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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
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Journal Article |
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2018 |
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Entropy |
Abbreviated Journal |
ENTROPY |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
20 |
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11 |
Pages |
809 |
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Keywords |
hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
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HUPBA; no proj |
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Admin @ si @ RKE2018 |
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3198 |
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Author |
Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants |
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Journal Article |
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2018 |
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IEEE Transactions on Multimedia |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
20 |
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12 |
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3266 - 3275 |
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The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. |
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MILAB; no proj |
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Admin @ si @ ARB2018 |
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3236 |
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Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez |
![goto web page url](img/www.gif)
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Title |
Recognizing new classes with synthetic data in the loop: application to traffic sign recognition |
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Journal Article |
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2020 |
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Sensors |
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SENS |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
20 |
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3 |
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583 |
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On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. |
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LAMP; ADAS; 600.118; 600.120 |
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no |
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Admin @ si @ VWL2020 |
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3405 |
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Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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Journal Article |
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2020 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
20 |
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11 |
Pages |
3249 |
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stereo matching; deep learning; embedded GPU |
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Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. |
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MSIAU; 600.122 |
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no |
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Admin @ si @ AAN2020 |
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3428 |
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Author |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities |
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Journal Article |
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2020 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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20 |
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16 |
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4587 |
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This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. |
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MSIAU; 600.130; 601.349; 600.122 |
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no |
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Admin @ si @ MSM2020 |
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3452 |
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Author |
Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem |
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2020 |
Publication |
Mathematics |
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MATH |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
20 |
Issue |
8(9) |
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1595 |
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STEM education; Project-based learning; Coding; software tool |
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In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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September 2020 |
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IAM; ISE |
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no |
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Admin @ si @ |
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3722 |
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Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
![goto web page url](img/www.gif)
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Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
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Journal Article |
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2023 |
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ACM Transactions on Multimedia Computing, Communications, and Applications |
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TMCCA |
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20 |
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2 |
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1–20 |
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Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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ISE |
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Admin @ si @ GZW2023 |
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3862 |
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Author |
A. Martinez; Jordi Vitria |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Learning mixture models using a genetic version of the EM algorithm. |
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2000 |
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Pattern Recognition Letters |
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PRL |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](img/sort_asc.gif) |
21 |
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8 |
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759–769 |
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OR;MV |
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BCNPCL @ bcnpcl @ MVi2000 |
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335 |
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Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
On Tracking Inside Groups |
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2010 |
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Machine Vision and Applications |
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MVA |
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21 |
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2 |
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113–127 |
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This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions. |
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Springer-Verlag |
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0932-8092 |
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ISE |
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ISE @ ise @ RGP2010 |
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1158 |
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Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
![goto web page (via DOI) doi](img/doi.gif)
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Intestinal motor activity, endoluminal motion and transit |
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2009 |
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Neurogastroenterology & Motility |
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NEUMOT |
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21 |
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12 |
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1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Traffic sign recognition system with β -correction |
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2010 |
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Machine Vision and Applications |
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MVA |
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21 |
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2 |
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99–111 |
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Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. |
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0932-8092 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010a |
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1276 |
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Author |
Debora Gil; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Shape Restoration via a Regularized Curvature Flow |
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2004 |
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Journal of Mathematical Imaging and Vision |
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21 |
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3 |
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205-223 |
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Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes.
In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications. |
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IAM @ iam @ GiR2004c |
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1532 |
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Author |
Carme Julia; Felipe Lumbreras; Angel Sappa |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A Factorization-based Approach to Photometric Stereo |
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Journal Article |
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2011 |
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International Journal of Imaging Systems and Technology |
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IJIST |
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21 |
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1 |
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115-119 |
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This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011. |
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Admin @ si @ JLS2011; ADAS @ adas @ |
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1711 |
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Author |
Mohammad Rouhani; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
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Implicit Polynomial Representation through a Fast Fitting Error Estimation |
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Journal Article |
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2012 |
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IEEE Transactions on Image Processing |
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TIP |
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21 |
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4 |
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2089-2098 |
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Impact Factor
This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach. |
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Admin @ si @ RoS2012b; ADAS @ adas @ |
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1937 |
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J. Stöttinger; A. Hanbury; N. Sebe; Theo Gevers |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Spars Color Interest Points for Image Retrieval and Object Categorization |
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2012 |
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IEEE Transactions on Image Processing |
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TIP |
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21 |
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5 |
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2681-2692 |
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Impact factor 2010: 2.92
IF 2011/2012?: 3.32
Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest points are based on luminance, and color has been largely ignored. However, the use of color increases the distinctiveness of interest points. The use of color may therefore provide selective search reducing the total number of interest points used for image matching. This paper proposes color interest points for sparse image representation. To reduce the sensitivity to varying imaging conditions, light-invariant interest points are introduced. Color statistics based on occurrence probability lead to color boosted points, which are obtained through saliency-based feature selection. Furthermore, a principal component analysis-based scale selection method is proposed, which gives a robust scale estimation per interest point. From large-scale experiments, it is shown that the proposed color interest point detector has higher repeatability than a luminance-based one. Furthermore, in the context of image retrieval, a reduced and predictable number of color features show an increase in performance compared to state-of-the-art interest points. Finally, in the context of object recognition, for the Pascal VOC 2007 challenge, our method gives comparable performance to state-of-the-art methods using only a small fraction of the features, reducing the computing time considerably. |
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1057-7149 |
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ALTRES;ISE |
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Admin @ si @ SHS2012 |
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1847 |
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