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| Author | David Berga | ||||
| Title | Understanding Eye Movements: Psychophysics and a Model of Primary Visual Cortex | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract  |
Humansmove their eyes in order to learn visual representations of the world. These eye movements depend on distinct factors, either by the scene that we perceive or by our own decisions. To select what is relevant to attend is part of our survival mechanisms and the way we build reality, as we constantly react both consciously and unconsciously to all the stimuli that is projected into our eyes. In this thesis we try to explain (1) how we move our eyes, (2) how to build machines that understand visual information and deploy eyemovements, and (3) how to make these machines understand tasks in order to decide for eye movements.
(1) We provided the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of 230 synthetically-generated image patterns. A total of 15 types of stimuli has been generated (e.g. orientation, brightness, color, size, etc.), with 7 feature contrasts for each feature category. Eye-tracking data was collected from 34 participants during the viewing of the dataset, using Free-Viewing and Visual Search task instructions. Results showed that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. Temporality of fixations, 4. task difficulty and 5. center bias. From such dataset (SID4VAM), we have computed a benchmark of saliency models by testing performance using psychophysical patterns. Model performance has been evaluated considering model inspiration and consistency with human psychophysics. Our study reveals that state-of-the-art Deep Learning saliency models do not performwell with synthetic pattern images, instead, modelswith Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. (2) Computations in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible, among several visual processing mechanisms, of bottom-up visual attention (also named saliency). In order to validate this hypothesis, images from eye tracking datasets have been processed with a biologically plausible model of V1 (named Neurodynamic SaliencyWaveletModel or NSWAM). Following Li’s neurodynamic model, we define V1’s lateral connections with a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. The resulting saliency maps are generated from the model output, representing the neuronal activity of V1 projections towards brain areas involved in eye movement control. We want to pinpoint that our unified computational architecture is able to reproduce several visual processes (i.e. brightness, chromatic induction and visual discomfort) without applying any type of training or optimization and keeping the same parametrization. The model has been extended (NSWAM-CM) with an implementation of the cortical magnification function to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outperforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences, specifically for nature and synthetic images. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts. (3) Although previous scanpath models have been able to efficiently predict saccades during Free-Viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-termmemory in combination with stimulus-driven eyemovement neuronal correlates. In our latest study we proposed an extension of the Selective Tuning Attentive Reference Fixation ControllerModel based on task demands (STAR-FCT), describing novel computational definitions of Long-TermMemory, Visual Task Executive and Task Working Memory. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memorymodel by processing a visual hierarchy of low- and high-level features. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting object localizationmaps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions compared to saliency. |
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| Address | July 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-948531-8-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ Ber2019 | Serial | 3390 | ||
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| Author | Yaxing Wang | ||||
| Title | Transferring and Learning Representations for Image Generation and Translation | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
Image generation is arguably one of the most attractive, compelling, and challenging tasks in computer vision. Among the methods which perform image generation, generative adversarial networks (GANs) play a key role. The most common image generation models based on GANs can be divided into two main approaches. The first one, called simply image generation takes random noise as an input and synthesizes an image which follows the same distribution as the images in the training set. The second class, which is called image-to-image translation, aims to map an image from a source domain to one that is indistinguishable from those in the target domain. Image-to-image translation methods can further be divided into paired and unpaired image-to-image translation based on whether they require paired data or not. In this thesis, we aim to address some challenges of both image generation and image-to-image generation.GANs highly rely upon having access to vast quantities of data, and fail to generate realistic images from random noise when applied to domains with few images. To address this problem, we aim to transfer knowledge from a model trained on a large dataset (source domain) to the one learned on limited data (target domain). We find that both GANs andconditional GANs can benefit from models trained on large datasets. Our experiments show that transferring the discriminator is more important than the generator. Using both the generator and discriminator results in the best performance. We found, however, that this method suffers from overfitting, since we update all parameters to adapt to the target data. We propose a novel architecture, which is tailored to address knowledge transfer to very small target domains. Our approach effectively exploreswhich part of the latent space is more related to the target domain. Additionally, the proposed method is able to transfer knowledge from multiple pretrained GANs. Although image-to-image translation has achieved outstanding performance, it still facesseveral problems. First, for translation between complex domains (such as translations between different modalities) image-to-image translation methods require paired data. We show that when only some of the pairwise translations have been seen (i.e. during training), we can infer the remaining unseen translations (where training pairs are not available). We propose a new approach where we align multiple encoders and decoders in such a way that the desired translation can be obtained by simply cascadingthe source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). Second, we address the issue of bias in image-to-image translation. Biased datasets unavoidably contain undesired changes, which are dueto the fact that the target dataset has a particular underlying visual distribution. We use carefully designed semantic constraints to reduce the effects of the bias. The semantic constraint aims to enforce the preservation of desired image properties. Finally, current approaches fail to generate diverse outputs or perform scalable image transfer in a single model. To alleviate this problem, we propose a scalable and diverse image-to-image translation. We employ random noise to control the diversity. The scalabitlity is determined by conditioning the domain label.computer vision, deep learning, imitation learning, adversarial generative networks, image generation, image-to-image translation. | ||||
