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Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MDB2021 |
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3491 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features |
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Conference Article |
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2020 |
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IEEE Winter Conference on Applications of Computer Vision |
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Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. |
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Aspen; Colorado; USA; March 2020 |
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WACV |
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DAG; 600.121; 600.129 |
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Admin @ si @ MDB2020 |
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3334 |
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Author |
Andres Traumann; Gholamreza Anbarjafari; Sergio Escalera |
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Title |
Accurate 3D Measurement Using Optical Depth Information |
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Journal Article |
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2015 |
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Electronic Letters |
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EL |
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51 |
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18 |
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1420-1422 |
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A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II. |
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HuPBA;MILAB |
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no |
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Admin @ si @ TAE2015 |
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2647 |
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Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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75-79 |
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Boston; EEUU; June 2015 |
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HuPBA;MILAB |
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no |
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Admin @ si @ TEA2015 |
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2653 |
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Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez |
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Obstacle mapping module for quadrotors on outdoor Search and Rescue operations |
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2013 |
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International Micro Air Vehicle Conference and Flight Competition |
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UAV |
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Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments. |
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Toulouse; France; September 2013 |
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IMAV |
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ADAS; 600.054; 600.057;IAM |
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no |
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Admin @ si @ NSH2013 |
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2371 |
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Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim |
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Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
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Conference Article |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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390-398 |
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Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers |
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This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. |
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New Orleans; USA; June 2022 |
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CVPRW |
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MSIAU; no menciona |
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no |
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Call Number |
Admin @ si @ RMV2022 |
Serial |
3774 |
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Author |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez |
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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 |
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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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Admin @ si @ MSM2020 |
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3452 |
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Author |
Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez |
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Title |
Robust Detection of Outdoor Urban Advertising Panels in Static Images |
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Conference Article |
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2019 |
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18th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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246-256 |
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Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality |
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One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. |
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Aquila; Italia; June 2019 |
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PAAMS |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ MSS2019 |
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3270 |
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Author |
Angel Sappa |
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Title |
Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation |
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2006 |
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IEEE Transactions on Image Processing, 15(2):377–384 |
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ADAS |
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ADAS @ adas @ Sap2006a |
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637 |
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Angel Sappa |
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Splitting up Panoramic Range Images into Compact 2½D Representations |
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2006 |
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International Journal of Imaging Systems and Technology, 16(3): 85–91 |
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ADAS @ adas @ Sap2006b |
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721 |
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Angel Sappa |
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Efficient Closed Contour Extraction from Range Image Edge Points |
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2005 |
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IEEE International Conference on Robotics and Automation |
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Barcelona (Spain) |
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ADAS @ adas @ Sap2005 |
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538 |
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Angel Sappa |
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Automatic Extraction of Planar Projections from Panoramic Range Images |
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2004 |
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IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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ADAS @ adas @ Sap2004a |
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454 |
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Angel Sappa |
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Surface Model Generation from Range Images of Industrial Environments |
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2004 |
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IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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ADAS @ adas @ Sap2004b |
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455 |
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Author |
Angel Sappa (ed) |
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Title |
ICT Applications for Smart Cities |
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2022 |
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ICT Applications for Smart Cities |
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224 |
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Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing |
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Part of the book series: Intelligent Systems Reference Library (ISRL)
This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:
• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes
The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field. |
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September 2022 |
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Springer |
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Angel Sappa |
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ISRL |
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978-3-031-06306-0 |
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MSIAU; MACO |
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Admin @ si @ Sap2022 |
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3812 |
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Author |
Angel Sappa (ed) |
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Title |
Computer Graphics and Imaging |
Type |
Book Whole |
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Year |
2010 |
Publication |
Computer Graphics and Imaging |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
Angel Sappa |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978–0–88986–836–6 |
Medium |
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Area |
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Expedition |
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Conference |
CGIM |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ Sap2010 |
Serial |
1468 |
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