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Author |
Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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33 |
Issue |
5 |
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917-930 |
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The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. |
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Los Alamitos; CA; USA; |
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IEEE Computer Society |
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0162-8828 |
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Admin @ si @ VBW2011 |
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1715 |
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Author |
Diego Velazquez |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Towards Robustness in Computer-based Image Understanding |
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Book Whole |
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Year |
2023 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis embarks on an exploratory journey into robustness in deep learning,
with a keen focus on the intertwining facets of generalization, explainability, and
edge cases within the realm of computer vision. In deep learning, robustness
epitomizes a model’s resilience and flexibility, grounded on its capacity to generalize across diverse data distributions, explain its predictions transparently, and navigate the intricacies of edge cases effectively. The challenges associated with robust generalization are multifaceted, encompassing the model’s performance on unseen data and its defense against out-of-distribution data and adversarial attacks. Bridging this gap, the potential of Embedding Propagation (EP) for improving out-of-distribution generalization is explored. EP is depicted as a powerful tool facilitating manifold smoothing, which in turn fortifies the model’s robustness against adversarial onslaughts and bolsters performance in few-shot and self-/semi-supervised learning scenarios. In the labyrinth of deep learning models, the path to robustness often intersects with explainability. As model complexity increases, so does the urgency to decipher their decision-making
processes. Acknowledging this, the thesis introduces a robust framework for
evaluating and comparing various counterfactual explanation methods, echoing
the imperative of explanation quality over quantity and spotlighting the intricacies of diversifying explanations. Simultaneously, the deep learning landscape is fraught with edge cases – anomalies in the form of small objects or rare instances in object detection tasks that defy the norm. Confronting this, the
thesis presents an extension of the DETR (DEtection TRansformer) model to enhance small object detection. The devised DETR-FP, embedding the Feature Pyramid technique, demonstrating improvement in small objects detection accuracy, albeit facing challenges like high computational costs. With emergence of foundation models in mind, the thesis unveils EarthView, the largest scale remote sensing dataset to date, built for the self-supervised learning of a robust foundational model for remote sensing. Collectively, these studies contribute to the grand narrative of robustness in deep learning, weaving together the strands of generalization, explainability, and edge case performance. Through these methodological advancements and novel datasets, the thesis calls for continued exploration, innovation, and refinement to fortify the bastion of robust computer vision. |
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Ph.D. thesis |
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IMPRIMA |
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Jordi Gonzalez;Josep M. Gonfaus;Pau Rodriguez |
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978-81-126409-5-3 |
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Admin @ si @ Vel2023 |
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3965 |
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Author |
Sergio Vera |
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Title |
Anatomic Registration based on Medial Axis Parametrizations |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Image registration has been for many years the gold standard method to bring two images into correspondence. It has been used extensively in the eld of medical imaging in order to put images of dierent patients into a common overlapping spatial position. However, medical image registration is a slow, iterative optimization process, where many variables and prone to fall into the pit traps local minima.
A coordinate system parameterizing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specic anatomical sites, parameterizations ensure integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric meshes over the surface of anatomical shapes, given their ability to set values at specic locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at discrete sites of limited geometric diversity.
The medial surface of the shape can be used to provide a continuous basis for the denition of a depth coordinate. However, given that dierent methods for generation of medial surfaces generate dierent manifolds, not all of them are equally suited to be the basis of radial coordinate for a parameterization. It would be desirable that the medial surface will be smooth, and robust to surface shape noise, with low number of spurious branches or surfaces.
