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Author |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Colour Normalisation Based on Background Information. |
Type |
Miscellaneous |
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2001 |
Publication |
Proceeding ICIP 2001, IEEE International Conference on Image Processing |
Abbreviated Journal |
ICIP 2001 |
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1 |
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874–877 |
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Grecia. |
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ADAS;DAG;CIC |
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no |
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ADAS @ adas @ VLP2001 |
Serial |
167 |
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Permanent link to this record |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
An Iterative Multiresolution Scheme for SFM |
Type |
Conference Article |
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2006 |
Publication |
International Conference on Image Analysis and Recognition |
Abbreviated Journal |
ICIAR 2006 |
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LNCS 4141 |
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1 |
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804–815 |
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ADAS |
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no |
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ADAS @ adas @ JSL2006c |
Serial |
704 |
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Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Asymmetric Distances for Binary Embeddings |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
1 |
Pages |
33-47 |
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Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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no |
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Admin @ si @ GPG2014 |
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2272 |
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Permanent link to this record |
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Author |
Josep Llados; Dorothea Blostein |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Special Issue on Graphics Recognition |
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Journal |
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2007 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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Volume |
9 |
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1 |
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1–2 |
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DAG |
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DAG @ dag @ LlB2007 |
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781 |
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Permanent link to this record |
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Author |
Ignasi Rius; Jordi Gonzalez; Mikhail Mozerov; Xavier Roca |
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Title |
Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis |
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2008 |
Publication |
International Journal for Computational Vision and Biomechanics |
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1 |
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1 |
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33–43 |
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ISE |
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no |
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Call Number |
ISE @ ise @ RGM2008 |
Serial |
1020 |
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Permanent link to this record |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
Type |
Journal Article |
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Year |
2009 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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Volume |
10 |
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1 |
Pages |
113–126 |
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Abstract |
The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ BEV2008 |
Serial |
1116 |
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Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System |
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Journal |
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Year |
2009 |
Publication |
Pattern Recognition and Image Analysis |
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Volume |
19 |
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1 |
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165-171 |
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An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path. |
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1054-6618 |
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ISE |
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ISE @ ise @ MAR2009a |
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1160 |
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Permanent link to this record |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach |
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Journal Article |
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Year |
2009 |
Publication |
International Journal of Electronic Commerce |
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14 |
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1 |
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89-108 |
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The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach. |
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1086-4415 |
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ADAS |
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ADAS @ adas @ JSL2009b |
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1237 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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2010 |
Publication |
IEEE on Pattern Analysis and Machine Intelligence |
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TPAMI |
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32 |
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120–134 |
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A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010b |
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1277 |
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Permanent link to this record |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes |
Type |
Journal Article |
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Year |
2010 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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28 |
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164-176 |
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Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach. |
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0262-8856 |
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ADAS |
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ADAS @ adas @ JSL2010 |
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1278 |
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Permanent link to this record |
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Author |
Bogdan Raducanu; D. Gatica-Perez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Inferring competitive role patterns in reality TV show through nonverbal analysis |
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Journal Article |
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2012 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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56 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
1 |
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207-226 |
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This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority. |
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Elsevier |
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1380-7501 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaG2012 |
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1360 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Learning photometric invariance for object detection |
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Journal Article |
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2010 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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90 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
1 |
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45-61 |
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Keywords |
road detection |
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Abstract |
Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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Springer US |
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0920-5691 |
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ADAS;ISE |
Approved |
no |
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ADAS @ adas @ AGL2010c |
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1451 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Road Detection Based on Illuminant Invariance |
Type |
Journal Article |
|
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2011 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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12 |
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1 |
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184-193 |
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road detection |
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By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. |
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ADAS @ adas @ AlL2011 |
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1456 |
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Debora Gil; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Extending anisotropic operators to recover smooth shapes |
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2005 |
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Computer Vision and Image Understanding |
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99 |
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110-125 |
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Contour completion; Functional extension; Differential operators; Riemmanian manifolds; Snake segmentation |
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Anisotropic differential operators are widely used in image enhancement processes. Recently, their property of smoothly extending functions to the whole image domain has begun to be exploited. Strong ellipticity of differential operators is a requirement that ensures existence of a unique solution. This condition is too restrictive for operators designed to extend image level sets: their own functionality implies that they should restrict to some vector field. The diffusion tensor that defines the diffusion operator links anisotropic processes with Riemmanian manifolds. In this context, degeneracy implies restricting diffusion to the varieties generated by the vector fields of positive eigenvalues, provided that an integrability condition is satisfied. We will use that any smooth vector field fulfills this integrability requirement to design line connection algorithms for contour completion. As application we present a segmenting strategy that assures convergent snakes whatever the geometry of the object to be modelled is. |
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1077-3142 |
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IAM @ iam @ GIR2005 |
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1530 |
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Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
![download PDF file pdf](img/file_PDF.gif)
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Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences |
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2011 |
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IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
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T-UFFC |
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58 |
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60-72 |
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3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging |
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Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. |
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0885-3010 |
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IAM;ADAS |
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IAM @ iam @ HGG2011 |
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1546 |
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