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Andres Traumann, Sergio Escalera, & Gholamreza Anbarjafari. (2015). A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 75–79).
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Ramin Irani, Kamal Nasrollahi, Chris Bahnsen, D.H. Lundtoft, Thomas B. Moeslund, Marc O. Simon, et al. (2015). Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 88–95).
Abstract: Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions.
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Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera, Albert Clapes, Kamal Nasrollahi, Michael Holte, et al. (2015). Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning. In IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 22–29).
Abstract: The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario.
This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use
of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology.
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Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, et al. (2015). AutoML Challenge 2015: Design and First Results. In 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 (pp. 1–8).
Abstract: ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML.
Keywords: AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
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Victor Ponce, Hugo Jair Escalante, Sergio Escalera, & Xavier Baro. (2015). Gesture and Action Recognition by Evolved Dynamic Subgestures. In 26th British Machine Vision Conference (129.pp. 1–129.13).
Abstract: This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation
of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets.
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Huamin Ren, Weifeng Liu, Soren Ingvor Olsen, Sergio Escalera, & Thomas B. Moeslund. (2015). Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection. In 26th British Machine Vision Conference.
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Eduardo Tusa, Arash Akbarinia, Raquel Gil Rodriguez, & Corina Barbalata. (2015). Real-Time Face Detection and Tracking Utilising OpenMP and ROS. In 3rd Asia-Pacific Conference on Computer Aided System Engineering (pp. 179–184).
Abstract: The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in
low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction.
Keywords: RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP
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Arash Akbarinia, & C. Alejandro Parraga. (2015). Biologically Plausible Colour Naming Model. In European Conference on Visual Perception ECVP2015.
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Miguel Oliveira, L. Seabra Lopes, G. Hyun Lim, S. Hamidreza Kasaei, Angel Sappa, & A. Tom. (2015). Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains. In International Conference on Intelligent Robots and Systems (pp. 2488–2495).
Abstract: In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.
Keywords: Visual Learning; Computer Vision; Autonomous Agents
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Adria Ruiz, Joost Van de Weijer, & Xavier Binefa. (2015). From emotions to action units with hidden and semi-hidden-task learning. In 16th IEEE International Conference on Computer Vision (pp. 3703–3711).
Abstract: Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.
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Marta Nuñez-Garcia, Sonja Simpraga, M.Angeles Jurado, Maite Garolera, Roser Pueyo, & Laura Igual. (2015). FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization. In Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 (pp. 61–68).
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Chen Zhang, Maria del Mar Vila Muñoz, Petia Radeva, Roberto Elosua, Maria Grau, Angels Betriu, et al. (2015). Carotid Artery Segmentation in Ultrasound Images. In Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT2015), Joint MICCAI Workshops.
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Onur Ferhat, Arcadi Llanza, & Fernando Vilariño. (2015). Gaze interaction for multi-display systems using natural light eye-tracker. In 2nd International Workshop on Solutions for Automatic Gaze Data Analysis.
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Martha Mackay, Fernando Alonso, Pere Salamero, Xavier Baro, Jordi Gonzalez, & Sergio Escalera. (2015). Care and caring: future proofing the new demographics. In 6th International Carers Conference.
Abstract: With an ageing population, the issue of care provision is becoming increasingly important. The simple aspiration of the majority of older people is to live safely and well at home. Housing will be part of health & care integration in the following years and decades. A higher proportion of people will have to rely on informal care through family, friends, neighbors and others who
provide care to an older person in need of assistance (around 80% of care across the EU). They do not usually have a formal status and are usually unpaid. We need to ensure that all disabled or chronically ill people can get the help they need without overburdening their families.
The physical and emotional stress of carers is one of the dangers that this dependency can bring. To prevent carers burnout it is necessary to provide new solutions that are affordable and user friendly for the families and caregivers.
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J. Chazalon, Marçal Rusiñol, & Jean-Marc Ogier. (2015). Improving Document Matching Performance by Local Descriptor Filtering. In 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 (pp. 1216–1220).
Abstract: In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
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