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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Virtual Worlds and Active Learning for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
393-400 |
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Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
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Address |
Alicante, Spain |
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Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
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Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
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978-1-4503-0641-6 |
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Conference |
ICMI |
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Notes |
ADAS |
Approved |
yes |
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Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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Permanent link to this record |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
363-370 |
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Keywords |
Pedestrian Detection; Color; Part Based Models |
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Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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Language |
English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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IbPRIA |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ RVL2011a |
Serial |
1666 |
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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 |
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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Pages |
137–144 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
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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Address |
San Francisco; CA; USA; June 2010 |
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English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
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Conference |
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 |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
6855 |
Issue |
II |
Pages |
463-470 |
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Keywords |
Pedestrian Detection; Color |
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Abstract |
Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. |
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Address |
Seville, Spain |
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Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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Language |
English |
Summary Language |
english |
Original Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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0302-9743 |
ISBN |
978-3-642-23677-8 |
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Conference |
CAIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ RVL2011b |
Serial |
1665 |
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Permanent link to this record |
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Author |
Josep Llados; J. Lopez-Krahe; Enric Marti |
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Title |
A Hough-based method for hatched pattern detection in maps and diagrams. |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the International Conference on Document Analysis and Recognition. |
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Bangalore-India |
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DAG |
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no |
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Call Number |
DAG @ dag @ LlM1999b |
Serial |
1 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution colour texture representations for tile classification |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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Bilbao |
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ADAS;CIC |
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no |
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ADAS @ adas @ LBV1999a |
Serial |
3 |
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Author |
Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria |
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Title |
Regularized EM |
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Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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invisible;ADAS;OR;MILAB;MV |
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no |
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ADAS @ adas @ PSL1999 |
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4 |
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Author |
David Guillamet; Jordi Vitria |
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Title |
Using Eigenspace analysis of color distributions for object recognition |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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OR;MV |
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BCNPCL @ bcnpcl @ GuV1999a |
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5 |
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Author |
A. Pujol; Felipe Lumbreras; X. Varona; Juan J. Villanueva |
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Title |
Template matching through invariant eigenspace projection. |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ADAS |
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ADAS @ adas @ PLV1999 |
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6 |
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Author |
Josep Llados; Felipe Lumbreras; X. Varona |
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Title |
A multidocument platform for automatic reading of identity cards. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ADAS;DAG |
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ADAS @ adas @ LLV1999 |
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7 |
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Author |
A. Martinez; Jordi Vitria |
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Title |
Learning mixture models with the EM algorithm and genetic algorithms |
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Report |
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1998 |
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CVC Technical Report #23 |
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CVC (UAB) |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1998a |
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8 |
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Author |
A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; J. Saludes |
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Title |
Crease enhancement diffusion |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS;MILAB |
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ADAS @ adas @ SLC1999 |
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9 |
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Author |
X. Varona; A. Pujol; Juan J. Villanueva |
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Title |
Visual tracking in application domains. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ISE @ ise @ VPV1999 |
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10 |
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Author |
Antonio Lopez; David Lloret; Joan Serrat |
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Title |
Creaseness measures for CT and MR image registration. |
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Miscellaneous |
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1998 |
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CVPR’98 , IEEE Computer Society, pgs.694–699 |
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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 |
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Address |
Santa Barbara, USA. |
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ADAS |
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no |
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ADAS @ adas @ LLS1998a |
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11 |
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Permanent link to this record |
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Author |
Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Title |
Creaseness form level set extrinsec curvature. |
Type |
Miscellaneous |
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Year |
1998 |
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5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 |
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Freiburg, Germany. |
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ADAS @ adas @ LLS1998b |
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12 |
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