|
|
Pau Rodriguez. (2019). Towards Robust Neural Models for Fine-Grained Image Recognition (Jordi Gonzalez, Josep M. Gonfaus, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Fine-grained recognition, i.e. identifying similar subcategories of the same superclass, is central to human activity. Recognizing a friend, finding bacteria in microscopic imagery, or discovering a new kind of galaxy, are just but few examples. However, fine-grained image recognition is still a challenging computer vision task since the differences between two images of the same category can overwhelm the differences between two images of different fine-grained categories. In this regime, where the difference between two categories resides on subtle input changes, excessively invariant CNNs discard those details that help to discriminate between categories and focus on more obvious changes, yielding poor classification performance.
On the other hand, CNNs with too much capacity tend to memorize instance-specific details, thus causing overfitting. In this thesis,motivated by the
potential impact of automatic fine-grained image recognition, we tackle the previous challenges and demonstrate that proper alignment of the inputs, multiple levels of attention, regularization, and explicitmodeling of the output space, results inmore accurate fine-grained recognitionmodels, that generalize better, and are more robust to intra-class variation. Concretely, we study the different stages of the neural network pipeline: input pre-processing, attention to regions, feature activations, and the label space. In each stage, we address different issues that hinder the recognition performance on various fine-grained tasks, and devise solutions in each chapter: i)We deal with the sensitivity to input alignment on fine-grained human facial motion such as pain. ii) We introduce an attention mechanism to allow CNNs to choose and process in detail the most discriminate regions of the image. iii)We further extend attention mechanisms to act on the network activations,
thus allowing them to correct their predictions by looking back at certain
regions, at different levels of abstraction. iv) We propose a regularization loss to prevent high-capacity neural networks to memorize instance details by means of almost-identical feature detectors. v)We finally study the advantages of explicitly modeling the output space within the error-correcting framework. As a result, in this thesis we demonstrate that attention and regularization seem promising directions to overcome the problems of fine-grained image recognition, as well as proper treatment of the input and the output space.
|
|
|
|
Adriana Romero. (2015). Assisting the training of deep neural networks with applications to computer vision (Carlo Gatta, & Petia Radeva, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Deep learning has recently been enjoying an increasing popularity due to its success in solving challenging tasks. In particular, deep learning has proven to be effective in a large variety of computer vision tasks, such as image classification, object recognition and image parsing. Contrary to previous research, which required engineered feature representations, designed by experts, in order to succeed, deep learning attempts to learn representation hierarchies automatically from data. More recently, the trend has been to go deeper with representation hierarchies.
Learning (very) deep representation hierarchies is a challenging task, which
involves the optimization of highly non-convex functions. Therefore, the search
for algorithms to ease the learning of (very) deep representation hierarchies from data is extensive and ongoing.
In this thesis, we tackle the challenging problem of easing the learning of (very) deep representation hierarchies. We present a hyper-parameter free, off-the-shelf, simple and fast unsupervised algorithm to discover hidden structure from the input data by enforcing a very strong form of sparsity. We study the applicability and potential of the algorithm to learn representations of varying depth in a handful of applications and domains, highlighting the ability of the algorithm to provide discriminative feature representations that are able to achieve top performance.
Yet, while emphasizing the great value of unsupervised learning methods when
labeled data is scarce, the recent industrial success of deep learning has revolved around supervised learning. Supervised learning is currently the focus of many recent research advances, which have shown to excel at many computer vision tasks. Top performing systems often involve very large and deep models, which are not well suited for applications with time or memory limitations. More in line with the current trends, we engage in making top performing models more efficient, by designing very deep and thin models. Since training such very deep models still appears to be a challenging task, we introduce a novel algorithm that guides the training of very thin and deep models by hinting their intermediate representations.
Very deep and thin models trained by the proposed algorithm end up extracting feature representations that are comparable or even better performing
than the ones extracted by large state-of-the-art models, while compellingly
reducing the time and memory consumption of the model.
|
|
|
|
Misael Rosales. (2005). A Physics-Based Image Modelling of IVUS as a Geometric and Kinematic System (Petia Radeva, Ed.). Ph.D. thesis, , .
|
|
|
|
German Ros. (2016). Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What (Angel Sappa, Julio Guerrero, & Antonio Lopez, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating.
