| |
Records |
Links |
|
Author |
Pedro Herruzo; Marc Bolaños; Petia Radeva |
|
| |
Title |
Can a CNN Recognize Catalan Diet? |
Type |
Book Chapter |
| |
Year |
2016 |
Publication |
AIP Conference Proceedings |
Abbreviated Journal |
|
|
| |
Volume |
1773 |
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
|
| |
Address  |
|
|
| |
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 |
Admin @ si @ HBR2016 |
Serial |
2837 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund |
|
| |
Title |
Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
80 |
Issue |
|
Pages |
208–215 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset. |
|
| |
Address |
|
|
| |
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; ISE;MILAB; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TEG2016 |
Serial |
2843 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera |
|
| |
Title |
RGB-D Segmentation of Poultry Entrails |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
9th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Best commercial paper award. |
|
| |
Address |
|
|
| |
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 |
AMDO |
|
| |
Notes |
HuPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PJM2016 |
Serial |
2848 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton |
|
| |
Title |
Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
28 |
Issue |
|
Pages |
1489 - 1491 |
|
| |
Keywords |
|
|
| |
Abstract |
The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others. |
|
| |
Address |
|
|
| |
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; ISE;MV; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
2851 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans |
|
| |
Title |
Improved RGB-D-T based Face Recognition |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
|
| |
Volume |
5 |
Issue |
4 |
Pages |
297 - 303 |
|
| |
Keywords |
|
|
| |
Abstract |
Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. |
|
| |
Address |
|
|
| |
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;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OCN2016 |
Serial |
2854 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
German Ros |
|
| |
Title |
Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What |
Type |
Book Whole |
| |
Year |
2016 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
| |
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Angel Sappa;Julio Guerrero;Antonio Lopez |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-84-945373-1-8 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ Ros2016 |
Serial |
2860 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Francisco Cruz |
|
| |
Title |
Probabilistic Graphical Models for Document Analysis |
Type |
Book Whole |
| |
Year |
2016 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models.
In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
| |
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Oriol Ramos Terrades |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-84-945373-2-5 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ Cru2016 |
Serial |
2861 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
19 |
Issue |
4 |
Pages |
335-349 |
|
| |
Keywords |
scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
|
| |
Abstract |
Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
|
| |
Address |
|
|
| |
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 |
DAG; 600.056; 601.197 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GoK2016a |
Serial |
2862 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
|
| |
Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
5th International Conference Games and Learning Alliance |
Abbreviated Journal |
|
|
| |
Volume |
10056 |
Issue |
|
Pages |
50-59 |
|
| |
Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
|
| |
Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
|
| |
Address |
|
|
| |
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 |
GALA |
|
| |
Notes |
ADAS;IAM; |
Approved |
no |
|
| |
Call Number |
HAC2016 |
Serial |
2864 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez |
|
| |
Title |
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest |
Type |
Miscellaneous |
| |
Year |
2016 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Domain Adaptation; Pedestrian detection; Random Forest |
|
| |
Abstract |
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. |
|
| |
Address |
|
|
| |
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 |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ MVJ2016 |
Serial |
2868 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena |
|
| |
Title |
Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Patter Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
76 |
Issue |
1 |
Pages |
76-82 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
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 |
IAM; 600.060 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MFV2017b |
Serial |
2941 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
|
| |
Title |
Development of general‐purpose projection‐based augmented reality systems |
Type |
Journal |
| |
Year |
2016 |
Publication |
IADIs international journal on computer science and information systems |
Abbreviated Journal |
IADIs |
|
| |
Volume |
11 |
Issue |
2 |
Pages |
1-18 |
|
| |
Keywords |
|
|
| |
Abstract |
Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups |
|
| |
Address |
|
|
| |
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 |
DAG; 600.084 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SCK2016 |
Serial |
2890 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez |
|
| |
Title |
Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding |
Type |
Book Whole |
| |
Year |
2016 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
| |
Publisher |
|
Place of Publication |
|
Editor |
Dimosthenis Karatzas |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ Gom2016 |
Serial |
2891 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
|
| |
Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
Type |
Journal |
| |
Year |
2016 |
Publication |
Lligall, Revista Catalana d'Arxivística |
Abbreviated Journal |
|
|
| |
Volume |
39 |
Issue |
|
Pages |
20-46 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
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 |
DAG; 600.097 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FLR2016 |
Serial |
2897 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
|
| |
Title |
Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
Type |
Book Chapter |
| |
Year |
2016 |
Publication |
The future of historical demography. Upside down and inside out |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
127-131 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Acco Publishers |
Place of Publication |
|
Editor |
K.Matthijs; S.Hin; H.Matsuo; J.Kok |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-94-6292-722-3 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.097 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PFL2016 |
Serial |
2907 |
|
| Permanent link to this record |
