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| Author | Mariella Dimiccoli | ||||
| Title | Figure-ground segregation: A fully nonlocal approach | Type | Journal Article | ||
| Year | 2016 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 126 | Issue | Pages | 308-317 | |
| Keywords | Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion | ||||
| Abstract | We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas. | ||||
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ Dim2016b | Serial | 2623 | ||
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| Author | Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo | ||||
| Title | Monocular visual odometry: A cross-spectral image fusion based approach | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 85 | Issue | Pages | 26-36 | |
| Keywords | Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion | ||||
| Abstract | This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Notes | ADAS;600.086; 600.076 | Approved | no | ||
| Call Number | Admin @ si @SAC2016 | Serial | 2811 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
| Title | Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 83 | Issue | Pages | 312-325 | |
| Keywords | Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives | ||||
| Abstract | When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Notes | ADAS; 600.086, 600.076 | Approved | no | ||
| Call Number | Admin @ si @OSS2016a | Serial | 2806 | ||
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| Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
| Title | Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices | Type | Journal Article | ||
| Year | 2016 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 175 | Issue | B | Pages | 866–876 |
| Keywords | Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices | ||||
| Abstract | During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. | ||||
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| Notes | LAMP; 600.072; 600.068; | Approved | no | ||
| Call Number | Admin @ si @ TRM2016 | Serial | 2721 | ||
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| Author | Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Robust non-blind color video watermarking using QR decomposition and entropy analysis | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 38 | Issue | Pages | 838-847 | |
| Keywords | Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition | ||||
| Abstract | Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. | ||||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @RSA2016 | Serial | 2766 | ||
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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 | 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 | Sumit K. Banchhor; Tadashi Araki; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Ayman El-Baz; Luca Saba; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri | ||||
| Title | Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach | Type | Journal Article | ||
| Year | 2016 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
| Volume | 134 | Issue | Pages | 237-258 | |
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| Abstract | BACKGROUND AND OBJECTIVE:
Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames. METHODS: This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio. RESULTS: Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings. CONCLUSIONS: We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance. |
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ BAL2016 | Serial | 2830 | ||
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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 o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent 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 di ers 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 | 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 | Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez | ||||
| Title | A reduced feature set for driver head pose estimation | Type | Journal Article | ||
| Year | 2016 | Publication | Applied Soft Computing | Abbreviated Journal | ASOC |
| Volume | 45 | Issue | Pages | 98-107 | |
| Keywords | Head pose estimation; driving performance evaluation; subspace based methods; linear regression | ||||
| Abstract | Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. | ||||
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| Notes | ADAS; 600.085; 600.076; | Approved | no | ||
| Call Number | Admin @ si @ DHL2016 | Serial | 2760 | ||
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| Author | Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | Multi-modal RGB-Depth-Thermal Human Body Segmentation | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 118 | Issue | 2 | Pages | 217-239 |
| Keywords | Human body segmentation; RGB ; Depth Thermal | ||||
| Abstract | This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ PCB2016 | Serial | 2767 | ||
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| Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
| Title | Hierarchical Adaptive Structural SVM for Domain Adaptation | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 119 | Issue | 2 | Pages | 159-178 |
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
| Abstract | A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ XRV2016 | Serial | 2669 | ||
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| Author | Antonio Hernandez; Sergio Escalera; Stan Sclaroff | ||||
| Title | Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 118 | Issue | 1 | Pages | 49–64 |
| Keywords | Contextual rescoring; Poselets; Human pose estimation | ||||
| Abstract | In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ HES2016 | Serial | 2719 | ||
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| Author | Svebor Karaman; Andrew Bagdanov; Lea Landucci; Gianpaolo D'Amico; Andrea Ferracani; Daniele Pezzatini; Alberto del Bimbo | ||||
| Title | Personalized multimedia content delivery on an interactive table by passive observation of museum visitors | Type | Journal Article | ||
| Year | 2016 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 75 | Issue | 7 | Pages | 3787-3811 |
| Keywords | Computer vision; Video surveillance; Cultural heritage; Multimedia museum; Personalization; Natural interaction; Passive profiling | ||||
| Abstract | The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello). | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 601.240; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ KBL2016 | Serial | 2520 | ||
| Permanent link to this record | |||||