| Records |
| Author |
Onur Ferhat; Fernando Vilariño |
| Title |
Low Cost Eye Tracking: The Current Panorama |
Type |
Journal Article |
| Year |
2016 |
Publication |
Computational Intelligence and Neuroscience |
Abbreviated Journal |
CIN |
| Volume |
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Issue |
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Pages  |
Article ID 8680541 |
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| Abstract |
Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools. |
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| Notes |
MV; 605.103; 600.047; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ FeV2016 |
Serial |
2744 |
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| Author |
C. Alejandro Parraga; Arash Akbarinia |
| Title |
NICE: A Computational Solution to Close the Gap from Colour Perception to Colour Categorization |
Type |
Journal Article |
| Year |
2016 |
Publication |
PLoS One |
Abbreviated Journal |
Plos |
| Volume |
11 |
Issue |
3 |
Pages |
e0149538 |
| Keywords |
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| Abstract |
The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms. |
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| Notes |
NEUROBIT; 600.068 |
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no |
| Call Number |
Admin @ si @ PaA2016a |
Serial |
2747 |
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| Author |
Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
| Title |
Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
Type |
Journal Article |
| Year |
2016 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
75 |
Issue |
22 |
Pages |
14985-14990 |
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| Notes |
ISE; HUPBA |
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no |
| Call Number |
Admin @ si @ DDB2016 |
Serial |
2934 |
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| Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
| Title |
Hierarchical online domain adaptation of deformable part-based models |
Type |
Conference Article |
| Year |
2016 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5536-5541 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
| Address |
Stockholm; Sweden; May 2016 |
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Conference |
ICRA |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ XVM2016 |
Serial |
2728 |
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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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Springer US |
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Edition |
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| ISSN |
1380-7501 |
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Medium |
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Expedition |
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Conference |
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| Notes |
LAMP; 601.240; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ KBL2016 |
Serial |
2520 |
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| Author |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
| Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
Type |
Conference Article |
| Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3234-3243 |
| Keywords |
Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
| Address |
Las Vegas; USA; June 2016 |
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Conference |
CVPR |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSM2016 |
Serial |
2739 |
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| Author |
Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
| Title |
GPU-based pedestrian detection for autonomous driving |
Type |
Conference Article |
| Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
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| Volume |
80 |
Issue |
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Pages |
2377-2381 |
| Keywords |
Pedestrian detection; Autonomous Driving; CUDA |
| Abstract |
We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. |
| Address |
San Diego; CA; USA; June 2016 |
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ICCS |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ CSE2016 |
Serial |
2741 |
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| Author |
Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah |
| Title |
Human Pose Estimation from Monocular Images: A Comprehensive Survey |
Type |
Journal Article |
| Year |
2016 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
16 |
Issue |
12 |
Pages |
1966 |
| Keywords |
human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods |
| Abstract |
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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| Notes |
ISE; 600.098; 600.119 |
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no |
| Call Number |
Admin @ si @ GZG2016 |
Serial |
2933 |
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| Author |
Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
| Title |
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
28 |
Issue |
8 |
Pages |
1548-1568 |
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Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
| Abstract |
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
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| Notes |
HuPBA;MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ COC2016 |
Serial |
2718 |
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| Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton |
| Title |
Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
28 |
Issue |
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Pages |
1489 - 1491 |
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The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others. |
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| Notes |
HuPBA; ISE;MV; |
Approved |
no |
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Admin @ si @ |
Serial |
2851 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
| Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
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This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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0196-2892 |
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| Notes |
LAMP; 600.079;MILAB |
Approved |
no |
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Admin @ si @ RGC2016 |
Serial |
2723 |
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Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
| Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
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Journal Article |
| Year |
2016 |
Publication |
Chest Journal |
Abbreviated Journal |
CHEST |
| Volume |
150 |
Issue |
4 |
Pages |
1003A |
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| Notes |
IAM; 600.096; 600.075 |
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no |
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Admin @ si @ DGC2016 |
Serial |
3099 |
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| Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
| Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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9915 |
Issue |
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Pages |
894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
| Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Amsterdam; The Netherlands; October 2016 |
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LNCS |
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ECCVW |
| Notes |
ADAS;IAM; 600.085; 600.076 |
Approved |
no |
| Call Number |
MHE2016 |
Serial |
2865 |
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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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LAMP; 600.072; 600.068; |
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| Call Number |
Admin @ si @ TRM2016 |
Serial |
2721 |
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| Author |
Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez |
| Title |
Comparison of two non-linear model-based control strategies for autonomous vehicles |
Type |
Conference Article |
| Year |
2016 |
Publication |
24th Mediterranean Conference on Control and Automation |
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Pages |
846-851 |
| Keywords |
Autonomous Driving; Control |
| Abstract |
This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation. |
| Address |
Athens; Greece; June 2016 |
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MED |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
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no |
| Call Number |
ADAS @ adas @ ASP2016 |
Serial |
2750 |
| Permanent link to this record |