|
|
|
|
David Rotger. (2009). Analysis and Multi-Modal Fusion of coronary Images (Petia Radeva, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: The framework of this thesis is to study in detail different techniques and tools for medical image registration in order to ease the daily life of clinical experts in cardiology. The first aim of this thesis is providing computer tools for
fusing IVUS and angiogram data is of high clinical interest to help the physicians locate in IVUS data and decide which lesion is observed, how long it is, how far from a bifurcation or another lesions stays, etc. This thesis proves and
validates that we can segment the catheter path in angiographies using geodesic snakes (based on fast marching algorithm), a three-dimensional reconstruction of the catheter inspired in stereo vision and a new technique to fuse IVUS
and angiograms that establishes exact correspondences between them. We have developed a new workstation called iFusion that has four strong advantages: registration of IVUS and angiographic images with sub-pixel precision, it works on- and off-line, it is independent on the X-ray system and there is no need of daily calibration. The second aim of the thesis is devoted to developing a computer-aided analysis of IVUS for image-guided intervention. We have designed, implemented
and validated a robust algorithm for stent extraction and reconstruction from IVUS videos. We consider a very special and recent kind of stents, bioabsorbable stents that represent a great clinical challenge due to their property to be
absorbed by time and thus avoiding the “danger” of neostenosis as one of the main problems of metallic stents. We present a new and very promising algorithm based on an optimized cascade of multiple classifiers to automatically detect individual stent struts of a very novel bioabsorbable drug eluting coronary stent. This problem represents a very challenging target given the variability in contrast, shape and grey levels of the regions to be detected, what is
denoted by the high variability between the specialists (inter-observer variability of 0.14~$\pm$0.12). The obtained results of the automatic strut detection are within the inter-observer variability.
|
|
|
|
Gemma Rotger. (2021). Lifelike Humans: Detailed Reconstruction of Expressive Human Faces (Felipe Lumbreras, & Antonio Agudo, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops.
|
|
|
|
Mohammad Rouhani. (2012). Shape Representation and Registration using Implicit Functions (Angel Sappa, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Shape representation and registration are two important problems in computer vision and graphics. Representing the given cloud of points through an implicit function provides a higher level information describing the data. This representation can be more compact more robust to noise and outliers, hence it can be exploited in different computer vision application. In the first part of this thesis implicit shape representations, including both implicit B-spline and polynomial, are tackled. First, an approximation of a geometric distance is proposed to measure the closeness of the given cloud of points and the implicit surface. The analysis of the proposed distance shows an accurate estimation with smooth behavior. The distance by itself is used in a RANSAC based quadratic fitting method. Moreover, since the gradient information of the distance with respect to the surface parameters can be analytically computed, it is used in Levenberg-Marquadt algorithm to refine the surface parameters. In a different approach, an algebraic fitting method is used to represent an object through implicit B-splines. The outcome is a smooth flexible surface and can be represented in different levels from coarse to fine. This property has been exploited to solve the registration problem in the second part of the thesis. In the proposed registration technique the model set is replaced with an implicit representation provided in the first part; then, the point-to-point registration is converted to a point-to-model one in a higher level. This registration error can benefit from different distance estimations to speed up the registration process even without need of correspondence search. Finally, the non-rigid registration problem is tackled through a quadratic distance approximation that is based on the curvature information of the model set. This approximation is used in a free form deformation model to update its control lattice. Then it is shown how an accurate distance approximation can benefit non-rigid registration problems.
|
|
|
|
Partha Pratim Roy. (2010). Multi-Oriented and Multi-Scaled Text Character Analysis and Recognition in Graphical Documents and their Applications to Document Image Retrieval (Josep Llados, & Umapada Pal, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: With the advent research of Document Image Analysis and Recognition (DIAR), an
important line of research is explored on indexing and retrieval of graphics rich documents. It aims at finding relevant documents relying on segmentation and recognition
of text and graphics components underlying in non-standard layout where commercial
OCRs can not be applied due to complexity. This thesis is focused towards text information extraction approaches in graphical documents and retrieval of such documents
using text information.
Automatic text recognition in graphical documents (map, engineering drawing,
etc.) involves many challenges because text characters are usually printed in multioriented and multi-scale way along with different graphical objects. Text characters
are used to annotate the graphical curve lines and hence, many times they follow
curvi-linear paths too. For OCR of such documents, individual text lines and their
corresponding words/characters need to be extracted.
