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Author |
Diego Velazquez |
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Title |
Towards Robustness in Computer-based Image Understanding |
Type |
Book Whole |
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Year |
2023 |
Publication |
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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Thesis |
Ph.D. thesis |
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Publisher |
IMPRIMA |
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Editor |
Jordi Gonzalez;Josep M. Gonfaus;Pau Rodriguez |
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978-81-126409-5-3 |
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ISE |
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no |
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Call Number |
Admin @ si @ Vel2023 |
Serial |
3965 |
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Author |
David Masip |
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Title |
Face Classification Using Discriminative Features and Classifier Combination |
Type |
Book Whole |
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Year |
2005 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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CVC (UAB) |
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Thesis |
Ph.D. thesis |
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Editor |
Jordi Vitria |
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84-933652-3-8 |
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Notes |
OR;MV |
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no |
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Call Number |
Admin @ si @ Mas2005b |
Serial |
602 |
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Author |
Debora Gil |
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Title |
Geometric Differential Operators for Shape Modelling |
Type |
Book Whole |
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Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Medical imaging feeds research in many computer vision and image processing fields: image filtering, segmentation, shape recovery, registration, retrieval and pattern matching. Because of their low contrast changes and large variety of artifacts and noise, medical imaging processing techniques relying on an analysis of the geometry of image level sets rather than on intensity values result in more robust treatment. From the starting point of treatment of intravascular images, this PhD thesis ad- dresses the design of differential image operators based on geometric principles for a robust shape modelling and restoration. Among all fields applying shape recovery, we approach filtering and segmentation of image objects. For a successful use in real images, the segmentation process should go through three stages: noise removing, shape modelling and shape recovery. This PhD addresses all three topics, but for the sake of algorithms as automated as possible, techniques for image processing will be designed to satisfy three main principles: a) convergence of the iterative schemes to non-trivial states avoiding image degeneration to a constant image and representing smooth models of the originals; b) smooth asymptotic behav- ior ensuring stabilization of the iterative process; c) fixed parameter values ensuring equal (domain free) performance of the algorithms whatever initial images/shapes. Our geometric approach to the generic equations that model the different processes approached enables defining techniques satisfying all the former requirements. First, we introduce a new curvature-based geometric flow for image filtering achieving a good compromise between noise removing and resemblance to original images. Sec- ond, we describe a new family of diffusion operators that restrict their scope to image level curves and serve to restore smooth closed models from unconnected sets of points. Finally, we design a regularization of snake (distance) maps that ensures its smooth convergence towards any closed shape. Experiments show that performance of the techniques proposed overpasses that of state-of-the-art algorithms. |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona (Spain) |
Editor |
Jordi Saludes i Closa;Petia Radeva |
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84-933652-0-3 |
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prit |
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IAM; |
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no |
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Call Number |
IAM @ iam @ GIL2004 |
Serial |
1517 |
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Author |
Ramon Baldrich |
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Title |
Perceptual approach to a computational colour-texture representation for surface inspection. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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CIC |
Approved |
no |
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Call Number |
CAT @ cat @ Bal2001 |
Serial |
73 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Editor |
Petia Radeva;JuanJose Villanueva |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ Tol2001 |
Serial |
166 |
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Author |
Antonio Lopez |
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Title |
Multilocal Methods for Ridge and Valley Delineation in Image Analysis. |
Type |
Book Whole |
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Year |
2000 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Editor |
Joan Serrat |
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ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ Lop2000 |
Serial |
174 |
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Author |
Felipe Lumbreras |
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Title |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ Lum2001 |
Serial |
188 |
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Author |
A. Pujol |
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Title |
Contributions to shape and texture face similarity measurement. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Editor |
JuanJose Villanueva |
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no |
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Call Number |
Admin @ si @ Puj2001 |
Serial |
202 |
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Author |
David Lloret |
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Title |
Medical Image Registration Based on a Creaseress Measure. |
Type |
Book Whole |
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Year |
2002 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Place of Publication |
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Editor |
Joan Serrat |
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no |
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Call Number |
Admin @ si @ Llo2002 |
Serial |
321 |
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Author |
Jordi Gonzalez |
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Title |
Human Sequence Evaluation: the Key-frame Approach |
Type |
Book Whole |
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Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Editor |
Xavier Roca;Javier Varona |
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Approved |
no |
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Call Number |
ISE @ ise @ Gon2004 |
Serial |
362 |
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Author |
David Guillamet |
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Title |
Statistical Local Appearance Models for Object Recognition |
Type |
Book Whole |
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Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Abstract |
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Address |
Bellaterra |
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Thesis |
Ph.D. thesis |
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Place of Publication |
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Editor |
Jordi Vitria |
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Approved |
no |
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Call Number |
Admin @ si @ Gui2004 |
Serial |
444 |
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Author |
Oriol Pujol |
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Title |
A semi-Supervised Statistical Framework and Generative Snakes for IVUS Analysis |
Type |
Book Whole |
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Year |
2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Address |
CVC (UAB), Bellaterra |
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Thesis |
Ph.D. thesis |
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Editor |
Petia Radeva |
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Notes |
HuPBA;MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ Puj2004 |
Serial |
512 |
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Author |
Misael Rosales |
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Title |
A Physics-Based Image Modelling of IVUS as a Geometric and Kinematic System |
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Book Whole |
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Year |
2005 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Address |
CVC (UAB) |
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Ph.D. thesis |
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Petia Radeva |
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978-84-922529-8-7 |
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no |
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Call Number |
Admin @ si @ Ros2005 |
Serial |
603 |
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Author |
Fernando Vilariño |
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
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2006 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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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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Address |
CVC (UAB) |
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Thesis |
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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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
Serial |
738 |
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Author |
Miquel Ferrer |
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Title |
Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering |
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Book Whole |
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Year |
2008 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Ph.D. thesis |
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Editor |
Francesc Serratosa Casanelles;Ernest Valveny |
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978-84-935251-7-0 |
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Notes |
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Approved |
no |
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Call Number |
Admin @ si @ Fer2008 |
Serial |
1105 |
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