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检索条件"任意字段=7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015"

共 64 条记录，以下是1-10 订阅

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7th iberian conference on pattern recognition and image analysis, ibpria 2015

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7th iberian conference on pattern recognition and image analysis, ibpria 2015

ISBN: (纸本)9783319193892

the proceedings contain 92 papers. the special focus in this conference is on pattern recognition, Machine Learning, Computer Vision, Signal Processing and Medical image. the topics include: Spatiotemporal stacked sequential learning for pedestrian detection;social signaling descriptor for group behaviour analysis;a new smoothing method for lexicon-based handwritten text keyword spotting;empirical evaluation of different feature representations for social circles detection;on the impact of distance metrics in instance-based learning algorithms;object discovery using CNN features in egocentric videos;prototype generation on structural data using dissimilarity space representation;human centered scene understanding based on 3D long-term tracking data;fast head pose estimation for human-computer interaction;structured light system calibration for perception in underwater tanks;scene recognition invariant to symmetrical reflections and illumination conditions in robotics;system for medical mask detection in the operating room through facial attributes;noise decomposition using polynomial approximation;color correction for image stitching by monotone cubic spline interpolation;escaping path approach with extended neighborhood for speckle noise reduction;unsupervised approximation of digital planar curves;on the modification of binarization algorithms to retain grayscale information for handwritten text recognition;improving the minimum description length inference of phrase-based translation models;a kinect-based system to assess lymphedema impairments in breast cancer patients;word-graph based applications for handwriting documents;spatial-dependent similarity metric supporting multi-atlas MRI segmentation and color detection in dermoscopy images based on scarce annotations.

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Learning Sparse Masks for Diffusion-Based image Inpainting 10th

Learning Sparse Masks for Diffusion-Based Image Inpainting

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

作者： Alt, Tobias Peter, Pascal Weickert, Joachim Saarland Univ Fac Math & Comp Sci Math Image Anal Grp Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9783031048814;9783031048807

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location - the inpainting mask - is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for applications such as image compression, where fast encoding is highly desirable.

关键词： image inpainting Diffusion Partial differential equations Data optimisation Deep learning

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pattern recognition and image analysis: 7th iberian conference, ibpria 2015 Santiago de Compostela, Spain, june 17–19, 2015 proceedings 7

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7th iberian conference on pattern recognition and image analysis, ibpria 2015

作者： Paredes, Roberto Cardoso, Jaime S. Pardo, Xosé M. Universitat Politècnica de València València Spain Universidade do Porto Porto Portugal Universidade de Santiago de Compostela Santiago de Compostela Spain

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An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks 10th

An End-to-End Approach for Seam Carving Detection Using Deep...

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

作者： Moreira, thierry P. Santana, Marcos Cleison S. Passos, Leandro A. Papa, Joao Paulo da Costa, Kelton Augusto P. Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube 14-01 BR-17033360 Bauru SP Brazil Univ Wolverhampton Sch Engn & Informat CMI Lab Wolverhampton England

ISBN: (纸本)9783031048814;9783031048807

Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model.

关键词： Seam carving Convolutional neural networks image security

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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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Food recognition by Integrating Local and Flat Classifiers 9th

Food Recognition by Integrating Local and Flat Classifiers

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Aguilar, Eduardo Radeva, Petia Univ Catolica Norte Antofagasta Chile Univ Barcelona Barcelona Spain Comp Vis Ctr Bellaterra Spain

ISBN: (纸本)9783030313326;9783030313319

the recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier;otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier.

关键词： CNNs Deep learning Epistemic Uncertainty image classification Food recognition

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Automated Detection and Identification of Olive Fruit Fly Using YOLOv7 Algorithm 11th

Automated Detection and Identification of Olive Fruit Fly ...

