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10th iberian conference on pattern recognition and image analysis, ibpria 2022

10th Iberian Conference on Pattern Recognition and Image Ana...

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10th iberian conference on pattern recognition and image analysis, ibpria 2022

ISBN: (纸本)9783031048807

the proceedings contain 54 papers. the special focus in this conference is on pattern recognition and image analysis. the topics include: On the Performance of Deep Learning Models for Respiratory Sound Classification Trained on Unbalanced Data;automated Adequacy Assessment of Cervical Cytology Samples Using Deep Learning;exploring Alterations in Electrocardiogram During the Postoperative Pain;differential Gene Expression analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification;Detection of Epilepsy in EEGs Using Deep Sequence Models – A Comparative Study;facial Emotion recognition for Sentiment analysis of Social Media Data;heartbeat Selection Based on Outlier Removal;characterization of Emotions through Facial Electromyogram Signals;classification of Untranscribed Handwritten Notarial Documents by Textual Contents;multimodal Feature Evaluation and Fusion for Emotional Well-Being Monitorization;temporal Convolutional Networks for Robust Face Liveness Detection;maxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks;transparent Management of Adjacencies in the Cubic Grid;abbreviating Labelling Cost for Sentinel-2 image Scene Classification through Active Learning;feature-Based Classification of Archaeal Sequences Using Compression-Based Methods;a First Approach to image Transformation Sequence Retrieval;discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression recognition and Retrieval;golf Swing Sequencing Using Computer Vision;domain Adaptation in Robotics: A Study Case on Kitchen Utensil recognition;incremental Vocabularies in Machine Translation through Aligned Embedding Projections;An Innovative Vision System for Floor-Cleaning Robots Based on YOLOv5;LIDAR Signature Based Node Detection and Classification in Graph Topological Maps for Indoor Navigation;visual Event-Based Egocentric Human Action recognition;an Edge-Based Computer Vision Approach for D

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Detection of Epilepsy in EEGs Using Deep Sequence Models - A Comparative Study 10th

Detection of Epilepsy in EEGs Using Deep Sequence Models - A...

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10th iberian conference on pattern recognition and image analysis (ibpria)

作者： Marques, Miguel Lourenco, Catarina da Silva Teixeira, Luis F. Univ Porto Fac Engn INESC TEC R Dr Roberto Frias P-4200605 Porto Portugal Univ Twente Clin Neurophysiol Drienerlolaan 5 NL-7522 NB Enschede Netherlands

ISBN: (纸本)9783031048814;9783031048807

the automation of interictal epileptiform discharges through deep learning models can increase assertiveness and reduce the time spent on epilepsy diagnosis, making the process faster and more reliable. It was demonstrated that deep sequence networks can be a useful type of algorithm to effectively detect IEDs. Several different deep networks were tested, of which the best three architectures reached average AUC values of 0.96, 0.95 and 0.94, with convergence of test specificity and sensitivity values around 90%, which indicates a good ability to detect IED samples in EEG records.

关键词： Deep learning Interictal epileptiform discharges Recurrent neural networks Electroencephalogram

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From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation 10th

From Captions to Explanations: A Multimodal Transformer-base...

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10th iberian conference on pattern recognition and image analysis (ibpria)

作者： Rio-Torto, Isabel Cardoso, Jaime S. Teixeira, Luis F. INESC TEC Porto Portugal Univ Porto Fac Ciencias Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031048814;9783031048807

the growing importance of the Explainable Artificial Intelligence (XAI) field has led to the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not sufficient because different end-users have different backgrounds and preferences. Natural language explanations (NLEs) are inherently understandable by humans and, thus, can complement visual explanations. therefore, we introduce a novel architecture based on multimodal Transformers to enable the generation of NLEs for image classification tasks. Contrary to the current literature, which models NLE generation as a supervised image captioning problem, we propose to learn to generate these textual explanations without their direct supervision, by starting from image captions and evolving to classification-relevant text. Preliminary experiments on a novel dataset where there is a clear demarcation between captions and NLEs show the potential of the approach and shed light on how it can be improved.