| Address | January 2020 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Abel Gonzalez;Luis Herranz | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-121011-5-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Wan2020 | Serial | 3397 | ||
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| Author | Sergio Vera | ||||
| Title | Anatomic Registration based on Medial Axis Parametrizations | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
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 di erent 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 speci c 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 speci c 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 de nition of a depth coordinate. However, given that di erent methods for generation of medial surfaces generate di erent 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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| Address | November 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Debora Gil;Miguel Angel Gonzalez Ballester | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-8-3 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ Ver2015 | Serial | 2708 | ||
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| Author | Eduard Vazquez | ||||
| Title | Unsupervised image segmentation based on material reflectance description and saliency | Type | Book Whole | ||
| Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Ramon Baldrich | ||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Vaz2011b | Serial | 1835 | ||
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| Author | Marc Serra | ||||
| Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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| Address | September 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Robert Benavente;Olivier Penacchio | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-4-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Ser2015 | Serial | 2688 | ||
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| Author | Fei Yang | ||||
| Title | Towards Practical Neural Image Compression | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
Images and videos are pervasive in our life and communication. With advances in smart and portable devices, high capacity communication networks and high definition cinema, image and video compression are more relevant than ever. Traditional block-based linear transform codecs such as JPEG, H.264/AVC or the recent H.266/VVC are carefully designed to meet not only the rate-distortion criteria, but also the practical requirements of applications.
Recently, a new paradigm based on deep neural networks (i.e., neural image/video compression) has become increasingly popular due to its ability to learn powerful nonlinear transforms and other coding tools directly from data instead of being crafted by humans, as was usual in previous coding formats. While achieving excellent rate-distortion performance, these approaches are still limited mostly to research environments due to heavy models and other practical limitations, such as being limited to function on a particular rate and due to high memory and computational cost. In this thesis, we study these practical limitations, and designing more practical neural image compression approaches. After analyzing the differences between traditional and neural image compression, our first contribution is the modulated autoencoder (MAE), a framework that includes a mechanism to provide multiple rate-distortion options within a single model with comparable performance to independent models. In a second contribution, we propose the slimmable compressive autoencoder (SlimCAE), which in addition to variable rate, can optimize the complexity of the model and thus reduce significantly the memory and computational burden. Modern generative models can learn custom image transformation directly from suitable datasets following encoder-decoder architectures, task known as image-to-image (I2I) translation. Building on our previous work, we study the problem of distributed I2I translation, where the latent representation is transmitted through a binary channel and decoded in a remote receiving side. We also propose a variant that can perform both translation and the usual autoencoding functionality. Finally, we also consider neural video compression, where the autoencoder is typically augmented with temporal prediction via motion compensation. One of the main bottlenecks of that framework is the optical flow module that estimates the displacement to predict the next frame. Focusing on this module, we propose a method that improves the accuracy of the optical flow estimation and a simplified variant that reduces the computational cost. Key words: neural image compression, neural video compression, optical flow, practical neural image compression, compressive autoencoders, image-to-image translation, deep learning. |
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| Address | December 2021 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Luis Herranz;Mikhail Mozerov;Yongmei Cheng | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-122714-7-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ Yan2021 | Serial | 3608 | ||
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| Author | Yi Xiao | ||||
| Title | Advancing Vision-based End-to-End Autonomous Driving | Type | Book Whole | ||
| Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
In autonomous driving, artificial intelligence (AI) processes the traffic environment to drive the vehicle to a desired destination. Currently, there are different paradigms that address the development of AI-enabled drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception, maneuver planning, and control. On the other hand, we find end-to-end driving approaches that attempt to learn the direct mapping of raw data from input sensors to vehicle control signals. The latter are relatively less studied but are gaining popularity as they are less demanding in terms of data labeling. Therefore, in this thesis, our goal is to investigate end-to-end autonomous driving.
