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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers (2021)

Tavares, João Manuel R. S. ; Papa, João Paulo ; González Hidalgo, Manuel

Cham :Springer International Publishing, | Cham :Springer.

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UID:

edoccha_BV047831224

Format: 1 Online-Ressource (XV, 490 p. 167 illus., 135 illus. in color).

Edition: 1st ed. 2021

ISBN: 978-3-030-93420-0

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics 12702

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93419-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93421-7

Language: English

Subjects: Computer Science

RVK:

SS 4800

Keywords: Mustererkennung ; Maschinelles Sehen ; Bildverarbeitung ; Automatische Klassifikation ; Konferenzschrift

DOI: 10.1007/978-3-030-93420-0

URL: Volltext (URL des Erstveröffentlichers)

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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings (2017)

Cardoso, M. Jorge. [editor.] ; Arbel, Tal. [editor.] ; Carneiro, Gustavo. [editor.] ; [et al.]

Cham :Springer International Publishing :

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UID:

almahu_9947397810902882

Format: XIX, 385 p. 169 illus. , online resource.

ISBN: 9783319675589

Series Statement: Lecture Notes in Computer Science, 10553

Content: This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783319675572

Language: English

Subjects: Computer Science

RVK:

SS 4800

Keywords: Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-319-67558-9

URL: http://dx.doi.org/10.1007/978-3-319-67558-9

URL: Volltext (URL des Erstveröffentlichers)

URL: Volltext (lizenzpflichtig)

URL: Cover

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers (2021)

Tavares, João Manuel R. S. ; Papa, João Paulo ; González Hidalgo, Manuel

Cham :Springer International Publishing, | Cham :Springer.

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UID:

edocfu_BV047831224

Format: 1 Online-Ressource (XV, 490 p. 167 illus., 135 illus. in color).

Edition: 1st ed. 2021

ISBN: 978-3-030-93420-0

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics 12702

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93419-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93421-7

Language: English

Subjects: Computer Science

RVK:

SS 4800

Keywords: Mustererkennung ; Maschinelles Sehen ; Bildverarbeitung ; Automatische Klassifikation ; Konferenzschrift

DOI: 10.1007/978-3-030-93420-0

URL: Volltext (URL des Erstveröffentlichers)

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers (2021)

Tavares, João Manuel R. S. ; Papa, João Paulo ; González Hidalgo, Manuel

Cham : Springer International Publishing | Cham : Springer

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UID:

b3kat_BV047831224

Format: 1 Online-Ressource (XV, 490 p. 167 illus., 135 illus. in color)

Edition: 1st ed. 2021

ISBN: 9783030934200

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics 12702

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93419-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-93421-7

Language: English

Subjects: Computer Science

RVK:

SS 4800

Keywords: Mustererkennung ; Maschinelles Sehen ; Bildverarbeitung ; Automatische Klassifikation ; Konferenzschrift

DOI: 10.1007/978-3-030-93420-0

URL: Volltext (URL des Erstveröffentlichers)

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Deep Learning and Data Labeling for Medical Applications : First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings (2016)

Carneiro, Gustavo. [editor.] ; Mateus, Diana. [editor.] ; Peter, Loïc. [editor.] ; [et al.]

Cham :Springer International Publishing :

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UID:

almahu_9947363970402882

Format: XIII, 280 p. 115 illus. , online resource.

ISBN: 9783319469768

Series Statement: Lecture Notes in Computer Science, 10008

Content: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Note: Active learning -- Semi-supervised learning -- Reinforcement learning -- Domain adaptation and transfer learning -- Crowd-sourcing annotations and fusion of labels from different sources -- Data augmentation -- Modelling of label uncertainty -- Visualization and human-computer interaction -- Image description -- Medical imaging-based diagnosis -- Medical signal-based diagnosis -- Medical image reconstruction and model selection using deep learning techniques -- Meta-heuristic techniques for fine-tuning -- Parameter in deep learning-based architectures -- Applications based on deep learning techniques.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783319469751

Language: English

DOI: 10.1007/978-3-319-46976-8

URL: http://dx.doi.org/10.1007/978-3-319-46976-8

URL: Volltext (lizenzpflichtig)

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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings (2018)

Stoyanov, Danail. [editor.] ; Taylor, Zeike. [editor.] ; Carneiro, Gustavo. [editor.] ; [et al.]

Cham :Springer International Publishing :

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UID:

almahu_9947971794302882

Format: XVII, 387 p. 159 illus. , online resource.

ISBN: 9783030008895

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11045

Content: This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Note: Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson’s Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783030008888

Additional Edition: Printed edition: ISBN 9783030008901

Language: English

DOI: 10.1007/978-3-030-00889-5

URL: https://doi.org/10.1007/978-3-030-00889-5

URL: Volltext (lizenzpflichtig)

URL: Volltext

URL: Cover

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10-13, 2021, Revised Selected Papers (2021)

Tavares, João Manuel R. S. [editor.] ; Papa, João Paulo. [editor.] ; González Hidalgo, Manuel. [editor.] ; [et al.]

Cham :Springer International Publishing :

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UID:

almahu_9949242452602882

Format: XV, 490 p. 167 illus., 135 illus. in color. , online resource.

Edition: 1st ed. 2021.

ISBN: 9783030934200

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12702

Content: This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10-13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision. .

