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Author Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke
Title Graph-based k-means clustering: A comparison of the set versus the generalized median graph Type Conference Article
Year 2009 Publication 13th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 5702 Issue Pages 342–350
Keywords
Abstract In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.
Address (down) Münster, Germany
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-03766-5 Medium
Area Expedition Conference CAIP
Notes DAG Approved no
Call Number DAG @ dag @ FVS2009d Serial 1219
Permanent link to this record
 
 
Author Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta
Title Structure-Preserving Smoothing of Biomedical Images Type Conference Article
Year 2009 Publication 13th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 5702 Issue Pages 427-434
Keywords non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography.
Abstract Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.
Address (down) Münster, Germany
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-03766-5 Medium
Area Expedition Conference CAIP
Notes IAM Approved no
Call Number IAM @ iam @ GHB2009 Serial 1527
Permanent link to this record
 
 
Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Dimensionality Reduction for Graph of Words Embedding Type Conference Article
Year 2011 Publication 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition Abbreviated Journal
Volume 6658 Issue Pages 22-31
Keywords
Abstract The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
Address (down) Münster, Germany
Corporate Author Thesis
Publisher Place of Publication Editor Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-20843-0 Medium
Area Expedition Conference GbRPR
Notes DAG Approved no
Call Number Admin @ si @ GVB2011a Serial 1743
Permanent link to this record
 
 
Author Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras
Title Information Theoretic Rotationwise Robust Binary Descriptor Learning Type Conference Article
Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal
Volume Issue Pages 368-378
Keywords
Abstract In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.
Address (down) Mérida; Mexico; November 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference S+SSPR
Notes DAG; ADAS; 600.097; 600.086 Approved no
Call Number Admin @ si @ RLL2016 Serial 2871
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Author Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li
Title ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition Type Journal Article
Year 2022 Publication IEEE Transactions on Cybernetics Abbreviated Journal TCIBERN
Volume 52 Issue 5 Pages 3422-3433
Keywords
Abstract The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.
Address (down) May 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ WLW2022 Serial 3522
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Author Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez
Title Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition Type Journal Article
Year 2022 Publication ACM Transactions on Multimedia Computing, Communications, and Applications Abbreviated Journal ACMTMC
Volume Issue Pages
Keywords Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition
Abstract Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.
Address (down) May 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE; 600.157 Approved no
Call Number Admin @ si @ GYW2022 Serial 3692
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Author Felipe Codevilla
Title On Building End-to-End Driving Models Through Imitation Learning Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Autonomous vehicles are now considered as an assured asset in the future. Literally, all the relevant car-markers are now in a race to produce fully autonomous vehicles. These car-makers usually make use of modular pipelines for designing autonomous vehicles. This strategy decomposes the problem in a variety of tasks such as object detection and recognition, semantic and instance segmentation, depth estimation, SLAM and place recognition, as well as planning and control. Each module requires a separate set of expert algorithms, which are costly specially in the amount of human labor and necessity of data labelling. An alternative, that recently has driven considerable interest, is the end-to-end driving. In the end-to-end driving paradigm, perception and control are learned simultaneously using a deep network. These sensorimotor models are typically obtained by imitation learning fromhuman demonstrations. The main advantage is that this approach can directly learn from large fleets of human-driven vehicles without requiring a fixed ontology and extensive amounts of labeling. However, scaling end-to-end driving methods to behaviors more complex than simple lane keeping or lead vehicle following remains an open problem. On this thesis, in order to achieve more complex behaviours, we
address some issues when creating end-to-end driving system through imitation
learning. The first of themis a necessity of an environment for algorithm evaluation and collection of driving demonstrations. On this matter, we participated on the creation of the CARLA simulator, an open source platformbuilt from ground up for autonomous driving validation and prototyping. Since the end-to-end approach is purely reactive, there is also the necessity to provide an interface with a global planning system. With this, we propose the conditional imitation learning that conditions the actions produced into some high level command. Evaluation is also a concern and is commonly performed by comparing the end-to-end network output to some pre-collected driving dataset. We show that this is surprisingly weakly correlated to the actual driving and propose strategies on how to better acquire data and a better comparison strategy. Finally, we confirmwell-known generalization issues
(due to dataset bias and overfitting), new ones (due to dynamic objects and the
lack of a causal model), and training instability; problems requiring further research before end-to-end driving through imitation can scale to real-world driving.
Address (down) May 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ Cod2019 Serial 3387
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Author Zhijie Fang
Title Behavior understanding of vulnerable road users by 2D pose estimation Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians
and cyclists can be critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, therefore, should be taken into account by systems providing any level of driving assistance, i.e. from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this PhD work, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow the established traffic codes to indicate future left/right turns and stop maneuvers with arm signals. In the case of pedestrians, no indications can be assumed a priori. Instead, we hypothesize that the walking pattern of a pedestrian can allow us to determine if he/she has the intention of crossing the road in the path of the egovehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this PhD work, we show how the same methodology can be used for recognizing pedestrians and cyclists’ intentions. For pedestrians, we perform experiments on the publicly available Daimler and JAAD datasets. For cyclists, we did not found an analogous dataset, therefore, we created our own one by acquiring
and annotating corresponding video-sequences which we aim to share with the
research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.
Address (down) May 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;David Vazquez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-948531-6-6 Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ Fan2019 Serial 3388
Permanent link to this record
 
