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Author |
Sergio Escalera |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A |
Type |
Book Whole |
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Year |
2008 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
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Abstract |
Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However,
the extension of many 2-class classifiers to the multi-class case is a hard task. In
this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful
tool to combine any number of binary classifiers to model multi-class problems. But
there are still many open issues about the capabilities of the ECOC framework. In
this thesis, the two main stages of an ECOC design are analyzed: the coding and
the decoding steps. We present different problem-dependent designs. These designs
take advantage of the knowledge of the problem domain to minimize the number
of classifiers, obtaining a high classification performance. On the other hand, we
analyze the ECOC codification in order to define new decoding rules that take full
benefit from the information provided at the coding step. Moreover, as a successful
classification requires a rich feature set, new feature detection/extraction techniques
are presented and evaluated on the new ECOC designs. The evaluation of the new
methodology is performed on different real and synthetic data sets: UCI Machine
Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease
data set. The results of this thesis show that significant performance improvements
are obtained on both traditional coding and decoding ECOC designs when the new
coding and decoding rules are taken into account. |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Petia Radeva;Oriol Pujol |
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MILAB; HuPBA |
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no |
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Call Number |
Admin @ si @ Esc2008b |
Serial |
2217 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann |
![download PDF file pdf](img/file_PDF.gif)
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Title |
When Is A Confidence Measure Good Enough? |
Type |
Conference Article |
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Year |
2013 |
Publication |
9th International Conference on Computer Vision Systems |
Abbreviated Journal |
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Volume |
7963 |
Issue |
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Pages |
344-353 |
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Keywords |
Optical flow, confidence measure, performance evaluation |
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Abstract |
Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality. |
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Address |
St Petersburg; Russia; July 2013 |
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Springer Link |
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LNCS |
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0302-9743 |
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978-3-642-39401-0 |
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ICVS |
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Notes |
IAM;ADAS; 600.044; 600.057; 600.060; 601.145 |
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Call Number |
IAM @ iam @ MGH2013a |
Serial |
2218 |
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Author |
David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes |
Type |
Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
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Pages |
706 - 711 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. |
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Portland; Oregon; June 2013 |
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IEEE |
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English |
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English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
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no |
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Call Number |
ADAS @ adas @ VXR2013a |
Serial |
2219 |
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Author |
Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers |
Type |
Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
Abbreviated Journal |
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Pages |
688 - 693 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%. |
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Portland; oregon; June 2013 |
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English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
yes |
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Call Number |
XVR2013; ADAS @ adas @ xvr2013a |
Serial |
2220 |
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Author |
David Augusto Rojas |
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Title |
Colouring Local Feature Detection for Matching |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
133 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Call Number |
Admin @ si @ Roj2009 |
Serial |
2392 |
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Author |
Juan Diego Gomez |
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Title |
Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
134 |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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MILAB |
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no |
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Call Number |
Admin @ si @ Gom2009 |
Serial |
2393 |
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Author |
Olivier Penacchio |
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Title |
Relative Density of L, M, S photoreceptors in the Human Retina |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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135 |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Pen2009 |
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2394 |
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Author |
Xavier Boix |
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Title |
Learning Conditional Random Fields for Stereo |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
136 |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Boi2009 |
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2395 |
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Author |
Shida Beigpour |
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Title |
Physics-based Reflectance Estimation Applied to Recoloring |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
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137 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Bei2009 |
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2396 |
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Author |
Jaume Gibert |
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Title |
Learning structural representations and graph matching paradigms in the context of object recognition |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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143 |
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Computer Vision Center |
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Master's thesis |
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DAG |
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no |
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Call Number |
Admin @ si @ Gib2009 |
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2397 |
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Author |
Jose Carlos Rubio |
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Title |
Graph matching based on graphical models with application to vehicle tracking and classification at night |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
144 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Rub2009 |
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2398 |
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Author |
Farshad Nourbakhsh |
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Title |
Colour logo recognition |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
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Volume |
145 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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DAG |
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no |
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Call Number |
Admin @ si @ Nou2009 |
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2399 |
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Author |
Enric Sala |
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Title |
Off-line person-dependent signature verification |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
146 |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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no |
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Call Number |
Admin @ si @ Sal2009 |
Serial |
2400 |
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Author |
Wenjuan Gong |
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Title |
Action priors for human pose tracking by particle filter |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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ISE |
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no |
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Call Number |
Admin @ si @ Gon2009 |
Serial |
2401 |
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Author |
Diego Alejandro Cheda |
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Title |
Monocular egomotion estimation for ADAS application |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
148 |
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Corporate Author |
Computer Vision Center |
Thesis |
Ph.D. thesis |
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Bellaterra, Barcelona |
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no |
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Admin @ si @ Che2009 |
Serial |
2402 |
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