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| Author | Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund | ||||
| Title | Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields | Type | Journal Article | ||
| Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 80 | Issue | Pages | 208–215 | |
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| Abstract | This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset. |
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| Notes | HuPBA; ISE;MILAB; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ TEG2016 | Serial | 2843 | ||
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| Author | Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla | ||||
| Title | Editorial: Special Issue on Computational Intelligence for Vision and Robotics | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 853–854 |
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| Notes | HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ GGE2017 | Serial | 2845 | ||
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| Author | Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
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| Address | Palma de Mallorca; Spain; July 2016 | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RUE2016 | Serial | 2846 | ||
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| Author | Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | Spatiotemporal Facial Super-Pixels for Pain Detection | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Facial images; Super-pixels; Spatiotemporal filters; Pain detection | ||||
| Abstract | Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
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| Address | Palma de Mallorca; Spain; July 2016 | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LNM2016 | Serial | 2847 | ||
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| Author | Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | RGB-D Segmentation of Poultry Entrails | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
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| Abstract | Best commercial paper award. | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PJM2016 | Serial | 2848 | ||
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| Author | Sergio Escalera; Mercedes Torres-Torres; Brais Martinez; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Georgios Tzimiropoulos; Ciprian Corneanu; Marc Oliu Simón; Mohammad Ali Bagheri; Michel Valstar | ||||
| Title | ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 | Type | Conference Article | ||
| Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org. | ||||
| Address | Las Vegas; USA; June 2016 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MV; | Approved | no | ||
| Call Number | ETM2016 | Serial | 2849 | ||
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| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Stable Airway Center Tracking for Bronchoscopic Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 28th Conference of the international Society for Medical Innovation and Technology | Abbreviated Journal | |
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| Abstract | Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers from intra‐operative videos based on tracking of maximal stable regions of energy maps. |
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| Address | Delft; Rotterdam; Leiden; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | SMIT | ||
| Notes | IAM; | Approved | no | ||
| Call Number | Admin @ si @ LSB2016a | Serial | 2856 | ||
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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 | Pages | 1489 - 1491 | |
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| Abstract | 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 | ||
| Call Number | Admin @ si @ | Serial | 2851 | ||
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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 | Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera | ||||
| Title | Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal | Type | Conference Article | ||
| Year | 2016 | Publication | 4th Congreso WCPT-SAR | Abbreviated Journal | |
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| Address | Buenos Aires; Argentina; June 2016 | ||||
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| Area | Expedition | Conference | WCPT-SAR | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RRR2016 | Serial | 2853 | ||
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| Author | Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans | ||||
| Title | Improved RGB-D-T based Face Recognition | Type | Journal Article | ||
| Year | 2016 | Publication | IET Biometrics | Abbreviated Journal | BIO |
| Volume | 5 | Issue | 4 | Pages | 297 - 303 |
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| Abstract | Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. | ||||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ OCN2016 | Serial | 2854 | ||
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| Author | Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima | ||||
| Title | CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions | Type | Conference Article | ||
| Year | 2016 | Publication | 16th International Conference for Integrated Care | Abbreviated Journal | |
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| Address | Barcelona; Spain; May 2016 | ||||
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| Area | Expedition | Conference | ICIC | ||
| Notes | HuPBA; ISE;MV | Approved | no | ||
| Call Number | Admin @ si @ ABE2016 | Serial | 2855 | ||
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| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Stable Anatomical Structure Tracking for video-bronchoscopy Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
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| Keywords | Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking | ||||
| Abstract | Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at di erent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. | ||||
| Address | Athens; Greece; October 2016 | ||||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ LSB2016b | Serial | 2857 | ||
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| Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
| Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Vision | Abbreviated Journal | JV |
| Volume | 17 | Issue | 10 | Pages | 390-390 |
| Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
| Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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| Address | Florida, USA; May 2017 | ||||
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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| Author | German Ros | ||||
| Title | Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What | Type | Book Whole | ||
| Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa;Julio Guerrero;Antonio Lopez | |
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| ISSN | ISBN | 978-84-945373-1-8 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Ros2016 | Serial | 2860 | ||
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