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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Farouk Suleiman Chris J. Hughes E.B. Obio Pattern Recognition and Image Analysis (PRImA) Research Lab University of Salford Greater Manchester United Kingdom School of Science Engineering & Environment University of Salford Greater Manchester United Kingdom School of Science & Engineering University of Manchester Greater Manchester United Kingdom

ISBN: (纸本)9781728188577

In this paper we propose, a novel adaptative histogram matching method to remove low contrast, smeared ink, bleed-through and uneven illumination artefacts from scanned images of historical documents. The goal is to provide a better representation of document images and therefore improve readability and the source images for Optical Character recognition (OCR). Unlike other methods that are designed for single artefacts, our proposed method enhances multiple (low-contrast, smeared-ink, bleed-through and uneven illumination). The method starts by taking the bimodal peaks of the original grayscale image and multiplying them to generated gaussian windows to create an ideal histogram with weights of importance to distribution. This histogram becomes the reference histogram to be matched to the original image for a more optimized image. Median filtering is also incorporated in the method to remove salt and pepper noise. We demonstrate the technique on the European Newspapers project (ENP) dataset chosen from the pattern recognition and image analysis research lab (PRImA) and establish from the results that, the proposed method significantly reduces background noise and improves image quality on multiple artefacts as compared to other enhancement methods tested. To evaluate the efficiency of the proposed method, we make use of several performance criteria. These include Signal to Noise Ratio (SNR), Mean opinion score (MOS), and visual document image quality assessment (VDIQA) metric. The proposed method performs best in all the evaluation metrics having a 42.6 % increment on the average score of the other methods for MOS, 44.3% increment on average score of other methods for SNR and 61.11% better in VDIQA compared to other methods.

关键词： Measurement image quality Histograms Visualization Filtering Lighting Ink

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LEDNet: Deep Network for Single image Haze Removal 2018

LEDNet: Deep Network for Single Image Haze Removal

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Proceedings of the 11th Indian Conference on Computer Vision, Graphics and image Processing

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. The proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. The second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

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Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Trainable Spectrally Initializable Matrix Transformations in...

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International Conference on pattern recognition

作者： Michele Alberti Angela Botros Narayan Schutz Rolf Ingold Marcus Liwicki Mathias Seuret Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland V7 Ltd London United Kingdom ARTORG Center for Biomedical Engineering Research University of Bern Switzerland EISLAB Machine Learning Luleå University of Technology Sweden Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

In this work, we introduce a new architectural component to Neural Network (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

关键词： Program processors Discrete Fourier transforms Transforms Documentation pattern recognition Discrete cosine transforms Convolutional neural networks

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ICDAR2019 competition on recognition of early Indian printed documents-REID2019 15

ICDAR2019 competition on recognition of early Indian printed...

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15th IAPR International Conference on Document analysis and recognition, ICDAR 2019

作者： Clausner, Christian Antonacopoulos, Apostolos Derrick, Tom Pletschacher, Stefan Pattern Recognition and Image Analysis Research Lab School of Computing Science and Engineering University of Salford Greater ManchesterM5 4WT United Kingdom Digital Scholarship British Library LondonNW1 2DB United Kingdom

ISBN: (纸本)9781728128610

This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script. It describes the competition rules, dataset, and evaluation methodology. Results are presented for five methods-three submit-ted, one re-run, and one open source state-of-the-art system. The focus is on optical character recognition (OCR) performance. Different evaluation metrics were used to gain an in-sight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that deep learning approaches are promising, but there are still significant challenges for historic material of this nature. © 2019 IEEE.

关键词： Optical character recognition

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Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels

arXiv

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arXiv 2020年

作者： Bertram, Christof A. Veta, Mitko Marzahl, Christian Stathonikos, Nikolas Maier, Andreas Klopfleisch, Robert Aubreville, Marc Institute of Veterinary Pathology Freie Universitt Berlin Berlin Germany Medical Image Analysis Group Eindhoven University of Technology Eindhoven Netherlands Pattern Recognition Lab Computer Science Friedrich-Alexander-Universitt Erlangen-Nrnberg Erlangen Germany Department of Pathology University Medical Center Utrecht Utrecht Netherlands

Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge. Additional to manual mitotic figure screening, we used a novel, algorithm-aided labeling process, that allowed to minimize the risk of missing rare mitotic figures in the images. All potential mitotic figures were independently assessed by two pathologists. The novel, publicly available set of labels contains 1,999 mitotic figures (+28.80%) and additionally includes 10,483 labels of cells with high similarities to mitotic figures (hard examples). We found significant difference comparing F1 scores between the original label set (0.549) and the new alternative label set (0.735) using a standard deep learning object detection architecture. The models trained on the alternative set showed higher overall confidence values, suggesting a higher overall label consistency. Findings of the present study show that pathologists-defined labels may vary significantly resulting in notable difference in the model performance. Comparison of deep learning-based algorithms between independent datasets with different labeling methods should be done with caution. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A Novel Baseline Estimation Technique for Geometric Correction of Historical Arabic Documents Based on Voronoi Diagrams

A Novel Baseline Estimation Technique for Geometric Correcti...