Note: Medical Applications -- Predicting the use of Invasive Mechanical Ventilation in ICU COVID-19 patients -- A Coarse to Fine Corneal Ulcer Segmentation Approach using U-net and DexiNed in Chain -- Replacing Data Augmentation with Rotation-equivariant CNNs in Image-based Classification of Oral Cancer -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- An Evaluation of Segmentation Techniques for COVID-19 Identification in Chest X-Ray -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- Detecting Hate Speech in Cross-Lingual and Multi-Lingual Settings Using Language Agnostic Representations -- Prediction of Perception of Security Using Social Media Content -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization -- Using Particle Swarm Optimization With Gradient Descent For Parameter Learning In Convolutional Neural Networks -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- Databases -- MIGMA: The Facial Emotion Image Dataset forHuman Expression Recognition -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- Japanese Kana and Brazilian Portuguese manuscript database -- Skelibras: a Large 2d Skeleton Dataset of Dynamic Brazilian Signs -- Deep Learning -- Cricket Scene Analysis using the RetinaNet architecture. -Texture-Based Image Transformations for Improved Deep Learning Classification -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- Web Application Attacks Detection Using Deep Learning -- Less is More: Accelerating Faster Neural Networks Straight from JPEG -- Optimizing Person Re-Identification using Generated Attention Masks -- Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- Interpreting Decision Patterns in Financial Applications -- Image Processing -- Metal Artifact Reduction based on color mapping and inpainting techniques -- New Improvement in Obtaining Monogenic Phase Congruency -- Machine Learning -- Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification -- Clustering-based Partitioning of Water Distribution Networks for Leak Zone Location -- Bias Quantification for Protected Features in Pattern Classification Problems -- Regional Commodities Price Volatility Assessment Using Self-Driven Recurrent Networks -- Semi-supervised Deep Learning Based on Label Propagation in a 2D Embedded Space -- Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets -- Semi-Autogeonous (SAG) Mill Overload Forecasting -- Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments -- Computer Vision -- Generalized Conics with the Sharp Corners -- Automatic Face Mask Detection using a Hide and Seek Algorithm -- A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition -- End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation -- Forensic Analysis of Tampered Digital Photos -- COVID-19 Lung CT Images Recognition: A Feature-Based Approach -- A Topologically Consistent Color Digital Image Representation by a Single Tree.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030934194

Additional Edition: Printed edition: ISBN 9783030934217

Language: English

DOI: 10.1007/978-3-030-93420-0

URL: https://doi.org/10.1007/978-3-030-93420-0

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Bio-Computation and Applications in Image Processing / (2016)

Yang, Xin-She, [editor.] ; Papa, João Paulo, [editor.]

London, United Kingdom :Academic Press,

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UID:

almahu_9948025731902882

Format: 1 online resource (xx, 353 pages) : , illustrations

ISBN: 0-12-804537-X

Note: Bio-inspired computation and its applications in image processing: an overview / X.-S. Yang, J.P. Papa -- Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization / S.E.N. Fernandes, K.K.F. Setoue, H. Adeli, J.P. Papa -- Fine-tuning deep belief networks using cuckoo search / D. Rodrigues, X.-S. Yang, J.P. Papa -- Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm / M. Tuba, M. Jordanski, A. Arsic -- Ground-glass opacity nodules detection and segmentation using the snake model / C.W. Bong, C.C. Liew, H.Y. Lam -- Mobile object tracking using the modified cuckoo search / T. Ljouad, A. Amine, M. Rziza -- Toward optimal watermarking of grayscale images using the multiple scaling factor-based cuckoo search technique / A. Mishra, C. Agarwal -- Bat algorithm-based automatic clustering method and its application in image processing / S. Nandy, P.P. Sarkar -- Multitemporal remote sensing image classification by nature-inspired techniques / J. Senthilnath, X.-S. Yang -- Firefly algorithm for optimized nonrigid demons registration / S. Chakraborty, N. Dey, S. Samanta, A.S. Ashour, V.E. Balas -- Minimizing the mode-change latency in real-time image processing applications / P.S. Martins, F.R. Massaro, E.L. Ursini, M.G. Carvalho, J. Real -- Learning OWA filters parameters for SAR imagery with multiple polarizations / L. Torres, J.C. Becceneri, C.C. Freitas, S.J.S. Sant'Anna, S. Sandri -- Oil reservoir quality assisted by machine learning and evolutionary computation / M.C. Kuroda, A.C. Vidal, J.P. Papa -- Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization / J. Li, S. Fong -- Retinal image vasculature analysis software (RIVAS) / B. Aliahmad, D.K. Kumar.

Additional Edition: ISBN 0-12-804536-1

Language: English

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Optimum-path forest : theory, algorithms and applications (2022)

Falcão, Alexandre Xavier, [editor.] ; Papa, João Paulo, [editor.]

London :Academic Press,

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UID:

edoccha_9960099788902883

Format: 1 online resource (246 pages)

ISBN: 0-12-822689-7

Note: 1. Introduction 2. Theoretical Background and Related Works 3. Real-time application of OPF-based classifier in Snort IDS 4. Optimum-Path Forest and Active Learning Approaches for Content-Based Medical Image Retrieval 5. Hybrid and Modified OPFs for Intrusion Detection Systems and Large-Scale Problems 6. Detecting Atherosclerotic Plaque Calcifications of the Carotid Artery Through Optimum-Path Forest 7. Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest 8. An Iterative Optimum-Path Forest Framework for Clustering 9. Future Trends in Optimum-Path Forest Classification

Additional Edition: ISBN 0-12-822688-9

Language: English

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Optimum-path forest : theory, algorithms and applications (2022)

Falcão, Alexandre Xavier, [editor.] ; Papa, João Paulo, [editor.]

London :Academic Press,

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UID:

edocfu_9960099788902883

Format: 1 online resource (246 pages)

ISBN: 0-12-822689-7

Additional Edition: ISBN 0-12-822688-9

Language: English

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