For recognition of multi-font, multi-scale and multi-oriented characters, we have
proposed a feature descriptor for character shape using angular information from contour pixels to take care of the invariance nature. To improve the efficiency of OCR, an
approach towards the segmentation of multi-oriented touching strings into individual
characters is also discussed. Convex hull based background information is used to
segment a touching string into possible primitive segments and later these primitive
segments are merged to get optimum segmentation using dynamic programming. To
overcome the touching/overlapping problem of text with graphical lines, a character
spotting approach using SIFT and skeleton information is included. Afterwards, we
propose a novel method to extract individual curvi-linear text lines using the foreground and background information of the characters of the text and a water reservoir
concept is used to utilize the background information.
We have also formulated the methodologies for graphical document retrieval applications using query words and seals. The retrieval approaches are performed using
recognition results of individual components in the document. Given a query text,
the system extracts positional knowledge from the query word and uses the same to
generate hypothetical locations in the document. Indexing of documents is also performed based on automatic detection of seals from documents containing cluttered
background. A seal is characterized by scale and rotation invariant spatial feature
descriptors computed from labelled text characters and a concept based on the Generalized Hough Transform is used to locate the seal in documents.
|
|
|
|
Jose Carlos Rubio. (2012). Many-to-Many High Order Matching. Applications to Tracking and Object Segmentation (Joan Serrat, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Feature matching is a fundamental problem in Computer Vision, having multiple applications such as tracking, image classification and retrieval, shape recognition and stereo fusion. In numerous domains, it is useful to represent the local structure of the matching features to increase the matching accuracy or to make the correspondence invariant to certain transformations (affine, homography, etc. . . ). However, encoding this knowledge requires complicating the model by establishing high-order relationships between the model elements, and therefore increasing the complexity of the optimization problem.
The importance of many-to-many matching is sometimes dismissed in the literature. Most methods are restricted to perform one-to-one matching, and are usually validated on synthetic, or non-realistic datasets. In a real challenging environment, with scale, pose and illumination variations of the object of interest, as well as the presence of occlusions, clutter, and noisy observations, many-to-many matching is necessary to achieve satisfactory results. As a consequence, finding the most likely many-to-many correspondence often involves a challenging combinatorial optimization process.
In this work, we design and demonstrate matching algorithms that compute many-to-many correspondences, applied to several challenging problems. Our goal is to make use of high-order representations to improve the expressive power of the matching, at the same time that we make feasible the process of inference or optimization of such models. We effectively use graphical models as our preferred representation because they provide an elegant probabilistic framework to tackle structured prediction problems.
We introduce a matching-based tracking algorithm which performs matching between frames of a video sequence in order to solve the difficult problem of headlight tracking at night-time. We also generalise this algorithm to solve the problem of data association applied to various tracking scenarios. We demonstrate the effectiveness of such approach in real video sequences and we show that our tracking algorithm can be used to improve the accuracy of a headlight classification system.
In the second part of this work, we move from single (point) matching to dense (region) matching and we introduce a new hierarchical image representation. We make use of such model to develop a high-order many-to-many matching between pairs of images. We show that the use of high-order models in comparison to simpler models improves not only the accuracy of the results, but also the convergence speed of the inference algorithm.
Finally, we keep exploiting the idea of region matching to design a fully unsupervised image co-segmentation algorithm that is able to perform competitively with state-of-the-art supervised methods. Our method also overcomes the typical drawbacks of some of the past works, such as avoiding the necessity of variate appearances on the image backgrounds. The region matching in this case is applied to effectively exploit inter-image information. We also extend this work to perform co-segmentation of videos, being the first time that such problem is addressed, as a way to perform video object segmentation
|
|
|
|
Idoia Ruiz. (2022). Deep Metric Learning for re-identification, tracking and hierarchical novelty detection (Joan Serrat, Ed.). Ph.D. thesis, , .
Abstract: Metric learning refers to the problem in machine learning of learning a distance or similarity measurement to compare data. In particular, deep metric learning involves learning a representation, also referred to as embedding, such that in the embedding space data samples can be compared based on the distance, directly providing a similarity measure. This step is necessary to perform several tasks in computer vision. It allows to perform the classification of images, regions or pixels, re-identification, out-of-distribution detection, object tracking in image sequences and any other task that requires computing a similarity score for their solution. This thesis addresses three specific problems that share this common requirement. The first one is person re-identification. Essentially, it is an image retrieval task that aims at finding instances of the same person according to a similarity measure. We first compare in terms of accuracy and efficiency, classical metric learning to basic deep learning based methods for this problem. In this context, we also study network distillation as a strategy to optimize the trade-off between accuracy and speed at inference time. The second problem we contribute to is novelty detection in image classification. It consists in detecting samples of novel classes, i.e. never seen during training. However, standard novelty detection does not provide any information about the novel samples besides they are unknown. Aiming at more informative outputs, we take advantage from the hierarchical taxonomies that are intrinsic to the classes. We propose a metric learning based approach that leverages the hierarchical relationships among classes during training, being able to predict the parent class for a novel sample in such hierarchical taxonomy. Our third contribution is in multi-object tracking and segmentation. This joint task comprises classification, detection, instance segmentation and tracking. Tracking can be formulated as a retrieval problem to be addressed with metric learning approaches. We tackle the existing difficulty in academic research that is the lack of annotated benchmarks for this task. To this matter, we introduce the problem of weakly supervised multi-object tracking and segmentation, facing the challenge of not having available ground truth for instance segmentation. We propose a synergistic training strategy that benefits from the knowledge of the supervised tasks that are being learnt simultaneously.