 
Author Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich
Title Image Segmentation in the Presence of Shadows and Highligts Type Conference Article
Year 2008 Publication 10th European Conference on Computer Vision Abbreviated Journal
Volume 5305 Issue Pages 1–14
Keywords
Abstract
Address (down) Marseille (France)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes CAT;CIC Approved no
Call Number CAT @ cat @ VVB2008b Serial 1013
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Author Akhil Gurram
Title Monocular Depth Estimation for Autonomous Driving Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract 3D geometric information is essential for on-board perception in autonomous driving and driver assistance. Autonomous vehicles (AVs) are equipped with calibrated sensor suites. As part of these suites, we can find LiDARs, which are expensive active sensors in charge of providing the 3D geometric information. Depending on the operational conditions for the AV, calibrated stereo rigs may be also sufficient for obtaining 3D geometric information, being these rigs less expensive and easier to install than LiDARs. However, ensuring a proper maintenance and calibration of these types of sensors is not trivial. Accordingly, there is an increasing interest on performing monocular depth estimation (MDE) to obtain 3D geometric information on-board. MDE is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Moreover, a set of single cameras with MDE capabilities would still be a cheap solution for on-board perception, relatively easy to integrate and maintain in an AV.
Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Accordingly, the overall goal of this PhD is to study methods for improving CNN-based MDE accuracy under different training settings. More specifically, this PhD addresses different research questions that are described below. When we started to work in this PhD, state-of-theart methods for MDE were already based on CNNs. In fact, a promising line of work consisted in using image-based semantic supervision (i.e., pixel-level class labels) while training CNNs for MDE using LiDAR-based supervision (i.e., depth). It was common practice to assume that the same raw training data are complemented by both types of supervision, i.e., with depth and semantic labels. However, in practice, it was more common to find heterogeneous datasets with either only depth supervision or only semantic supervision. Therefore, our first work was to research if we could train CNNs for MDE by leveraging depth and semantic information from heterogeneous datasets. We show that this is indeed possible, and we surpassed the state-of-the-art results on MDE at the time we did this research. To achieve our results, we proposed a particular CNN architecture and a new training protocol.
After this research, it was clear that the upper-bound setting to train CNN-based MDE models consists in using LiDAR data as supervision. However, it would be cheaper and more scalable if we would be able to train such models from monocular sequences. Obviously, this is far more challenging, but worth to research. Training MDE models using monocular sequences is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. To alleviate these problems, we perform MDE by virtual-world supervision and real-world SfM self-supervision. We call our proposalMonoDEVSNet. We compensate the SfM self-supervision limitations by leveraging
virtual-world images with accurate semantic and depth supervision, as well as addressing the virtual-to-real domain gap. MonoDEVSNet outperformed previous MDE CNNs trained on monocular and even stereo sequences. We have publicly released MonoDEVSNet at <https://github.com/HMRC-AEL/MonoDEVSNet>.
Finally, since MDE is performed to produce 3D information for being used in downstream tasks related to on-board perception. We also address the question of whether the standard metrics for MDE assessment are a good indicator for future MDE-based driving-related perception tasks. By using 3D object detection on point clouds as proxy of on-board perception, we conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods which reflects relatively well the 3D object detection results we may expect.
Address (down) March, 2022
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Antonio Lopez;Onay Urfalioglu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-0-0 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Gur2022 Serial 3712
Permanent link to this record
 
