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Author |
Anjan Dutta; Zeynep Akata |
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Title |
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval |
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Conference Article |
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Year |
2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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5089-5098 |
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Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. |
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Long beach; California; USA; June 2019 |
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DAG; 600.141; 600.121 |
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Admin @ si @ DuA2019 |
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3268 |
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Author |
Oriol Pujol; Eloi Puertas; Carlo Gatta |
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Title |
Multi-scale Stacked Sequential Learning |
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Conference Article |
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Year |
2009 |
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8th International Workshop of Multiple Classifier Systems |
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5519 |
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262–271 |
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One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. |
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Reykjavik, Iceland |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02325-5 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PPG2009 |
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1260 |
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Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
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Combining local and global bag-of-word representations for semantic segmentation |
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Conference Article |
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2009 |
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Workshop on The PASCAL Visual Object Classes Challenge |
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Kyoto (Japan) |
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ICCV |
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ADAS;ISE |
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ADAS @ adas @ BGS2009 |
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1273 |
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Author |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Title |
Automatic Point-based Facial Trait Judgments Evaluation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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2715–2720 |
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Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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OR;MV |
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BCNPCL @ bcnpcl @ RMT2010 |
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1282 |
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Author |
Santiago Segui; Laura Igual; Jordi Vitria |
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Title |
Weighted Bagging for Graph based One-Class Classifiers |
Type |
Conference Article |
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Year |
2010 |
Publication |
9th International Workshop on Multiple Classifier Systems |
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5997 |
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1-10 |
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Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MSTCD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MSTCD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data. |
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Cairo, Egypt |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-12126-5 |
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MILAB;OR;MV |
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no |
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BCNPCL @ bcnpcl @ SIV2010 |
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1284 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Seal Object Detection in Document Images using GHT of Local Component Shapes |
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Conference Article |
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Year |
2010 |
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10th ACM Symposium On Applied Computing |
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23–27 |
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Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents. |
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Sierre, Switzerland |
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SAC |
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DAG |
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no |
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DAG @ dag @ RPL2010a |
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1291 |
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Author |
Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
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Title |
Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles |
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Conference Article |
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2010 |
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Colloque International Francophone sur l'Écrit et le Document |
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169-184 |
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Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition |
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We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented. |
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Sousse, Tunisia |
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CIFED |
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DAG |
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no |
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DAG @ dag @ LBR2010a |
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1293 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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4246–4250 |
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Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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ADAS |
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ADAS @ adas @ Amo2010 |
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1295 |
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Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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Author |
Naila Murray; Eduard Vazquez |
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Title |
Lacuna Restoration: How to choose a neutral colour? |
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Conference Article |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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248–252 |
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Painting restoration which involves filling in material loss (called lacuna) is a complex process. Several standard techniques exist to tackle lacuna restoration,
and this article focuses on those techniques that employ a “neutral” colour to mask the defect. Restoration experts often disagree on the choice of such a colour and in fact, the concept of a neutral colour is controversial. We posit that a neutral colour is one that attracts relatively little visual attention for a specific lacuna. We conducted an eye tracking experiment to compare two common neutral
colour selection methods, specifically the most common local colour and the mean local colour. Results obtained demonstrate that the most common local colour triggers less visual attention in general. Notwithstanding, we have observed instances in which the most common colour triggers a significant amount of attention when subjects spent time resolving their confusion about whether or not a lacuna was part of the painting. |
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Gjovik, Norway |
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CREATE |
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CIC |
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Admin @ si @ MuV2010 |
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1297 |
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Marta Teres; Eduard Vazquez |
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Museums, spaces and museographical resources. Current state and proposals for a multidisciplinary framework to open new perspectives |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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319–323 |
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Two of the main aims of a museum are to communicate its heritage and to make enjoy its visitors. This communication can be done through the pieces itself and the museographical resources but also through the building, the interior design, the light and the colour. Art museums, in opposition with other museums, lack on the application of these additional resources. Such a work necessarily requires a multidisciplinary point of view for a holistic vision of all what a museum implies and to use all its potential as a tool of knowledge and culture for all the visitors. |
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Gjovik, Norway |
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CREATE |
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Admin @ si @ TeV2010 |
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1298 |
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Author |
Eduard Vazquez; Ramon Baldrich |
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Title |
Non-supervised goodness measure for image segmentation |
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Conference Article |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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334–335 |
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Gjovik, Norway |
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CAT @ cat @ VaB2010 |
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1299 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
3D Scene Priors for Road Detection |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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57–64 |
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road detection |
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Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS @ adas @ AGL2010a |
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1302 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3066-3072 |
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This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS @ adas @ RoS2010a |
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1303 |
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Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
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Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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137–144 |
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Pedestrian Detection; Domain Adaptation |
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Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
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San Francisco; CA; USA; June 2010 |
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Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS @ adas @ MVG2010 |
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1304 |
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