We propose to evaluate three approaches to tackle the challenge of end-to-end autonomous driving. First, we focus on the input, considering adding depth information as complementary to RGB data, in order to mimic the human being’s ability to estimate the distance to obstacles. Notice that, in the real world, these depth maps can be obtained either from a LiDAR sensor, or a trained monocular depth estimation module, where human labeling is not needed. Then, based on the intuition that the latent space of end-to-end driving models encodes relevant information for driving, we use it as prior knowledge for training an affordancebased driving model. In this case, the trained affordance-based model can achieve good performance while requiring less human-labeled data, and it can provide interpretability regarding driving actions. Finally, we present a new pure vision-based end-to-end driving model termed CIL++, which is trained by imitation learning. CIL++ leverages modern best practices, such as a large horizontal field of view and a self-attention mechanism, which are contributing to the agent’s understanding of the driving scene and bringing a better imitation of human drivers. Using training data without any human labeling, our model yields almost expert performance in the CARLA NoCrash benchmark and could rival SOTA models that require large amounts of human-labeled data. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Antonio Lopez | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-126409-4-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Xia2023 | Serial | 3964 | ||
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| Author | Naila Murray | ||||
| Title | Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks. Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification. As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu;Maria Vanrell | |
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Mur2012 | Serial | 2212 | ||
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| Author | Lichao Zhang | ||||
| Title | Towards end-to-end Networks for Visual Tracking in RGB and TIR Videos | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
In the current work, we identify several problems of current tracking systems. The lack of large-scale labeled datasets hampers the usage of deep learning, especially end-to-end training, for tracking in TIR images. Therefore, many methods for tracking on TIR data are still based on hand-crafted features. This situation also happens in multi-modal tracking, e.g. RGB-T tracking. Another reason, which hampers the development of RGB-T tracking, is that there exists little research on the fusion mechanisms for combining information from RGB and TIR modalities. One of the crucial components of most trackers is the update module. For the currently existing end-to-end tracking architecture, e.g, Siamese trackers, the online model update is still not taken into consideration at the training stage. They use no-update or a linear update strategy during the inference stage. While such a hand-crafted approach to updating has led to improved results, its simplicity limits the potential gain likely to be obtained by learning to update.
To address the data-scarcity for TIR and RGB-T tracking, we use image-to-image translation to generate a large-scale synthetic TIR dataset. This dataset allows us to perform end-to-end training for TIR tracking. Furthermore, we investigate several fusion mechanisms for RGB-T tracking. The multi-modal trackers are also trained in an end-to-end manner on the synthetic data. To improve the standard online update, we pose the updating step as an optimization problem which can be solved by training a neural network. Our approach thereby reduces the hand-crafted components in the tracking pipeline and sets a further step in the direction of a complete end-to-end trained tracking network which also considers updating during optimization. |
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| Address | November 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Abel Gonzalez;Fahad Shahbaz Khan | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-1210011-1-9 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Zha2019 | Serial | 3393 | ||
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| Author | Santiago Segui | ||||
| Title | Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis | Type | Book Whole | ||
| Year | 2011 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| 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. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Vitria | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Seg2011 | Serial | 1836 | ||
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| Author | Cesar de Souza | ||||
| Title | Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
| Address | April 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Naila Murray | |
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Sou2018 | Serial | 3127 | ||
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| Author | Xim Cerda-Company | ||||
| Title | Understanding color vision: from psychophysics to computational modeling | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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In this PhD we have approached the human color vision from two different points of view: psychophysics and computational modeling. First, we have evaluated 15 different tone-mapping operators (TMOs). We have conducted two experiments that