|
|
|
|
Carles Sanchez. (2014). Tracheal Structure Characterization using Geometric and Appearance Models for Efficient Assessment of Stenosis in Videobronchoscopy (F. Javier Sanchez, Debora Gil, & Jorge Bernal, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Recent advances in endoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures. Among all endoscopic modalities, bronchoscopy is one of the most frequent with around 261 millions of procedures per year. Although the use of bronchoscopy is spread among clinical facilities it presents some drawbacks, being the visual inspection for the assessment of anatomical measurements the most prevalent of them. In
particular, inaccuracies in the estimation of the degree of stenosis (the percentage of obstructed airway) decreases its diagnostic yield and might lead to erroneous treatments. An objective computation of tracheal stenosis in bronchoscopy videos would constitute a breakthrough for this non-invasive technique and a reduction in treatment cost.
This thesis settles the first steps towards on-line reliable extraction of anatomical information from videobronchoscopy for computation of objective measures. In particular, we focus on the computation of the degree of stenosis, which is obtained by comparing the area delimited by a healthy tracheal ring and the stenosed lumen. Reliable extraction of airway structures in interventional videobronchoscopy is a challenging task. This is mainly due to the large variety of acquisition conditions (positions and illumination), devices (different digitalizations) and in videos acquired at the operating room the unpredicted presence of surgical devices (such as probe ends). This thesis contributes to on-line stenosis assessment in several ways. We
propose a parametric strategy for the extraction of lumen and tracheal rings regions based on the characterization of their geometry and appearance that guide a deformable model. The geometric and appearance characterization is based on a physical model describing the way bronchoscopy images are obtained and includes local and global descriptions. In order to ensure a systematic applicability we present a statistical framework to select the optimal
parameters of our method. Experiments perform on the first public annotated database, show that the performance of our method is comparable to the one provided by clinicians and its computation time allows for a on-line implementation in the operating room.
|
|
|
|
Angel Sappa, & George A. Triantafyllid. (2012). Computer Graphics and Imaging.
|
|
|
|
Angel Sappa (Ed.). (2022). ICT Applications for Smart Cities (Vol. 224). ISRL. Springer.
Abstract: Part of the book series: Intelligent Systems Reference Library (ISRL)
This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:
• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes
The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field.
Keywords: Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing
|
|
|
|
Angel Sappa, & Jordi Vitria. (2013). Multimodal Interaction in Image and Video Applications (Vol. 48). Springer Berlin Heidelberg.
Abstract: Book Series Intelligent Systems Reference Library
|
|
|
|
Santiago Segui. (2011). Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis (Jordi Vitria, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of
the intestine by using a small, swallowed camera. This small size device was received
with a high enthusiasm within the medical community, and until now, it is still one
of the medical devices with the highest use growth rate. WCE can be used as a novel
diagnostic tool that presents several clinical advantages, since it is non-invasive and
at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to
detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However,
the visual analysis of WCE videos presents an important drawback: the long time
required by the physicians for proper video visualization. In this sense and regarding
to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community.
The work presented in this thesis is a set of contributions for the automatic image
analysis and computer-aided diagnosis of intestinal motility disorders using WCE.
Until now, the diagnosis of small bowel motility dysfunctions was basically performed
by invasive techniques such as the manometry test, which can only be conducted at
some referral centers around the world owing to the complexity of the procedure and
the medial expertise required in the interpretation of the results.
Our contributions are divided in three main blocks:
1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events
such as: intestinal contractions, intestinal content, tunnel and wrinkles;
2. Machine learning techniques for the analysis and the manipulation of the data
from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data;
3. Two different systems for the computer-aided diagnosis of intestinal motility
disorders using WCE. The first system presents a fully automatic method that
aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients.
|
|
