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22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

作者： Pellet, Jean-Philippe Elisseeff, André Pattern Recognition and Machine Learning Group Swiss Federal Institute of Technology Zurich 8092 Zurich Switzerland Data Analytics Group IBM Research GmbH 8803 Ruschlikon Switzerland

ISBN: (纸本)9781605609492

Causal structure-discovery techniques usually assume that all causes of more than one variable are observed. This is the so-called causal sufficiency assumption. In practice, it is untestable, and often violated. In this paper, we present an efficient causal structure-learning algorithm, suited for causally insufficient data. Similar to algorithms such as IC* and FCI, the proposed approach drops the causal sufficiency assumption and learns a structure that indicates (potential) latent causes for pairs of observed variables. Assuming a constant local density of the data-generating graph, our algorithm makes a quadratic number of conditional-independence tests w.r.t. the number of variables. We show with experiments that our algorithm is comparable to the state-of-the-art FCI algorithm in accuracy, while being several orders of magnitude faster on large problems. We conclude that MBCS* makes a new range of causally insufficient problems computationally tractable.

关键词： learning algorithms

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Labeling, cutting, grouping: An efficient text line segmentation method for medieval manuscripts 15

Labeling, cutting, grouping: An efficient text line segmenta...

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

作者： Alberti, Michele Vogtlin, Lars Pondenkandath, Vinaychandran Seuret, Mathias Ingold, Rolf Liwicki, Marcus University of Fribourg Switzerland Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Machine Learning Group Luleå University of Technology Sweden

ISBN: (纸本)9781728128610

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. © 2019 IEEE.

关键词： Semantic Segmentation

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DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

DNLM-MA-P: A Parallelization of the Deceived Non Local Means...

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2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

作者： Calderon, Saul Castro, Jorge Zurnbado, Manuel Pattern Recognition and Machine Learning Group School of Computing Costa Rica Institute of Technology Costa Rica Advanced Computing Laboratory Costa Rica National High Technology Center Costa Rica

ISBN: (纸本)9781538675069

This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x. © 2018 IEEE.

关键词： Image denoising

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A first glance on the enhancement of digital cell activity videos from glioblastoma cells with nuclear staining 36

A first glance on the enhancement of digital cell activity v...

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36th IEEE Central American and Panama Convention, CONCAPAN 2016

作者： Calderon, Saul Moya, Daniel Cruz, Juan Carlos Valverde, Juan Manuel Computing Engineering PARMA-Group: PAttern Recognition and MAchine Learning Group Instituto Tecnológico de Costa Rica Cartago Costa Rica Faculty of Microbiology Universidad de Costa Rica San José Costa Rica

ISBN: (纸本)9781467395786

In this work we explore a set of image enhancement techniques for improving contrast and removing noise from digital images of cell activity. The cells studied were extracted from cancerous brain tissue and exposed to different chemo-therapeutic agents, as microbiologists aim to analyze the behavior of cells exposed to different chemo-therapies. To ease and improve the precision of such analysis, an automatic cell tracking framework is of great interest. Thus, in this work we focus on the first stage of such framework, which refers to image preprocessing, aiming to noise removal and contrast enhancement, in order to improve cell segmentation and tracking performance. We compared the segmentation precision using different image preprocessing techniques based on the Deceived Weighting Average Framework (DeWAFF), reaching improvements of around 15 percent of segmentation accuracy, over no preprocessed images. © 2016 IEEE.

关键词： Cells

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DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

DNLM-MA-P: A Parallelization of the Deceived Non Local Means...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： Saul Calderon Jorge Castro Manuel Zurnbado Pattern Recognition and Machine Learning Group Costa Rica National High Technology Center

关键词： Optimization Microsoft Windows Graphics processing units Computer architecture Kernel Instruction sets Time complexity

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A survey of historical document image datasets

A survey of historical document image datasets

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作者： Nikolaidou, Konstantina Seuret, Mathias Mokayed, Hamam Liwicki, Marcus EISLAB Machine Learning Group Luleå University of Technology Aurorum 1 Norrbotten Luleå97187 Sweden Pattern Recognition Lab Computer Vision Group Friedrich-Alexander-Universität Martensstr. 3 Bavaria Erlangen91058 Germany

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies. © 2022, The Author(s).

