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Author |
Marçal Rusiñol; Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny; Josep Llados |
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Title |
Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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Pages |
1594–1597 |
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Abstract |
In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues. We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms the sole use of the shape context descriptor. |
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Istanbul (Turkey) |
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ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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DAG |
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no |
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DAG @ dag @ RNK2010 |
Serial |
1435 |
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Author |
David Augusto Rojas; Fahad Shahbaz Khan; Joost Van de Weijer |
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Title |
The Impact of Color on Bag-of-Words based Object Recognition |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
1549–1553 |
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In recent years several works have aimed at exploiting color information in order to improve the bag-of-words based image representation. There are two stages in which color information can be applied in the bag-of-words framework. Firstly, feature detection can be improved by choosing highly informative color-based regions. Secondly, feature description, typically focusing on shape, can be improved with a color description of the local patches. Although both approaches have been shown to improve results the combined merits have not yet been analyzed. Therefore, in this paper we investigate the combined contribution of color to both the feature detection and extraction stages. Experiments performed on two challenging data sets, namely Flower and Pascal VOC 2009; clearly demonstrate that incorporating color in both feature detection and extraction significantly improves the overall performance. |
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Istanbul (Turkey) |
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1051-4651 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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no |
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Call Number |
CAT @ cat @ RKW2010 |
Serial |
1415 |
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Permanent link to this record |
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Author |
Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
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Title |
Perceptual color texture codebooks for retrieving in highly diverse texture datasets |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Pages |
866–869 |
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Abstract |
Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel. |
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Istanbul (Turkey) |
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1051-4651 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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Notes |
CIC |
Approved |
no |
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Call Number |
CAT @ cat @ ASV2010b |
Serial |
1426 |
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Permanent link to this record |
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Author |
Francesco Ciompi; Oriol Pujol; Petia Radeva |
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Title |
A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
710–713 |
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Abstract |
We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems. |
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Istanbul;Turkey |
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ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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Notes |
MILAB;HUPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ CPR2010a |
Serial |
1365 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Pages |
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 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ Amo2010 |
Serial |
1295 |
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Permanent link to this record |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
A Fast accurate Implicit Polynomial Fitting Approach |
Type |
Conference Article |
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Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Pages |
1429–1432 |
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This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons. |
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Hong-Kong |
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ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
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Conference |
ICIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ RoS2010b |
Serial |
1359 |
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Permanent link to this record |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space |
Type |
Conference Article |
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Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Pages |
2749–2752 |
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This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach. |
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Hong-Kong |
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ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
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ICIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ BLS2010 |
Serial |
1358 |
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Permanent link to this record |
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Author |
Antonio Hernandez; Miguel Reyes; Sergio Escalera; Petia Radeva |
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Title |
Spatio-Temporal GrabCut human segmentation for face and pose recovery |
Type |
Conference Article |
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Year |
2010 |
Publication |
IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
Abbreviated Journal |
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Pages |
33–40 |
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In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology. |
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Address |
San Francisco; CA; USA; June 2010 |
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ISSN |
2160-7508 |
ISBN |
978-1-4244-7029-7 |
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AMFG |
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Notes |
MILAB;HUPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ HRE2010 |
Serial |
1362 |
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Permanent link to this record |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Single Snapshot 3D Head Pose Initialization for Tracking in Human Robot Interaction Scenario |
Type |
Conference Article |
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Year |
2010 |
Publication |
1st International Workshop on Computer Vision for Human-Robot Interaction |
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32–39 |
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Keywords |
1st International Workshop on Computer Vision for Human-Robot Interaction, in conjunction with IEEE CVPR 2010 |
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Abstract |
This paper presents an automatic 3D head pose initialization scheme for a real-time face tracker with application to human-robot interaction. It has two main contributions. First, we propose an automatic 3D head pose and person specific face shape estimation, based on a 3D deformable model. The proposed approach serves to initialize our realtime 3D face tracker. What makes this contribution very attractive is that the initialization step can cope with faces
under arbitrary pose, so it is not limited only to near-frontal views. Second, the previous framework is used to develop an application in which the orientation of an AIBO’s camera can be controlled through the imitation of user’s head pose.
In our scenario, this application is used to build panoramic images from overlapping snapshots. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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Address |
San Francisco; CA; USA; June 2010 |
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ISSN |
2160-7508 |
ISBN |
978-1-4244-7029-7 |
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Conference |
CVPRW |
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Notes |
OR;MV |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ DoR2010a |
Serial |
1309 |
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Permanent link to this record |
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Author |
Michal Drozdzal; Laura Igual; Petia Radeva; Jordi Vitria; Carolina Malagelada; Fernando Azpiroz |
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Title |
Aligning Endoluminal Scene Sequences in Wireless Capsule Endoscopy |
Type |
Conference Article |
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Year |
2010 |
Publication |
IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis |
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Pages |
117–124 |
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Abstract |
Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences will help to find regions of similar intestinal activity and in this way will provide a valuable information on intestinal motility problems. This work, for first time, addresses the problem of aligning endoluminal sequences taking into account motion and structure of the intestine. To describe motility in the sequence, we propose different descriptors based on the Sift Flow algorithm, namely: (1) Histograms of Sift Flow Directions to describe the flow course, (2) Sift Descriptors to represent image intestine structure and (3) Sift Flow Magnitude to quantify intestine deformation. We show that the merge of all three descriptors provides robust information on sequence description in terms of motility. Moreover, we develop a novel methodology to rank the intestinal sequences based on the expert feedback about relevance of the results. The experimental results show that the selected descriptors are useful in the alignment and similarity description and the proposed method allows the analysis of the WCE. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
ISBN |
978-1-4244-7029-7 |
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Conference |
MMBIA |
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Notes |
OR;MILAB;MV |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ DIR2010 |
Serial |
1316 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
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Year |
2010 |
Publication |
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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ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
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CVPR |
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ADAS;ISE |
Approved |
no |
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Call Number |
ADAS @ adas @ AGL2010a |
Serial |
1302 |
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Permanent link to this record |
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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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Year |
2010 |
Publication |
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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English |
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English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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1063-6919 |
ISBN |
978-1-4244-6984-0 |
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CVPR |
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ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
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Permanent link to this record |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
Type |
Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Volume |
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Pages |
1046–1053 |
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Abstract |
We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
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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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Admin @ si @ ARL2010a |
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1311 |
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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 |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Title |
Automatic Point-based Facial Trait Judgments Evaluation |
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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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