关键词： Natural language explanations Explainable AI Transformers Natural language generation image captioning

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Test-Time Augmentation for Document image Binarization 11th

Test-Time Augmentation for Document Image Binarization

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11th iberian conference on pattern recognition and image analysis, ibpria 2023

作者： Rosello, Adrian Castellanos, Francisco J. Martinez-Esteso, Juan P. Gallego, Antonio Javier Calvo-Zaragoza, Jorge Department of Software and Computing Systems University of Alicante Alicante Spain

ISBN: (纸本)9783031366154

Document binarization is a well-known process addressed in the document image analysis literature, which aims to isolate the ink information from the background. Current solutions use deep learning, which requires a great amount of annotated data for training robust models. Data augmentation is known to reduce such annotation requirements, and it can be used in two ways: during training and during prediction. the latter is the so-called Test Time Augmentation (TTA), which has been successfully applied for general classification tasks. In this work, we study the application of TTA for binarization, a more complex and specific task. We focus on cases with a severe scarcity of annotated data over 5 existing binarization benchmarks. Although the results report certain improvements, these are rather limited. this implies that existing TTA strategies are not sufficient for binarization, which points to interesting lines of future work to further boost the performance. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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An Innovative Vision System for Floor-Cleaning Robots Based on YOLOv5 10th

An Innovative Vision System for Floor-Cleaning Robots Based ...

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10th iberian conference on pattern recognition and image analysis (ibpria)

作者： Canedo, Daniel Fonseca, Pedro Georgieva, Petia Neves, Antonio J. R. Univ Aveiro IEETA DETI P-3810193 Aveiro Portugal

ISBN: (纸本)9783031048814;9783031048807

the implementation of a robust vision system in floor-cleaning robots enables them to optimize their navigation and analysing the surrounding floor, leading to a reduction on power, water and chemical products' consumption. In this paper, we propose a novel pipeline of a vision system to be integrated into floor-cleaning robots. this vision system was built upon the YOLOv5 framework, and its role is to detect dirty spots on the floor. the vision system is fed by two cameras: one on the front and the other on the back of the floor-cleaning robot. the goal of the front camera is to save energy and resources of the floor-cleaning robot, controlling its speed and how much water and detergent is spent according to the detected dirt. the goal of the back camera is to act as evaluation and aid the navigation node, since it helps the floor-cleaning robot to understand if the cleaning was effective and if it needs to go back later for a second sweep. A self-calibration algorithm was implemented on both cameras to stabilize image intensity and improve the robustness of the vision system. A YOLOv5 model was trained with carefully prepared training data. A new dataset was obtained in an automotive factory using the floor-cleaning robot. A hybrid training dataset was used, consisting on the Automation and Control Institute dataset (ACIN), the automotive factory dataset, and a synthetic dataset. Data augmentation was applied to increase the dataset and to balance the classes. Finally, our vision system attained a mean average precision (mAP) of 0.7 on the testing set.

关键词： Computer vision Object detection Deep learning Floor-cleaning robots

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Lesion-Based Chest Radiography image Retrieval for Explainability in Pathology Detection 10th

Lesion-Based Chest Radiography Image Retrieval for Explainab...

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10th iberian conference on pattern recognition and image analysis (ibpria)

作者： Pedrosa, Joao Sousa, Pedro Silva, Joana Mendonca, Ana Maria Campilho, Aurelio Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal Ctr Hosp Vila Nova de Gaia Espinho Vila Nova De Gaia Portugal Inst Portugues Oncol Porto Francisco Gentil Porto Portugal Adm Reg Saude Norte ARSN Porto Portugal

ISBN: (纸本)9783031048814;9783031048807

Chest radiography is one of the most common medical imaging modalites. However, chest radiography interpretation is a complex task that requires significant expertise. As such, the development of automatic systems for pathology detection has been proposed in literature, particularly using deep learning. However, these techniques suffer from a lack of explainability, which hinders their adoption in clinical scenarios. One technique commonly used by radiologists to support and explain decisions is to search for cases with similar findings for direct comparison. However, this process is extremely time-consuming and can be prone to confirmation bias. Automatic image retrieval methods have been proposed in literature but typically extract features from the whole image, failing to focus on the lesion in which the radiologist is interested. In order to overcome these issues, a novel framework LXIR for lesion-based image retrieval is proposed in this study, based on a state of the art object detection framework (YOLOv5) for the detection of relevant lesions as well as feature representation of those lesions. It is shown that the proposed method can successfully identify lesions and extract features which accurately describe high-order characteristics of each lesion, allowing to retrieve lesions of the same pathological class. Furthermore, it is show that in comparison to SSIM-based retrieval, a classical perceptual metric, and random retrieval of lesions, the proposed method retrieves the most relevant lesions 81% of times, according to the evaluation of two independent radiologists, in comparison to 42% of times by SSIM.