In this thesis we present methods for computation of smooth medial manifolds and apply them to the generation of for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. This reference system sets a solid base for creating anatomical models of the anatomical shapes, and allows comparing several patients in a common framework of reference. |
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November 2015 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Debora Gil;Miguel Angel Gonzalez Ballester |
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978-84-943427-8-3 |
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IAM; 600.075 |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ Ver2015 |
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2708 |
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Author |
Javier Vazquez; Graham D. Finlayson; Luis Herranz |
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Title |
Improving the perception of low-light enhanced images |
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Journal Article |
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Year |
2024 |
Publication |
Optics Express |
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32 |
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4 |
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5174-5190 |
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Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests. |
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MACO |
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Admin @ si @ VFH2024 |
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4018 |
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Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes |
![download PDF file pdf](img/file_PDF.gif)
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Title |
The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities |
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Conference Article |
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2016 |
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Digital Humanities Centres: Experiences and Perspectives |
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Warsaw; Poland; December 2016 |
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DHLABS |
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DAG; 600.097 |
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Admin @ si @ VFV2016 |
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2908 |
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Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes |
![download PDF file pdf](img/file_PDF.gif)
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Title |
La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades |
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Conference Article |
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2017 |
Publication |
3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional |
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281-383 |
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978-84-697-5692-8 |
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HDH |
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DAG; 600.121 |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ VFV2017 |
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3060 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Volumetric Anatomical Parameterization and Meshing for Inter-patient Liver Coordinate System Deffinition |
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Conference Article |
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2013 |
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16th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Nagoya; Japan; September 2013 |
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MICCAI |
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IAM |
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Admin @ si @ VGG2013 |
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2301 |
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Author |
Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Anatomical parameterization for volumetric meshing of the liver |
Type |
Conference Article |
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2014 |
Publication |
SPIE – Medical Imaging |
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9036 |
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Coordinate System; Anatomy Modeling; Parameterization |
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Abstract |
A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference. |
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Amsterdam; September 2014 |
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SPIE-MI |
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IAM; 600.075 |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ VGG2014 |
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2456 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
![goto web page url](img/www.gif)
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Title |
A Novel Cochlear Reference Frame Based On The Laplace Equation |
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Conference Article |
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2015 |
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29th international Congress and Exhibition on Computer Assisted Radiology and Surgery |
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10 |
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1 |
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1-312 |
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Poster |
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Barcelona; Spain; June 2015 |
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CARS |
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IAM; 600.075 |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ VGG2015 |
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2615 |
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Author |
Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
![goto web page url](img/www.gif)
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Title |
Logo Detection With No Priors |
Type |
Journal Article |
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Year |
2021 |
Publication |
IEEE Access |
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ACCESS |
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9 |
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106998-107011 |
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In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors. |
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Admin @ si @ VGR2021 |
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3664 |
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Author |
Ekta Vats; Anders Hast; Alicia Fornes |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion |
Type |
Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1294-1299 |
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Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching |
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Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 600.121 |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ VHF2019 |
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3356 |
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Author |
Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
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2006 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way. |
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CVC (UAB) |
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Ph.D. thesis |
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Petia Radeva |
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84-933652-7-0 |
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800 |
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MV;SIAI |
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Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
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738 |
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Fernando Vilariño |
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Citizen experience as a powerful communication tool: Open Innovation and the role of Living Labs in EU |
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2017 |
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European Conference of Science Journalists |
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The Open Innovation 2.0 model spearheaded by the European Commission introduces conceptual changes in how innovation processes should be developed. The notion of an innovation ecosystem, and the active participation of the citizens (and all the different actors of the quadruple helix) in innovation processes, opens up new channels for scientific communication, where the citizens (and all actors) can be naturally reached and facilitate the spread of the scientific message in their communities. Unleashing the power of such mechanisms, while maintaining control over the scientific communication done through such channels presents an opportunity and a challenge at the same time.
This workshop will look into key concepts that the Open Innovation 2.0 EU model introduces, and what new opportunities for communication they bring about. Specifically, we will focus on Living Labs, as a key instrument for implementing this innovation model at the regional level, and their potential in creating scientific dissemination spaces. |
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Copenhagen; June 2017 |
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ECSJ |
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MV; 600.097;SIAI |
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no |
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Admin @ si @ Vil2017a |
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3032 |
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Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation |
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Miscellaneous |
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2017 |
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Open Living Lab Days Report |
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Krakow; August 2017 |
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MV; no menciona;SIAI |
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Admin @ si @ Vil2017b |
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3033 |
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Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique |
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2019 |
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Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier |
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Montreal; Canada; December 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019a |
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3457 |
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