 
Author Carola Figueroa Flores
Title Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency Type Book Whole
Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords computer vision; visual saliency; fine-grained object recognition; convolutional neural networks; images classification
Abstract For humans, the recognition of objects is an almost instantaneous, precise and
extremely adaptable process. Furthermore, we have the innate capability to learn
new object classes from only few examples. The human brain lowers the complexity
of the incoming data by filtering out part of the information and only processing
those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple
glance the most important or salient regions from an image. This mechanism can
be observed by analyzing on which parts of images subjects place attention; where
they fix their eyes when an image is shown to them. The most accurate way to
record this behavior is to track eye movements while displaying images.
Computational saliency estimation aims to identify to what extent regions or
objects stand out with respect to their surroundings to human observers. Saliency
maps can be used in a wide range of applications including object detection, image
and video compression, and visual tracking. The majority of research in the field has
focused on automatically estimating saliency maps given an input image. Instead, in
this thesis, we set out to incorporate saliency maps in an object recognition pipeline:
we want to investigate whether saliency maps can improve object recognition
results.
In this thesis, we identify several problems related to visual saliency estimation.
First, to what extent the estimation of saliency can be exploited to improve the
training of an object recognition model when scarce training data is available. To
solve this problem, we design an image classification network that incorporates
saliency information as input. This network processes the saliency map through a
dedicated network branch and uses the resulting characteristics to modulate the
standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive
experiments on standard benchmark datasets for fine-grained object recognition,
we show that our proposed architecture can significantly improve performance,
especially on dataset with scarce training data.
Next, we address the main drawback of the above pipeline: SMIC requires an
explicit saliency algorithm that must be trained on a saliency dataset. To solve this,
we implement a hallucination mechanism that allows us to incorporate the saliency
estimation branch in an end-to-end trained neural network architecture that only
needs the RGB image as an input. A side-effect of this architecture is the estimation
of saliency maps. In experiments, we show that this architecture can obtain similar
results on object recognition as SMIC but without the requirement of ground truth
saliency maps to train the system.
Finally, we evaluated the accuracy of the saliency maps that occur as a sideeffect of object recognition. For this purpose, we use a set of benchmark datasets
for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human
eye-tracking experiments. Our results show that these saliency maps can obtain
competitive results on benchmark saliency maps. On one synthetic saliency dataset
this method even obtains the state-of-the-art without the need of ever having seen
an actual saliency image for training.
Address (down) March 2021
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Bogdan Raducanu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-4-7 Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ Fig2021 Serial 3600
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Author Pau Rodriguez
Title Towards Robust Neural Models for Fine-Grained Image Recognition Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
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.
Address (down) March 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Josep M. Gonfaus;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-948531-3-5 Medium
Area Expedition Conference
Notes ISE; 600.119 Approved no
Call Number Admin @ si @ Rod2019 Serial 3258
Permanent link to this record
 
 
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
Volume Issue Pages
Keywords
Abstract 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.
Address (down) March 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 978-84-948531-4-2 Medium
Area Expedition Conference
Notes NEUROBIT Approved no
Call Number Admin @ si @ Cer2019 Serial 3259
Permanent link to this record
 
 
Author Petia Radeva; Cristina Cañero; Juan J. Villanueva; J. Mauri; E Fernandez-Nofrerias
Title 3D Reconstruction of a Stent by Deformable Models. Type Miscellaneous
Year 2001 Publication Proceedings of the IASTED International Conference, Visualization, Imaging and Image Processing, 417–422. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (down) Marbella.
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RCV2001 Serial 158
Permanent link to this record
 
 
Author Partha Pratim Roy; Josep Llados
Title Multi-Oriented Character Recognition from Graphical Documents Type Conference Article
Year 2008 Publication 2nd International Conference on Cognition and Recognition Abbreviated Journal
Volume Issue Pages 30–35
Keywords
Abstract
Address (down) Mandya (India)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCR
Notes DAG Approved no
Call Number DAG @ dag @ RLP2008 Serial 965
Permanent link to this record