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Author |
Susana Alvarez; Maria Vanrell |
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Title |
Texton theory revisited: a bag-of-words approach to combine textons |
Type |
Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
12 |
Pages |
4312-4325 |
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Abstract |
The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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0031-3203 |
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no |
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Admin @ si @ AlV2012a |
Serial |
2130 |
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Author |
Aura Hernandez-Sabate; Debora Gil |
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Title |
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Intravascular Ultrasound |
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Pages |
185-206 |
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Intech |
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Editor |
Yasuhiro Honda |
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Language |
English |
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english |
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978-953-307-900-4 |
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IAM; ADAS |
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no |
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IAM @ iam @ HeG2012 |
Serial |
1684 |
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Author |
David Masip; Alexander Todorov; Jordi Vitria |
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Title |
The Role of Facial Regions in Evaluating Social Dime |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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Volume |
7584 |
Issue |
II |
Pages |
210-219 |
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Keywords |
Workshops and Demonstrations |
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Abstract |
Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics. |
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Florence, Italy |
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Springer Berlin Heidelberg |
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Andrea Fusiello, Vittorio Murino, Rita Cucchiara |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33867-0 |
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ECCVW |
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Notes |
OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ MTV2012 |
Serial |
2171 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback |
Type |
Conference Article |
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Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
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Pages |
55-60 |
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In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model. |
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Bari, Italy |
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978-1-4673-2262-1 |
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ICFHR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ RuL2012 |
Serial |
2054 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Three-Dimensional Design of Error Correcting Output Codes |
Type |
Conference Article |
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Year |
2012 |
Publication |
8th International Conference on Machine Learning and Data Mining |
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Pages |
29- |
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Berlin, Germany |
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MLDM |
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Notes |
HuPBA;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ BGE2012a |
Serial |
2041 |
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Permanent link to this record |
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Author |
Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin |
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Title |
Towards automatic and flexible concept transfer |
Type |
Journal Article |
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Year |
2012 |
Publication |
Computers and Graphics |
Abbreviated Journal |
CG |
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Volume |
36 |
Issue |
6 |
Pages |
622–634 |
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Abstract |
This paper introduces a novel approach to automatic, yet flexible, image concepttransfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The presented method modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This method is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. Our framework is flexible for two reasons. First, the user may select one of two modalities to map input image chromaticities to target concept chromaticities depending on the level of photo-realism required. Second, the user may adjust the intensity level of the concepttransfer to his/her liking with a single parameter. The proposed method uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. Results show that our approach yields transferred images which effectively represent concepts as confirmed by a user study. |
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0097-8493 |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ MSM2012 |
Serial |
2002 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
Type |
Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
Pages |
3166-3182 |
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Keywords |
Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
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Abstract |
This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
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Elsevier |
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0031-3203 |
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800 |
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Conference |
IbPRIA |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ BSV2012; IAM @ iam |
Serial |
1997 |
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Permanent link to this record |
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Author |
Josep M. Gonfaus |
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Title |
Towards Deep Image Understanding: From pixels to semantics |
Type |
Book Whole |
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Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity.
Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Jordi Gonzalez;Theo Gevers |
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ISE |
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no |
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Call Number |
Admin @ si @ Gon2012 |
Serial |
2208 |
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Author |
Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez |
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Title |
Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence |
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Volume |
384 |
Issue |
3 |
Pages |
87-95 |
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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. |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
1860-949X |
ISBN |
978-3-642-24033-1 |
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Notes |
ISE |
Approved |
no |
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Call Number |
Admin @ si @ BFR2012 |
Serial |
2062 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers |
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Title |
Understanding Road Scenes using Visual Cues |
Type |
Miscellaneous |
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Year |
2012 |
Publication |
European Conference on Computer Vision |
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DEMO |
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Address |
Florence; Italy |
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ISE |
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no |
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Call Number |
Admin @ si @ ALL2012 |
Serial |
2795 |
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Author |
Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo |
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Title |
Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models |
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Book Chapter |
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Year |
2012 |
Publication |
Vision Robotics: Technologies for Machine Learning and Vision Applications |
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Issue |
2 |
Pages |
23-42 |
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Abstract |
Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis. |
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IGI-Global |
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MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ PIE2012 |
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2064 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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749-756 |
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Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
ISBN |
978-1-4673-1226-4 |
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CVPR |
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ADAS |
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no |
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Admin @ si @ RSL2012b; ADAS @ adas @ |
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2033 |
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Permanent link to this record |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
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Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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3492 - 3495 |
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Pedestrian Detection; Domain Adaptation; Virtual worlds |
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Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
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Tsukuba Science City, Japan |
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IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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ICPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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Permanent link to this record |
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Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis |
Type |
Conference Article |
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Year |
2012 |
Publication |
7th Conference on Articulated Motion and Deformable Objects |
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7378 |
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1-11 |
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Abstract |
We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Mallorca |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-31566-4 |
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HUPBA;MILAB |
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2010 |
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Author |
Jaume Gibert |
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Title |
Vector Space Embedding of Graphs via Statistics of Labelling Information |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.
Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.
In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Ernest Valveny |
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2204 |
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