关键词： Chest radiography image retrieval Deep learning Explainability

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Py4MER: A CTC-Based Mathematical Expression recognition System 11th

Py4MER: A CTC-Based Mathematical Expression Recognition Syst...

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11th iberian conference on pattern recognition and image analysis, ibpria 2023

作者： Anitei, Dan Sánchez, Joan Andreu Benedí, José Miguel Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica València Valencia Spain Valencian Graduate School and Research Network of Artificial Intelligence Camí de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031366154

Mathematical expression recognition is a research field that aims to develop algorithms and systems capable of interpreting mathematical content. the recognition of MEs requires handling two-dimensional symbol relationships such as sub/superscripts, matrices and nested fractions, among others. the prevalent technology for addressing these challenges are based on encoder-decoder architectures with attention models. In this paper we propose the Py4MER system, based on Convolutional Recurrent Neural Network (CRNN) models, for the recognition and transcription of MEs into LaTeX mark-up sequences. this model is proposed as an alternative to encoder-decoder approaches, as CRNN models trained through Connectionist Temporal Classification (CTC) implicitly model the dependencies between symbols and do not suffer from under/over parsing of the input image, generating more consistent mark-up. the proposed model is evaluated on the Im2Latex-100k data set based on both textual and image-level metrics, showing a remarkable improvement from other CTC-based approaches. recognition results are analyzed for different ME lengths and ME structures. Furthermore, a study based on the edit distance is performed, showing a considerable improvement in precision when up to 5 edit operations are considered. Finally, we show that CTC-based CRNN models can adapt to non left-to-right ordering of ME elements, warranting more research for this approach. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word image recognition 11th

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian...

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11th iberian conference on pattern recognition and image analysis, ibpria 2023

作者： Gurav, Aniket Jensen, Joakim Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031366154

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition, and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical character recognition

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Can Representation Learning for Multimodal image Registration be Improved by Supervision of Intermediate Layers? 11th

Can Representation Learning for Multimodal Image Registrati...

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11th iberian conference on pattern recognition and image analysis, ibpria 2023

作者： Wetzer, Elisabeth Lindblad, Joakim Sladoje, Nataša Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783031366154

Multimodal imaging and correlative analysis typically require image alignment. Contrastive learning can generate representations of multimodal images, reducing the challenging task of multimodal image registration to a monomodal one. Previously, additional supervision on intermediate layers in contrastive learning has improved biomedical image classification. We evaluate if a similar approach improves representations learned for registration to boost registration performance. We explore three approaches to add contrastive supervision to the latent features of the bottleneck layer in the U-Nets encoding the multimodal images and evaluate three different critic functions. Our results show that representations learned without additional supervision on latent features perform best in the downstream task of registration on two public biomedical datasets. We investigate the performance drop by exploiting recent insights in contrastive learning in classification and self-supervised learning. We visualize the spatial relations of the learned representations by means of multidimensional scaling, and show that additional supervision on the bottleneck layer can lead to partial dimensional collapse of the intermediate embedding space. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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MicrogliaJ: An Automatic Tool for Microglial Cell Detection and Segmentation 11th

MicrogliaJ: An Automatic Tool for Microglial Cell Detection...

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11th iberian conference on pattern recognition and image analysis, ibpria 2023

作者： Casado-García, Ángela Carlos, Estefanía Domínguez, César Heras, Jónathan Izco, María Mata, Eloy Pascual, Vico Álvarez-Erviti, Lydia Department of Mathematics and Computer Science University of La Rioja Logroño26004 Spain Piqueras 98 3th floor Logroño26006 Spain

ISBN: (纸本)9783031366154

Microglial cells are now recognized as crucial players in the development of neurodegenerative diseases. the analysis and quantification of microglia changes is necesary to better understand the contribution of microglia in neurodegenerative processes or drug treatments. However, the manual quantification of microglial cells is a time-consuming and subjective tasks;and, therefore, reliable tools that automate this process are desirable. In this paper, we present MicrogliaJ, an imageJ macro, that can measure both the number and area of microglial cells. the automatic procedure implemented in MicrogliaJ is based on classical image processing techniques, and the results can be manually validated by experts with a simple-to-use interface. MicrogliaJ has been tested by experts and it obtains analogous results to those manually produced, but considerably reducing the time required for such analysis. thanks to this work, the analysis of microglia images will be faster and more reliable, and this will help us to advance our understanding of the behaviour of microglial cells. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neurodegenerative diseases

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