关键词： Deterioration

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Simple Graph Comparison Inspired on Metabolic Pathway Correlation

Simple Graph Comparison Inspired on Metabolic Pathway Correl...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： Esteban Arias-Mendez Alonso Montero-Marin Danny Chaves-Chaves Francisco J. Torres-Rojas PAttern Recognition and Machine Learning (PARMA) Group The Happy Few Cartago Costa Rica Ingenieria en Computacion School of Computing

Comparing two graphs is a computationally difficult task [9], [8]. After a work by E. Arias-Mendez and F. Torres-Rojas [7] about the correlation of metabolic pathways with two new proposed approaches to simplify the comparison of its associated graph representation, we extended this work to general graph structures as a simple way to compare them. The approach presented here is an extension of those algorithms to general graphs. The first algorithm proposed looks to transform the comparing graphs into linear sequences, to be analyzed using sequence-alignment tools from bioinformatics and get a numeric score as its value of similitude. The second proposed algorithm consists of the search of equal connected nodes between 2 graphs to eliminate then on both structures, only leaving the differences, as heuristic for comparison. These algorithms were developed as a low-cost process to correlate metabolic pathways showing good results; the suggestion is to use this information as a previous analysis to a deeper, more expensive, comparing tools use. Here we review the extension of this work as an application to a more general graph data structure. These methods have shown to be an effective way to treat the problem as listed in the results section.

关键词： Finishing Tools Correlation Data structures Two dimensional displays Discrete Fourier transforms Heuristic algorithms

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Network-guided group feature selection for classification of autism spectrum disorder

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8679卷 190-197页

作者： Cheplygina, Veronika Tax, David M.J. Loog, Marco Feragen, Aasa Pattern Recognition Laboratory Delft University of Technology Netherlands The Image Group University of Copenhagen Denmark Machine Learning and Computational Biology Group Max Planck Institutes Tübingen Germany

We present an anatomically guided feature selection scheme for prediction of neurological disorders based on brain connectivity networks. Using anatomical information not only gives rise to an interpretable model, but also prevents overfitting, caused by high dimensionality, noise and correlated features. Our method selects meaningful and discriminative groups of connections between anatomical regions, which can be used as input for any supervised classifier, such as logistic regression or a support vector machine. We demonstrate the effectiveness of our method on a dataset of autism spectrum disorder, with an AUC of 0.76, outperforming baseline methods. © Springer International Publishing Switzerland 2014.

关键词： Feature Selection

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Automatic Calibration of the Deceived Non Local Means Filter for Improving the Segmentation of Cells in Fluorescence Based Microscopy

Automatic Calibration of the Deceived Non Local Means Filter...

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International Conference on Biomedical Engineering and Applications (ICBEA)

作者： S. Calderón J. Barrantes J. Schuster M. Mendez J. Begera PAttern Recognition and MAchine Learning Group (PARMA-Group) Costa Rican Institute of Technology Computing School Institute of Experimental and Applied Physics Czech Technical University in Prague Cartago Costa Rica

ISBN: (纸本)9781538680599

This paper presents an automatic approach for optimal calibration of the deceived non local means filter (DNLM), for enhancing segmentation accuracy of fluorescence based microscopy images. The DNLM is designed for image denoising and enhancement. The calibration of its parameters in real image preprocessing applications is very often time consuming, since doing a manual calibration in a sample image might not work in different image samples. We compared three different stochastic optimization approaches: simulated annealing, particle swarm optimization and genetic algorithms, and selected the best approach. The implemented solution needs from the user only to define a precision metric and a set of image samples, and the algorithm will arrive to a locally optimal set of the filter parameters, to improve segmentation accuracy, using Otsu thresholding and measured with the Dice index. The PSO approach presented the overall best performance, with an average Dice index of 0.9667 in the validation set, a two percent boost over the best manually calibrated set of parameters for the DNLM.

关键词： calibration genetic algorithms image denoising image filtering image segmentation medical image processing particle swarm optimisation simulated annealing image samples image denoising Particle swarm optimization image filtering Medical image processing Genetic Algorithm (GA) simulated annealing automatic calibration image segmentation filter parameters Fluorescence Microscopy Calibration

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Labeling, cutting, grouping: An efficient text line segmentation method for medieval manuscripts

arXiv

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arXiv 2019年

作者： Alberti, Michele Vögtlin, Lars Pondenkandath, Vinaychandran Seurety, Mathias Ingold, Rolf Liwicki, Marcus University of 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, bleedthrough, 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 highquality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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