
Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering. IEEE Trans. Image Process. 23(11): 4750-4762 (2014) - [c23]Tristan van Leeuwen, Kees Joost Batenburg:
Adaptive Grid Refinement for Discrete Tomography. DGCI 2014: 297-308 - 2013
- [j24]Kees Joost Batenburg, Willem Jan Palenstijn, Péter Balázs, Jan Sijbers:
Dynamic angle selection in binary tomography. Comput. Vis. Image Underst. 117(4): 306-318 (2013) - [j23]Kees Joost Batenburg, Wagner Fortes, Lajos Hajdu, Robert Tijdeman:
Bounds on the quality of reconstructed images in binary tomography. Discret. Appl. Math. 161(15): 2236-2251 (2013) - [j22]Hilde Segers, Willem Jan Palenstijn, Kees Joost Batenburg, Jan Sijbers:
Discrete Tomography in MRI: a Simulation Study. Fundam. Informaticae 125(3-4): 223-237 (2013) - [j21]Kees Joost Batenburg, Wagner Fortes, Robert Tijdeman:
Approximate Discrete Reconstruction Algorithm. Fundam. Informaticae 125(3-4): 239-259 (2013) - [j20]Karim Zarei Zefreh, Wim van Aarle, Kees Joost Batenburg, Jan Sijbers:
Discrete algebraic reconstruction technique: a new approach for superresolution reconstruction of license plates. J. Electronic Imaging 22(4): 041111 (2013) - [j19]Daniël Maria Pelt, Kees Joost Batenburg:
Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. IEEE Trans. Image Process. 22(12): 5238-5251 (2013) - [c22]Wagner Fortes, Kees Joost Batenburg:
A Method for Feature Detection in Binary Tomography. DGCI 2013: 372-382 - [c21]Nicola Viganò, Wolfgang Ludwig, Kees Joost Batenburg:
Discrete representation of local orientation in grains using diffraction contrast tomography. ISPA 2013: 594-599 - [c20]Folkert Bleichrodt, Kees Joost Batenburg:
Automatic Optimization of Alignment Parameters for Tomography Datasets. SCIA 2013: 489-500 - 2012
- [j18]Kees Joost Batenburg, Walter A. Kosters:
On the Difficulty of Nonograms. J. Int. Comput. Games Assoc. 35(4): 195-205 (2012) - [j17]Kees Joost Batenburg, Linda Plantagie:
Fast Approximation of Algebraic Reconstruction Methods for Tomography. IEEE Trans. Image Process. 21(8): 3648-3658 (2012) - [j16]Wim van Aarle, Kees Joost Batenburg, Jan Sijbers:
Automatic Parameter Estimation for the Discrete Algebraic Reconstruction Technique (DART). IEEE Trans. Image Process. 21(11): 4608-4621 (2012) - [c19]Geert Van Eyndhoven, Jan Sijbers, Kees Joost Batenburg:
Combined Motion Estimation and Reconstruction in Tomography. ECCV Workshops (1) 2012: 12-21 - [c18]Péter Balázs, Kees Joost Batenburg:
A Central Reconstruction Based Strategy for Selecting Projection Angles in Binary Tomography. ICIAR (1) 2012: 382-391 - 2011
- [j15]Kees Joost Batenburg, Wim van Aarle, Jan Sijbers:
A semi-automatic algorithm for grey level estimation in tomography. Pattern Recognit. Lett. 32(9): 1395-1405 (2011) - [j14]Kees Joost Batenburg, Jan Sijbers:
DART: A Practical Reconstruction Algorithm for Discrete Tomography. IEEE Trans. Image Process. 20(9): 2542-2553 (2011) - [j13]Wim van Aarle, Kees Joost Batenburg, Jan Sijbers:
Optimal Threshold Selection for Segmentation of Dense Homogeneous Objects in Tomographic Reconstructions. IEEE Trans. Medical Imaging 30(4): 980-989 (2011) - [c17]Wagner Fortes, Kees Joost Batenburg:
Error Bounds on the Reconstruction of Binary Images from Low Resolution Scans. CAIP (1) 2011: 152-160 - [c16]Kees Joost Batenburg, Wagner Fortes, Lajos Hajdu, Robert Tijdeman:
Bounds on the Difference between Reconstructions in Binary Tomography. DGCI 2011: 369-380 - [i1]Kees Joost Batenburg, Sandra Van Aert:
Three-Dimensional Reconstruction of a Nanoparticle at Atomic Resolution. ERCIM News 2011(86) (2011) - 2010
- [j12]Arjen Stolk, Kees Joost Batenburg:
An Algebraic Framework for Discrete Tomography: Revealing the Structure of Dependencies. SIAM J. Discret. Math. 24(3): 1056-1079 (2010) - [c15]Gert Van Gompel, Kees Joost Batenburg, Elke Van de Casteele, Wim van Aarle, Jan Sijbers:
A discrete tomography approach for superresolution micro-CT images: application to bone. ISBI 2010: 816-819
2000 – 2009
- 2009
- [j11]Kees Joost Batenburg, Jan Sijbers:
Generic iterative subset algorithms for discrete tomography. Discret. Appl. Math. 157(3): 438-451 (2009) - [j10]Kees Joost Batenburg, Walter A. Kosters:
Solving Nonograms by combining relaxations. Pattern Recognit. 42(8): 1672-1683 (2009) - [j9]Kees Joost Batenburg, Jan Sijbers:
Adaptive thresholding of tomograms by projection distance minimization. Pattern Recognit. 42(10): 2297-2305 (2009) - [j8]Kees Joost Batenburg, Jan Sijbers:
Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization. IEEE Trans. Medical Imaging 28(5): 676-686 (2009) - [c14]Kees Joost Batenburg, Wim van Aarle, Jan Sijbers:
Grey Level Estimation for Discrete Tomography. DGCI 2009: 517-529 - [c13]Sander van der Maar, Kees Joost Batenburg, Jan Sijbers:
Experiences with Cell-BE and GPU for Tomography. SAMOS 2009: 298-307 - 2008
- [j7]Kees Joost Batenburg:
A Network Flow Algorithm for Reconstructing Binary Images from Continuous X-rays. J. Math. Imaging Vis. 30(3): 231-248 (2008) - [j6]Kees Joost Batenburg, Antal Nagy, Maurice Nivat:
Preface. Theor. Comput. Sci. 406(1-2): 1 (2008) - [j5]Kees Joost Batenburg, Antal Nagy, Maurice Nivat:
In Memoriam Attila Kuba (1953-2006). Theor. Comput. Sci. 406(1-2): 2-7 (2008) - [c12]Kees Joost Batenburg, Jan Sijbers:
Selection of Local Thresholds for Tomogram Segmentation by Projection Distance Minimization. DGCI 2008: 380-391 - [c11]Wim van Aarle, Kees Joost Batenburg, Jan Sijbers:
Threshold Selection for Segmentation of Dense Objects in Tomograms. ISVC (1) 2008: 700-709 - [c10]Kees Joost Batenburg, Walter A. Kosters:
A Reasoning Framework for Solving Nonograms. IWCIA 2008: 372-383 - [c9]Rudolf Hanel, Kees Joost Batenburg, Steve De Backer, Paul Scheunders, Jan Sijbers:
Fast bias field reduction by localized Lloyd-Max quantization. Medical Imaging: Image Processing 2008: 69141A - 2007
- [j4]Kees Joost Batenburg:
A Network Flow Algorithm for Reconstructing Binary Images from Discrete X-rays. J. Math. Imaging Vis. 27(2): 175-191 (2007) - [c8]Kees Joost Batenburg, Jan Sijbers:
Optimal Threshold Selection for Tomogram Segmentation by Reprojection of the Reconstructed Image. CAIP 2007: 563-570 - [c7]Kees Joost Batenburg, Jan Sijbers:
Dart: A Fast Heuristic Algebraic Reconstruction Algorithm for Discrete Tomography. ICIP (4) 2007: 133-136 - [c6]Kees Joost Batenburg, Jan Sijbers:
Automatic multiple threshold scheme for segmentation of tomograms. Medical Imaging: Image Processing 2007: 65123D - 2006
- [c5]Kees Joost Batenburg:
A Network Flow Algorithm for Binary Image Reconstruction from Few Projections. DGCI 2006: 86-97 - [c4]Kees Joost Batenburg:
A Learning Classifier Approach to Tomography. ECAI 2006: 655-659 - [c3]Kees Joost Batenburg, Walter A. Kosters:
A Neural Network Approach to Real-Time Discrete Tomography. IWCIA 2006: 389-403 - 2005
- [j3]Kees Joost Batenburg:
An evolutionary algorithm for discrete tomography. Discret. Appl. Math. 151(1-3): 36-54 (2005) - [j2]Kees Joost Batenburg:
A new algorithm for 3D binary tomography. Electron. Notes Discret. Math. 20: 247-261 (2005) - [c2]Kees Joost Batenburg, Walter A. Kosters:
Neural Networks for Discrete Tomography. BNAIC 2005: 21-27 - 2004
- [c1]Kees Joost Batenburg, Willem Jan Palenstijn:
On the Reconstruction of Crystals Through Discrete Tomography. IWCIA 2004: 23-37 - 2003
- [j1]Kees Joost Batenburg:
Analysis and optimization of an algorithm for discrete tomography. Electron. Notes Discret. Math. 12: 35-46 (2003)
Coauthor Index
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