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Author | B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva | ||||
Title | Learning Deep Features for Scene Recognition using Places Database | Type ![]() |
Conference Article | ||
Year | 2014 | Publication | 28th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
Volume | Issue | Pages | 487-495 | ||
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Abstract | |||||
Address | Montreal; Canada; December 2014 | ||||
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 | NIPS | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ ZLX2014 | Serial | 2621 | ||
Permanent link to this record | |||||
Author | Agata Lapedriza; David Masip; David Sanchez | ||||
Title | Emotions Classification using Facial Action Units Recognition | Type ![]() |
Conference Article | ||
Year | 2014 | Publication | 17th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | 269 | Issue | Pages | 55-64 | |
Keywords | |||||
Abstract | In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-61499-451-0 | Medium | ||
Area | Expedition | Conference | CCIA | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ LMS2014 | Serial | 2622 | ||
Permanent link to this record | |||||
Author | Youssef El Rhabi; Simon Loic; Brun Luc | ||||
Title | Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration | ||||
Abstract | Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data. | ||||
Address | Amiens; France; June 2015 | ||||
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 | ORASIS | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RLL2015 | Serial | 2626 | ||
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Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
Title | Multi-observation Face Recognition in Videos based on Label Propagation | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 | Abbreviated Journal | |
Volume | Issue | Pages | 10-17 | ||
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Abstract | In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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Address | Boston; USA; June 2015 | ||||
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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 | CVPRW | ||
Notes | LAMP; 600.068; 600.072; | Approved | no | ||
Call Number | Admin @ si @ RBD2015 | Serial | 2627 | ||
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Author | M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa | ||||
Title | Cross-spectral image registration and fusion: an evaluation study | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 2nd International Conference on Machine Vision and Machine Learning | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | multispectral imaging; image registration; data fusion; infrared and visible spectra | ||||
Abstract | This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Address | Barcelona; July 2015 | ||||
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 | MVML | ||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ CAV2015 | Serial | 2629 | ||
Permanent link to this record | |||||
Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo | ||||
Title | LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 22th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 178 - 181 | ||
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Abstract | |||||
Address | Quebec; Canada; September 2015 | ||||
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 | ICIP | ||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ AST2015 | Serial | 2630 | ||
Permanent link to this record | |||||
Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company | ||||
Title | Brightness and colour induction through contextual influences in V1 | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | Scottish Vision Group 2015 SGV2015 | Abbreviated Journal | |
Volume | 12 | Issue | 9 | Pages | 1208-2012 |
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Abstract | |||||
Address | Carnoustie; Scotland; March 2015 | ||||
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 | SGV | ||
Notes | NEUROBIT; | Approved | no | ||
Call Number | Admin @ si @ OPC2015a | Serial | 2632 | ||
Permanent link to this record | |||||
Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris | ||||
Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | |||||
Address | Liverpool; uk; August 2015 | ||||
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 | ECVP | ||
Notes | NEUROBIT; | Approved | no | ||
Call Number | Admin @ si @ POW2015 | Serial | 2633 | ||
Permanent link to this record | |||||
Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company | ||||
Title | An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | Barcelona Computational, Cognitive and Systems Neuroscience | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | |||||
Address | Barcelona; June 2015 | ||||
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 | BARCCSYN | ||
Notes | NEUROBIT; | Approved | no | ||
Call Number | Admin @ si @ OPC2015b | Serial | 2634 | ||
Permanent link to this record | |||||
Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
Title | Learning to count with deep object features | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
Volume | Issue | Pages | 90-96 | ||
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Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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Address | Boston; USA; June 2015 | ||||
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 | CVPRW | ||
Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
Permanent link to this record | |||||
Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
Title | Active labeling application applied to food-related object recognition | Type ![]() |
Conference Article | ||
Year | 2013 | Publication | 5th International Workshop on Multimedia for Cooking & Eating Activities | Abbreviated Journal | |
Volume | Issue | Pages | 45-50 | ||
Keywords | |||||
Abstract | Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015]. |
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Address | Barcelona; October 2013 | ||||
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 | ACM-CEA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BGR2013b | Serial | 2637 | ||
Permanent link to this record | |||||
Author | Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva | ||||
Title | Visual Summary of Egocentric Photostreams by Representative Keyframes | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | IEEE International Conference on Multimedia and Expo ICMEW2015 | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
Keywords | egocentric; lifelogging; summarization; keyframes | ||||
Abstract | Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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Address | Torino; italy; July 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | 978-1-4799-7079-7 | Edition | ||
ISSN | ISBN | 978-1-4799-7079-7 | Medium | ||
Area | Expedition | Conference | ICME | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BMT2015 | Serial | 2638 | ||
Permanent link to this record | |||||
Author | Nuria Cirera; Alicia Fornes; Josep Llados | ||||
Title | Hidden Markov model topology optimization for handwriting recognition | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
Volume | Issue | Pages | 626-630 | ||
Keywords | |||||
Abstract | In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Address | Nancy; France; August 2015 | ||||
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 | ICDAR | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
Permanent link to this record | |||||
Author | Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados | ||||
Title | Document Analysis Techniques for Automatic Electoral Document Processing: A Survey | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 139-141 | ||
Keywords | Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally | ||||
Abstract | In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. | ||||
Address | Bern; Switzerland; September 2015 | ||||
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 | VoteID | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ TCP2015 | Serial | 2641 | ||
Permanent link to this record | |||||
Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Handwritten Word Spotting by Inexact Matching of Grapheme Graphs | Type ![]() |
Conference Article | ||
Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
Volume | Issue | Pages | 781 - 785 | ||
Keywords | |||||
Abstract | This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. | ||||
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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 | ICDAR | ||
Notes | DAG; 600.077; 600.061; 602.006 | Approved | no | ||
Call Number | Admin @ si @ RLF2015b | Serial | 2642 | ||
Permanent link to this record |