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Author |
Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2018 |
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29th British Machine Vision Conference |
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Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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Newcastle; UK; September 2018 |
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BMVC |
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HUPBA; no proj |
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Admin @ si @ PSB2018 |
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3208 |
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Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo |
![download PDF file pdf](img/file_PDF.gif)
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Title |
2D-to-3D Facial Expression Transfer |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2018 |
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24th International Conference on Pattern Recognition |
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2008 - 2013 |
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Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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MSIAU; 600.086; 600.130; 600.118 |
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Admin @ si @ RLM2018 |
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3232 |
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Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu |
![goto web page url](img/www.gif)
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Title |
Reducing Label Effort: Self- Supervised Meets Active Learning |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2021 |
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International Conference on Computer Vision Workshops |
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1631-1639 |
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Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled. |
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October 2021 |
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ICCVW |
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LAMP; |
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no |
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Admin @ si @ ZVT2021 |
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3672 |
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Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer |
![goto web page url](img/www.gif)
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Title |
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2021 |
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19th International Conference on Computer Analysis of Images and Patterns |
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13052 |
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1 |
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403-413 |
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Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results. |
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September 2021 |
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CAIP |
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LAMP; |
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no |
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Admin @ si @ ZRV2021 |
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3673 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Single view facial hair 3D reconstruction |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
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11867 |
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423-436 |
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3D Vision; Shape Reconstruction; Facial Hair Modeling |
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n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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IbPRIA |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ |
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3707 |
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Author |
Chenshen Wu; Joost Van de Weijer |
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Title |
Density Map Distillation for Incremental Object Counting |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2505-2514 |
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We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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LAMP |
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no |
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Admin @ si @ WuW2023 |
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3916 |
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Author |
Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei |
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Title |
Surveillance Face Presentation Attack Detection Challenge |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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Year |
2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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6360-6370 |
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Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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HuPBA |
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no |
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Admin @ si @ FLW2023 |
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3917 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
![goto web page url](img/www.gif)
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Title |
Detailed 3D face reconstruction from a single RGB image |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2019 |
Publication |
Journal of WSCG |
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JWSCG |
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27 |
Issue |
2 |
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103-112 |
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3D Wrinkle Reconstruction; Face Analysis, Optimization. |
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This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
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2019/11 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3708 |
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Author |
Maria Vanrell; Jordi Vitria; Xavier Roca |
![goto web page (via DOI) doi](img/doi.gif)
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A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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1997 |
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Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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Felipe Lumbreras; Joan Serrat |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Segmentation of petrographical images of marbles |
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1996 |
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Computers and Geosciences |
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ADAS @ adas @ LuS1996b |
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F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra |
![goto web page (via DOI) doi](img/doi.gif)
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Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. |
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1994 |
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Kidney International |
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1721-1727 |
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BCNPCL @ bcnpcl @ MSV1994 |
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113 |
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A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Anisotropic 2-D and 3-D Averaging of fMRI Signals |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2001 |
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IEEE Transactions on Medical Imaging |
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2020 |
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86-93 |
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ADAS @ adas @ SNS2001 |
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165 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Rank Estimation in 3D Multibody Motion Segmentation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2008 |
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Electronic Letters |
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44 |
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4 |
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279-280 |
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A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data. |
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ADAS @ adas @ JSL2008a |
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939 |
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Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
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Dynamic Comparison of Headlights |
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2008 |
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Journal of Automobile Engineering |
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222 |
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5 |
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643–656 |
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video alignment |
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958 |
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C. Alejandro Parraga; Robert Benavente; Maria Vanrell |
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Towards a general model of colour categorization which considers context |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2010 |
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Perception. ECVP Abstract Supplement |
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PER |
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39 |
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86 |
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In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant dierences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the dierences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly dierent(more diuse) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we
completed our parametric fuzzy-sets model of colour naming space. |
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CAT @ cat @ PBV2010b |
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1326 |
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