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

作者： Victoriano, Margarida Oliveira, Lino Oliveira, Hélder P. INESC TEC Porto Portugal FCUP Porto Portugal

ISBN: (纸本)9783031366154

the impact of climate change on global temperature and precipitation patterns can lead to an increase in extreme environmental events. these events can create favourable conditions for the spread of plant pests and diseases, leading to significant production losses in agriculture. To mitigate these losses, early detection of pests is crucial in order to implement effective and safe control management strategies, to protect the crops, public health and the environment. Our work focuses on the development of a computer vision framework to detect and classify the olive fruit fly, also known as Bactrocera oleae, from images, which is a serious concern to the EU’s olive tree industry. the images of the olive fruit fly were obtained from traps placed throughout olive orchards located in Greece. the approach entails augmenting the dataset and fine-tuning the YOLOv7 model to improve the model performance, in identifying and classifying olive fruit flies. A Portuguese dataset was also used to further perform detection. To assess the model, a set of metrics were calculated, and the experimental results indicated that the model can precisely identify the positive class, which is the olive fruit fly. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Impact of Ultrasound image Reconstruction Method on Breast Lesion Classification with Deep Learning 9th

Impact of Ultrasound Image Reconstruction Method on Breast L...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Byra, Michal Sznajder, Tomasz Korzinek, Danijel Piotrzkowska-Wroblewska, Hanna Dobruch-Sobczak, Katarzyna Nowicki, Andrzej Marasek, Krzysztof Polish Acad Sci Inst Fundamental Technol Res Dept Ultrasound Warsaw Poland Polish Japanese Acad Informat Technol Dept Multimedia Warsaw Poland

ISBN: (纸本)9783030313326;9783030313319

In this work we investigate the usefulness and robustness of transfer learning with deep convolutional neural networks (CNNs) for breast lesion classification in ultrasound (US). Deep learning models can be vulnerable to adversarial examples, engineered input image pixel intensities perturbations that force models to make classification errors. In US imaging, distribution of US image pixel intensities relies on applied US image reconstruction algorithm. We explore the possibility of fooling deep learning models for breast mass classification by modifying US image reconstruction method. Raw radio-frequency US signals acquired from malignant and benign breast masses were used to reconstruct US images, and develop classifiers using transfer learning with the VGG19, InceptionV3 and InceptionResNetV2 CNNs. the areas under the receiver operating characteristic curve (AUCs) obtained for each deep learning model developed and evaluated using US images reconstructed in the same way were equal to approximately 0.85, and there were no associated differences in AUC values between the models (DeLong test p-values > 0.15). However, due to small modifications of the US image reconstruction method the AUC values for the models utilizing the VGG19, InceptionV3 and InceptionResNetV2 CNNs significantly decreased to 0.592, 0.584 and 0.687, respectively. Our study shows that the modification of US image reconstruction algorithm can have significant negative impact on classification performance of deep models. Taking into account medical image reconstruction algorithms may help develop more robust deep learning computer aided diagnosis systems.

关键词： Adversarial attacks Breast lesion classification Computer aided diagnosis Deep learning Robustness Ultrasound imaging Transfer learning

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Sentiment recognition in Egocentric Photostreams 8th

Sentiment Recognition in Egocentric Photostreams

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8th iberian conference on pattern recognition and image analysis (ibpria)

作者： Talavera, Estefania Strisciuglio, Nicola Petkov, Nicolai Radeva, Petia Univ Groningen Intelligent Syst Grp Groningen Netherlands Univ Barcelona Dept Math & Comp Sci Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9783319588384;9783319588377

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral).

关键词： Egocentric photos Lifelogging Sentiment image analysis

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Interactive Layout Detection 8th

Interactive Layout Detection

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8th iberian conference on pattern recognition and image analysis (ibpria)

作者： Quiros, Lorenzo Martinez-Hinarejos, Carlos-D. Toselli, Alejandro H. Vidal, Enrique Univ Politecn Valencia Pattern Recognit & Human Language Technol Res Ctr Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9783319588384;9783319588377

the amounts of ancient documents transcribed by means of Handwritten Text recognition (HTR) technology have been rising dramatically over the last years. Consequently, the development and enhancement of HTR methods and algorithms have become an important issue in the field, with significant contributions in performance for documents with segmented layout. However, Layout analysis remains a bottleneck in the development and generalization of HTR technology. In this work a new Interactive-Probabilistic method to obtain document layout is presented. this new method incorporates the user feedback in the Layout analysis process, in order to provide not just a very accurate layout, but an interactive framework in which user feedback is used to help the system to fix any error.

关键词： Layout analysis Page segmentation Interactive pattern recognition Conditional Random Field Handwritten Text recognition

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