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Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
Title | Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 146-156 | |
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Abstract | Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. | ||||
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ ADR2016b | Serial | 2742 | ||
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Author | Gerard Canal; Sergio Escalera; Cecilio Angulo | ||||
Title | A Real-time Human-Robot Interaction system based on gestures for assistive scenarios | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 65-77 | |
Keywords | Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation | ||||
Abstract | Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEA2016 | Serial | 2768 | ||
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Author | Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria | ||||
Title | Automatic garment retexturing based on infrared information | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Computers & Graphics | Abbreviated Journal | CG |
Volume | 59 | Issue | Pages | 28-38 | |
Keywords | Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading | ||||
Abstract | This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ ADT2016 | Serial | 2759 | ||
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Author | L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip | ||||
Title | Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Computers & Industrial Engineering | Abbreviated Journal | CIE |
Volume | 94 | Issue | Pages | 93-104 | |
Keywords | Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning | ||||
Abstract | In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one. | ||||
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Publisher | PERGAMON-ELSEVIER SCIENCE LTD | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | CIE | ||
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ISSN | 0360-8352 | ISBN | Medium | ||
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Notes | OR;MV; | Approved | no | ||
Call Number | Admin @ si @ CFG2016 | Serial | 2749 | ||
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Author | Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
Title | Generic Feature Learning for Wireless Capsule Endoscopy Analysis | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 79 | Issue | Pages | 163-172 | |
Keywords | Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis | ||||
Abstract | The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase). | ||||
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Notes | OR; MILAB;MV; | Approved | no | ||
Call Number | Admin @ si @ SDP2016 | Serial | 2836 | ||
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Author | Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci | ||||
Title | Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting | Type | Book Whole | ||
Year | 2016 | Publication ![]() |
Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting | Abbreviated Journal | |
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Publisher | Elsevier | Place of Publication | Editor | ||
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ISSN | ISBN | 9780128110188 | Medium | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BZZ2016 | Serial | 2821 | ||
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Author | Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
Title | The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
Digital Humanities Centres: Experiences and Perspectives | Abbreviated Journal | |
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Address | Warsaw; Poland; December 2016 | ||||
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Area | Expedition | Conference | DHLABS | ||
Notes | DAG; 600.097 | Approved | no | ||
Call Number | Admin @ si @ VFV2016 | Serial | 2908 | ||
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Author | Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez | ||||
Title | Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Endoscopy | Abbreviated Journal | END |
Volume | 48 | Issue | 9 | Pages | 837-842 |
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Abstract | Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence. Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively. Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice. |
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Notes | MV; | Approved | no | ||
Call Number | Admin @ si @FBL2016 | Serial | 2778 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | ||
Year | 2016 | Publication ![]() |
European Conference on Visual Perception | Abbreviated Journal | |
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Address | Barcelona; Spain; August 2016 | ||||
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Area | Expedition | Conference | ECVP | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ RaV2016b | Serial | 2895 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
European Conference on Visual Perception | Abbreviated Journal | |
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Address | Barcelona; Spain; August 2016 | ||||
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Area | Expedition | Conference | ECVP | ||
Notes | NEUROBIT | Approved | no | ||
Call Number | Admin @ si @ AkP2016b | Serial | 2900 | ||
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Author | Florin Popescu; Stephane Ayache; Sergio Escalera; Xavier Baro; Cecile Capponi; Patrick Panciatici; Isabelle Guyon | ||||
Title | From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
European Geosciences Union General Assembly | Abbreviated Journal | |
Volume | 18 | Issue | Pages | ||
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Abstract | The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image. We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks. |
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Address | Vienna; Austria; April 2016 | ||||
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Area | Expedition | Conference | EGU | ||
Notes | HuPBA;MV; | Approved | no | ||
Call Number | Admin @ si @ PAE2016 | Serial | 2772 | ||
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Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez | ||||
Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
GPU Technology Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Pedestrian Detection; GPU | ||||
Abstract | Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. | ||||
Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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Area | Expedition | Conference | GTC | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ CSM2016 | Serial | 2737 | ||
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Author | Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez | ||||
Title | Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
GPU Technology Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Stereo; Autonomous Driving; GPU; 3d reconstruction | ||||
Abstract | Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms | ||||
Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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Area | Expedition | Conference | GTC | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ HME2016 | Serial | 2738 | ||
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Author | Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay | ||||
Title | Care Respite: a remote monitoring eHealth system for improving ambient assisted living | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
Human Motion Analysis for Healthcare Applications | Abbreviated Journal | |
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Abstract | Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions. This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations. |
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Address | Savoy Place; London; uk; May 2016 | ||||
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Area | Expedition | Conference | HMAHA | ||
Notes | HuPBA; ISE; | Approved | no | ||
Call Number | Admin @ si @ EGB2016 | Serial | 2852 | ||
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Author | Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer | ||||
Title | Development of general‐purpose projection‐based augmented reality systems | Type | Journal | ||
Year | 2016 | Publication ![]() |
IADIs international journal on computer science and information systems | Abbreviated Journal | IADIs |
Volume | 11 | Issue | 2 | Pages | 1-18 |
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Abstract | Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups | ||||
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Notes | DAG; 600.084 | Approved | no | ||
Call Number | Admin @ si @ SCK2016 | Serial | 2890 | ||
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