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Jose Manuel Alvarez; Antonio Lopez |
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Title |
Road Detection Based on Illuminant Invariance |
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2011 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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12 |
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1 |
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184-193 |
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road detection |
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By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. |
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ADAS @ adas @ AlL2011 |
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1456 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Title |
Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
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2016 |
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IEEE Transactions on Affective Computing |
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TAC |
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9 |
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2 |
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161-175 |
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Mirroring; Nodding; Competence; Perception; Wearable Technology |
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Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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LAMP; 600.072; |
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Admin @ si @ MTR2016 |
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2826 |
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Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina |
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CYBERH: Cyber-Physical Systems in Health for Personalized Assistance |
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2017 |
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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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Assistance systems for e-Health applications have some specific requirements that demand of new methods for data gathering, analysis and modeling able to deal with SmallData:
1) systems should dynamically collect data from, both, the environment and the user to issue personalized recommendations; 2) data analysis should be able to tackle a limited number of samples prone to include non-informative data and possibly evolving in time due to changes in patient condition; 3) algorithms should run in real time with possibly limited computational resources and fluctuant internet access.
Electronic medical devices (and CyberPhysical devices in general) can enhance the process of data gathering and analysis in several ways: (i) acquiring simultaneously multiple sensors data instead of single magnitudes (ii) filtering data; (iii) providing real-time implementations condition by isolating tasks in individual processors of multiprocessors Systems-on-chip (MPSoC) platforms and (iv) combining information through sensor fusion
techniques.
Our approach focus on both aspects of the complementary role of CyberPhysical devices and analysis of SmallData in the process of personalized models building for e-Health applications. In particular, we will address the design of Cyber-Physical Systems in Health for Personalized Assistance (CyberHealth) in two specific application cases: 1) A Smart Assisted Driving System (SADs) for dynamical assessment of the driving capabilities of Mild Cognitive Impaired (MCI) people; 2) An Intelligent Operating Room (iOR) for improving the yield of bronchoscopic interventions for in-vivo lung cancer diagnosis. |
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Timisoara; Rumania; September 2017 |
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SYNASC |
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IAM; 600.085; 600.096; 600.075; 600.145 |
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Admin @ si @ GHC2017 |
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3045 |
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Xavier Soria; Angel Sappa; Arash Akbarinia |
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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2017 |
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7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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IPTA |
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NEUROBIT; MSIAU; 600.122 |
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Admin @ si @ SSA2017 |
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3074 |
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Author |
Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon |
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Title |
ChaLearn Looking at People: A Review of Events and Resources |
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2017 |
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30th International Joint Conference on Neural Networks |
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This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. |
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Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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HuPBA; 602.143 |
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Admin @ si @ EBE2017 |
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3012 |
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Author |
Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo |
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Title |
2D-to-3D Facial Expression Transfer |
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Conference Article |
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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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J.R. Serra; J.B. Subirana |
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Title |
Perceptual Grouping on Texture Images Using Non-Cartesian Networks |
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1996 |
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IEEE International Conference on Pattern Recognition. Vol B, pp. 462–466 |
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Admin @ si @ SeS1996a |
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217 |
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X. Binefa; Jordi Vitria |
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A contrast based focusing criterium. |
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1996 |
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IEEE International Conference on Pattern Recognition. Vol. A, pp. 805–809 |
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OR;MV |
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BCNPCL @ bcnpcl @ BiV1996 |
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79 |
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V. Valev; Petia Radeva |
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A Method of Solving Pattern or image Recognition Problems by Learning Boolean Formulas. |
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1992 |
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Proc. of 11th IAPR International Conference on Pattern Recognition |
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The Hague, Netherlands |
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IEEE Computer Society Press |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1992b |
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253 |
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Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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All the people around me: face clustering in egocentric photo streams |
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2017 |
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24th International Conference on Image Processing |
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face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams |
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arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose. |
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Beijing; China; September 2017 |
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ICIP |
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MILAB; no menciona |
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Admin @ si @ EDR2017 |
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3025 |
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Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai |
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WordFences: Text Localization and Recognition |
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2017 |
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24th International Conference on Image Processing |
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Beijing; China; September 2017 |
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HUPBA; no menciona |
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Admin @ si @ PAE2017 |
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3007 |
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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences |
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2003 |
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IEEE International Conference on Image Processing, Barcelona, Spain, September 2003 |
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325-328 |
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Barcelona |
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ADAS @ adas @ SAM2003 |
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418 |
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Debora Gil; Petia Radeva; Fernando Vilariño |
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Anisotropic Contour Completion |
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2003 |
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Proceedings of the IEEE International Conference on Image Processing |
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I-869 |
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In this paper we introduce a novel application of the diffusion tensor for anisotropic image processing. The Anisotropic Contour Completion (ACC) we suggest consists in extending the characteristic function of the open curve by means of a degenerated diffusion tensor that prevents any diffusion in the normal direction. We show that ACC is equivalent to a dilation with a continuous elliptic structural element that takes into account the local orientation of the contours to be closed. Experiments on contours extracted from real images show that ACC produces shapes able to adapt to any curve in an active contour framework. 1. |
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Barcelona, Spain |
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Barcelona, Spain |
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0-7803-7751-6 |
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IAM;MV;MILAB;SIAI |
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IAM @ iam @ GRV2003 |
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1539 |
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David Guillamet; B. Moghaddam; Jordi Vitria |
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Higher-Order Dependencies in Local Appearance Models |
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2003 |
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IEEE International Conference on Image Processing (ICIP) |
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Barcelona, Spain |
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OR;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ GMV2003b |
Serial |
377 |
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Author |
Xose M. Pardo; Petia Radeva; Juan J. Villanueva |
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Title |
Self-Training Statistic Snake for Image Segmentation and Tracking. |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings 10th International Conference on Image Analysis and Processing |
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Pages |
406-411 |
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Abstract |
. |
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Address |
Venice |
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ICIAP |
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Notes |
MILAB |
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no |
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Call Number |
BCNPCL @ bcnpcl @ PRV1999 |
Serial |
26 |
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