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International Conference on pattern recognition Systems

作者： Ali Dulla Apostolos Antonacopoulos School of Computing Science and Engineering University of Salford Greater Manchester MS 4WT United Kingdom Pattern Recognition and Image Analysis (PRImA) Research Lab School of Computing Science and Engineering University of Salford United Kingdom

ISBN: (数字)9781839531088

Since Arabic writing has a robust baseline, several state-of-the-art recognition systems for handwritten Arabic produce use of baseline-dependent characteristics. For modem Arabic documents, the baseline can be detected reliably by obtaining the maximum in the horizontal projection profile or the Hough transformed image. However, the performance of these techniques leaks significantly on Historical Arabic Documents. In this paper, we introduce an effective novel approach to baseline detection in Historical Arabic Documents which is based on Based on Voronoi Diagrams. The proposed technique is carried out, verified and validated on a dataset of Warped Historical Arabic Documents based on affecting by warping percentage.

关键词： Historical documents VD Baseline estimation Arbitrary warping Geometric correction

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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labeling, Cutting, Grouping: An Efficient Text Line Segmentation Method for Medieval Manuscripts

Labeling, Cutting, Grouping: An Efficient Text Line Segmenta...

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International Conference on Document analysis and recognition

作者： Michele Alberti Lars Vögtlin Vinaychandran Pondenkandath Mathias Seuret Rolf Ingold Marcus Liwicki Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland University of Fribourg Fribourg Switzerland Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Machine Learning Group Luleå University of Technology Sweden

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.

关键词： Semantics Task analysis image segmentation Degradation Layout image color analysis Noise reduction

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Mitosis domain generalization in histopathology images - The MIDOG challenge

arXiv

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arXiv 2022年

作者： Aubreville, Marc Stathonikos, Nikolas Bertram, Christof A. Klopfleisch, Robert ter Hoeve, Natalie Ciompi, Francesco Wilm, Frauke Marzahl, Christian Donovan, Taryn A. Maier, Andreas Breen, Jack Ravikumar, Nishant Chung, Youjin Park, Jinah Nateghi, Ramin Pourakpour, Fattaneh Fick, Rutger H.J. Hadj, Saima Ben Jahanifar, Mostafa Rajpoot, Nasir Dexl, Jakob Wittenberg, Thomas Kondo, Satoshi Lafarge, Maxime W. Koelzer, Viktor H. Liang, Jingtang Wang, Yubo Long, Xi Liu, Jingxin Razavi, Salar Khademi, April Yang, Sen Wang, Xiyue Veta, Mitko Breininger, Katharina Technische Hochschule Ingolstadt Ingolstadt Germany Pathology Department UMC Utrecht Netherlands Institute of Pathology University of Veterinary Medicine Vienna Austria Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Computational Pathology Group Radboud UMC Nijmegen Netherlands Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Anatomic Pathology Schwarzman Animal Medical Center New York United States CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds United Kingdom Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Electrical and Electronics Engineering Department Shiraz University of Technology Shiraz Iran Tehran Iran Tribun Health Paris France Tissue Image Analytics Centre Department of Computer Science University of Warwick United Kingdom Fraunhofer-Institute for Integrated Circuits IIS Erlangen Germany Muroran Institute of Technology Hokkaido Japan Department of Pathology and Molecular Pathology University Hospital University of Zurich Zurich Switzerland School of Life Science and Technology Xidian University Shannxi China Histo Pathology Diagnostic Center Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Tencent AI Lab Shenzhen518057 China College of Computer Science Sichuan University Chengdu610065 China Medical Image Analysis Group TU Eindhoven Netherlands Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Author Correction: A comprehensive multi-domain dataset for mitotic figure detection

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Scientific data 2024年第1期11卷 717页

作者： Marc Aubreville Frauke Wilm Nikolas Stathonikos Katharina Breininger Taryn A Donovan Samir Jabari Mitko Veta Jonathan Ganz Jonas Ammeling Paul J van Diest Robert Klopfleisch Christof A Bertram Technische Hochschule Ingolstadt Ingolstadt Germany. marc.aubreville@thi.de. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Department of Pathology University Medical Center Utrecht Utrecht The Netherlands. Schwarzman Animal Medical Center New York USA. Department of Neuropathology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Medical Image Analysis Group Eindhoven University of Technology Eindhoven the Netherlands. Technische Hochschule Ingolstadt Ingolstadt Germany. Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany. Institute of Pathology University of Veterinary Medicine Vienna Vienna Austria.

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