| Home | [91–100] << 101 102 103 104 105 106 107 108 109 110 >> [111–120] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
| Title | Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes | Type | Conference Article | ||
| Year | 2013 | Publication  |
CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
| Volume | Issue | Pages | 706 - 711 | ||
| Keywords | Pedestrian Detection; Domain Adaptation | ||||
| 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. | ||||
| Address | Portland; Oregon; June 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | ADAS; 600.054; 600.057; 601.217 | Approved | no | ||
| Call Number | ADAS @ adas @ VXR2013a | Serial | 2219 | ||
| Permanent link to this record | |||||
| Author | Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
| Title | Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers | Type | Conference Article | ||
| Year | 2013 | Publication |
CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
| Volume | Issue | Pages | 688 - 693 | ||
| Keywords | Pedestrian Detection; Domain Adaptation | ||||
| 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%. | ||||
| Address | Portland; oregon; June 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | ADAS; 600.054; 600.057; 601.217 | Approved | yes | ||
| Call Number | XVR2013; ADAS @ adas @ xvr2013a | Serial | 2220 | ||
| Permanent link to this record | |||||
| Author | Antonio Lopez; David Lloret; Joan Serrat | ||||
| Title | Creaseness measures for CT and MR image registration. | Type | Miscellaneous | ||
| Year | 1998 | Publication |
CVPR’98 , IEEE Computer Society, pgs.694–699 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy | ||||
| Address | Santa Barbara, USA. | ||||
| 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 @ LLS1998a | Serial | 11 | ||
| Permanent link to this record | |||||
| Author | M. Bressan; David Guillamet; Jordi Vitria | ||||
| Title | Multiclass Object Recognition using Class-Conditional Independent Component Analisis | Type | Journal | ||
| Year | 2004 | Publication |
Cybernetics and Systems, 35/1:35–61 (IF: 0.768) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| 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 | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BGV2004 | Serial | 442 | ||
| Permanent link to this record | |||||
| Author | Josep Llados; Horst Bunke; Enric Marti | ||||
| Title | Using cyclic string matching to find rotational and reflectional symmetric shapes | Type | Conference Article | ||
| Year | 1996 | Publication |
Dagstuhl Seminar on Modelling and Planning for Sensor–based Intelligent Robot Systems | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | World Scientific | Place of Publication | Saarbrucken (Germany). | Editor | R.C. Bolles, H.B.H.N. |
| 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;IAM | Approved | no | ||
| Call Number | IAM @ iam @ LBM1996 | Serial | 1564 | ||
| Permanent link to this record | |||||
| Author | Jaime Moreno; Xavier Otazu | ||||
| Title | Image coder based on Hilbert scanning of embedded quadTrees | Type | Conference Article | ||
| Year | 2011 | Publication |
Data Compression Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 470-470 | ||
| Keywords | |||||
| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. | ||||
| 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 | DCC | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MoO2011b | Serial | 2177 | ||
| Permanent link to this record | |||||
| Author | Laura Lopez-Fuentes; Claudio Rossi; Harald Skinnemoen | ||||
| Title | River segmentation for flood monitoring | Type | Conference Article | ||
| Year | 2017 | Publication |
Data Science for Emergency Management at Big Data 2017 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation. | ||||
| 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 | LAMP; 600.084; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LRS2017 | Serial | 3078 | ||
| 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 | ||
| Keywords | |||||
| 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. |
||||
| 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 | Enric Marti; J. Rocarias; Petia Radeva; H. Tizon; Jordi Vitria | ||||
| Title | Caronte. Un gestor documental para asignaturas de universidad en el EEES | Type | Miscellaneous | ||
| Year | 2007 | Publication |
Desarrollo de gestion de grupos, encuestas y autoevaluacion, MoodleMoot 2007 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| 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;OR;MILAB;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MRR2007b | Serial | 1128 | ||
| Permanent link to this record | |||||
| Author | Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance | Type | Book Chapter | ||
| Year | 2012 | Publication |
Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence | Abbreviated Journal | |
| Volume | 384 | Issue | 3 | Pages | 87-95 |
| Keywords | |||||
| Abstract | The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-24033-1 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ BFR2012 | Serial | 2062 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre | ||||
| Title | A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts | Type | Conference Article | ||
| Year | 2014 | Publication |
Digital Access to Textual Cultural Heritage Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 103-108 | ||
| Keywords | |||||
| Abstract | In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts. | ||||
| Address | Madrid; May 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 | 978-1-4503-2588-2 | Medium | ||
| Area | Expedition | Conference | DATeCH | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ FLM2014 | Serial | 2516 | ||
| Permanent link to this record | |||||
| Author | Josep Llados | ||||
| Title | Advances in Graphics Recognition | Type | Book Chapter | ||
| Year | 2007 | Publication |
Digital Document Processing, Major Directions and Recent Advances, Advances in Pattern Recognition, B.B. Chaudhuri, ed., 281–304 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Springer London | ||||
| 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 | Approved | no | ||
| Call Number | DAG @ dag @ Lla2007 | Serial | 780 | ||
| Permanent link to this record | |||||
| Author | Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
| Title | The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities | Type | Conference Article | ||
| Year | 2016 | Publication |
Digital Humanities Centres: Experiences and Perspectives | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Warsaw; Poland; December 2016 | ||||
| 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 | DHLABS | ||
| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ VFV2016 | Serial | 2908 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Beata Megyesi; Joan Mas | ||||
| Title | Transcription of Encoded Manuscripts with Image Processing Techniques | Type | Conference Article | ||
| Year | 2017 | Publication |
Digital Humanities Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 441-443 | ||
| 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 | DH | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ FMM2017 | Serial | 3061 | ||
| Permanent link to this record | |||||
| Author | Agnes Borras | ||||
| Title | High-Level Clothes Description Based on Colour-Texture Features. | Type | Miscellaneous | ||
| Year | 2002 | Publication |
Director: J. Llados, Master Thesis. | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| 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 | Approved | no | |||
| Call Number | DAG @ dag @ Bor2002 | Serial | 322 | ||
| Permanent link to this record | |||||