consider two different criteria: the first one evaluates the local relationships among intensity levels and the second one evaluates the global appearance of the tonemapped imagesw.r.t. the physical one (presented side by side). We conclude that the rankings depend on the criterion and they are not correlated. Considering both criteria, the best TMOs are KimKautz (Kim and Kautz, 2008) and Krawczyk (Krawczyk, Myszkowski, and Seidel, 2005). Another conclusion is that a more standardized evaluation criteria is needed to do a fair comparison among TMOs. Secondly, we have conducted several psychophysical experiments to study the color induction. We have studied two different properties of the visual stimuli: temporal frequency and luminance spatial distribution. To study the temporal frequency we defined equiluminant stimuli composed by both uniform and striped surrounds and we flashed them varying the flash duration. For uniform surrounds, the results show that color induction depends on both the flash duration and inducer’s chromaticity. As expected, in all chromatic conditions color contrast was induced. In contrast, for striped surrounds, we expected to induce color assimilation, but we observed color contrast or no induction. Since similar but not equiluminant striped stimuli induce color assimilation, we concluded that luminance differences could be a key factor to induce color assimilation. Thus, in a subsequent study, we have studied the luminance differences’ effect on color assimilation. We varied the luminance difference between the target region and its inducers and we observed that color assimilation depends on both this difference and the inducer’s chromaticity. For red-green condition (where the first inducer is red and the second one is green), color assimilation occurs in almost all luminance conditions. Instead, for green-red condition, color assimilation never occurs. Purple-lime and lime-purple chromatic conditions show that luminance difference is a key factor to induce color assimilation. When the target is darker than its surround, color assimilation is stronger in purple-lime, while when the target is brighter, color assimilation is stronger in lime-purple (’mirroring’ effect). Moreover, we evaluated whether color assimilation is due to luminance or brightness differences. Similarly to equiluminance condition, when the stimuli are equibrightness no color assimilation is induced. Our results support the hypothesis that mutual-inhibition plays a major role in color perception, or at least in color induction. Finally, we have defined a new firing rate model of color processing in the V1 parvocellular pathway. We have modeled two different layers of this cortical area: layers 4Cb and 2/3. Our model is a recurrent dynamic computational model that considers both excitatory and inhibitory cells and their lateral connections. Moreover, it considers the existent laminar differences and the cells’ variety. Thus, we have modeled both single- and double-opponent simple cells and complex cells, which are a pool of double-opponent simple cells. A set of sinusoidal drifting gratings have been used to test the architecture. In these gratings we have varied several spatial properties such as temporal and spatial frequencies, grating’s area and orientation. To reproduce the electrophysiological observations, the architecture has to consider the existence of non-oriented double-opponent cells in layer 4Cb and the lack of lateral connections between single-opponent cells. Moreover, we have tested our lateral connections simulating the center-surround modulation and we have reproduced physiological measurements where for high contrast stimulus, the result of the lateral connections is inhibitory, while it is facilitatory for low contrast stimulus. |
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| Address | March 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu | |
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| ISSN | ISBN | 978-84-948531-4-2 | Medium | ||
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| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ Cer2019 | Serial | 3259 | ||
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| Author | Juan Ignacio Toledo | ||||
| Title | Information Extraction from Heterogeneous Handwritten Documents | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction. |
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| Address | July 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Josep Llados | |
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| ISSN | ISBN | 978-84-948531-7-3 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Tol2019 | Serial | 3389 | ||
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| Author | Francesco Ciompi | ||||
| Title | Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods. The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets. A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events. Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Cio2012 | Serial | 2146 | ||
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| Author | Marina Alberti | ||||
| Title | Detection and Alignment of Vascular Structures in Intravascular Ultrasound using Pattern Recognition Techniques | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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In this thesis, several methods for the automatic analysis of Intravascular Ultrasound
(IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques. First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classi cation problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are re ned using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability. Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at dierent moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological pro les from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm - is applied to the pro les and adapted to the speci c clinical problem. Two dierent robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the pro le extraction. The bene ts of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method. Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a rst classi cation phase (the identi- cation of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Simone Balocco;Petia Radeva | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Alb2013 | Serial | 2215 | ||
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