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Author | Fernando Vilariño; Debora Gil; Petia Radeva | ||||
Title | A Novel FLDA Formulation for Numerical Stability Analysis | Type | Book Chapter | ||
Year | 2004 | Publication | Recent Advances in Artificial Intelligence Research and Development | Abbreviated Journal | |
Volume | 113 | Issue | Pages | 77-84 | |
Keywords | Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision | ||||
Abstract | Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision. | ||||
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Publisher | IOS Press | Place of Publication | Editor | J. Vitrià, P. Radeva and I. Aguiló | |
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ISSN | ISBN | 978-1-58603-466-5 | Medium | ||
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Notes | MV;IAM;MILAB;SIAI | Approved | no | ||
Call Number | IAM @ iam @ VGR2004 | Serial | 1663 | ||
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Author | Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig | ||||
Title | Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis | Type | Book Chapter | ||
Year | 2015 | Publication | Artificial Intelligence Research and Development | Abbreviated Journal | |
Volume | 277 | Issue | Pages | 247 - 256 | |
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Abstract | Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. | ||||
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Publisher | IOS Press | Place of Publication | Editor | ||
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Series Editor | Series Title | Frontiers in Artificial Intelligence and Applications | Abbreviated Series Title | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @TVR2015 | Serial | 2780 | ||
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Author | Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus | ||||
Title | Color Naming | Type | Book Chapter | ||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
Volume | Issue | 17 | Pages | 287-317 | |
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Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | |
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ WBV2012 | Serial | 2063 | ||
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Author | David Geronimo; Angel Sappa; Antonio Lopez | ||||
Title | Stereo-based Candidate Generation for Pedestrian Protection Systems | Type | Book Chapter | ||
Year | 2010 | Publication | Binocular Vision: Development, Depth Perception and Disorders | Abbreviated Journal | |
Volume | Issue | 9 | Pages | 189–208 | |
Keywords | Pedestrian Detection | ||||
Abstract | This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. | ||||
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Publisher | NOVA Publishers | Place of Publication | Editor | ||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ GSL2010 | Serial | 1301 | ||
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Author | Fadi Dornaika; Bogdan Raducanu | ||||
Title | Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation | Type | Book Chapter | ||
Year | 2012 | Publication | Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions | Abbreviated Journal | |
Volume | Issue | Pages | 157-178 | ||
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Publisher | NOVA Publishers | Place of Publication | Editor | S.E. Carter | |
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Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ DoR2012 | Serial | 2183 | ||
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Author | Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh | ||||
Title | Facial expression recognition based on multi observations with application to social robotics | Type | Book Chapter | ||
Year | 2015 | Publication | Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance | Abbreviated Journal | |
Volume | Issue | Pages | 153-166 | ||
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Abstract | Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression. |
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Publisher | Nova Science publishers | Place of Publication | Editor | Bruce Flores | |
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Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ DRB2015 | Serial | 2720 | ||
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Author | Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez | ||||
Title | Stereo Vision Camera Pose Estimation for On-Board Applications | Type | Book Chapter | ||
Year | 2007 | Publication | Scene Reconstruction, Pose Estimation and Traking | Abbreviated Journal | |
Volume | Issue | Pages | 39-50 | ||
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Publisher | Rustam Stolking | Place of Publication | Editor | ||
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ISSN | ISBN | 978-3-902613-06-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ SGD2007 | Serial | 797 | ||
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Author | Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel | ||||
Title | Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures | Type | Book Chapter | ||
Year | 2010 | Publication | Bayesian Network | Abbreviated Journal | |
Volume | Issue | Pages | 13-37 | ||
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Publisher | Sciyo | Place of Publication | Editor | Ahmed Rebai | |
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ISSN | ISBN | 978-953-307-124-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ BDL2010 | Serial | 1461 | ||
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Author | Angel Sappa; Boris X. Vintimilla | ||||
Title | Edge Point Linking by Means of Global and Local Schemes | Type | Book Chapter | ||
Year | 2008 | Publication | in Signal Processing for Image Enhancement and Multimedia Processing | Abbreviated Journal | |
Volume | 11 | Issue | Pages | 115–125 | |
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Publisher | Springer | Place of Publication | Editor | E. Damiani | |
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ SaV2008 | Serial | 938 | ||
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Author | Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros | ||||
Title | From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example | Type | Book Chapter | ||
Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
Volume | Issue | 13 | Pages | 243-258 | |
Keywords | Domain Adaptation | ||||
Abstract | Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. | ||||
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Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
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Notes | ADAS; 600.085; 601.223; 600.076; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ LXG2017 | Serial | 2872 | ||
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Author | German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez | ||||
Title | Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA | Type | Book Chapter | ||
Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
Volume | 12 | Issue | Pages | 227-241 | |
Keywords | SYNTHIA; Virtual worlds; Autonomous Driving | ||||
Abstract | Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. | ||||
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Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ RSV2017 | Serial | 2882 | ||
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Author | Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel | ||||
Title | Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition | Type | Book Chapter | ||
Year | 2017 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 9657 | Issue | Pages | ||
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Publisher | Springer | Place of Publication | Editor | B. Lamiroy; R Dueire Lins | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ JLR2017 | Serial | 2928 | ||
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Author | Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio | ||||
Title | Introduction to NIPS 2017 Competition Track | Type | Book Chapter | ||
Year | 2018 | Publication | The NIPS ’17 Competition: Building Intelligent Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1-23 | ||
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Abstract | Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
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Publisher | Springer | Place of Publication | Editor | Sergio Escalera; Markus Weimer | |
Language | Summary Language | Original Title | |||
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ISSN | ISBN | 978-3-319-94042-7 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EWB2018 | Serial | 3200 | ||
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Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
Volume | 11009 | Issue | Pages | 81-95 | |
Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
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Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
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Author | Isabelle Guyon; Lisheng Sun Hosoya; Marc Boulle; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michele Sebag; Alexander R.Statnikov; Wei-Wei Tu; Evelyne Viegas | ||||
Title | Analysis of the AutoML Challenge Series 2015-2018. | Type | Book Chapter | ||
Year | 2019 | Publication | Automated Machine Learning | Abbreviated Journal | |
Volume | Issue | Pages | 177-219 | ||
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Abstract | The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/. | ||||
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Publisher | Springer | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | SSCML | ||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GHB2019 | Serial | 3330 | ||
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