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Marc Bolaños, R. Mestre, Estefania Talavera, Xavier Giro, & Petia Radeva. (2015). Visual Summary of Egocentric Photostreams by Representative Keyframes. In IEEE International Conference on Multimedia and Expo ICMEW2015 (pp. 1–6).
Abstract: Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries.
Keywords: egocentric; lifelogging; summarization; keyframes
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Nuria Cirera, Alicia Fornes, & Josep Llados. (2015). Hidden Markov model topology optimization for handwriting recognition. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 626–630).
Abstract: In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task.
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Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey, Sayan Chakraborty, Luca Saba, Dinesh Kumar, et al. (2015). A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. CMPB - Computer Methods and Programs in Biomedicine, 118(2), 158–172.
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Juan Ignacio Toledo, Jordi Cucurull, Jordi Puiggali, Alicia Fornes, & Josep Llados. (2015). Document Analysis Techniques for Automatic Electoral Document Processing: A Survey. In E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 (pp. 139–141). LNCS.
Abstract: In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents.
Keywords: Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally
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Pau Riba, Josep Llados, & Alicia Fornes. (2015). Handwritten Word Spotting by Inexact Matching of Grapheme Graphs. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 781–785).
Abstract: This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections.
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Victor Campmany, Sergio Silva, Juan Carlos Moure, Antoni Espinosa, David Vazquez, & Antonio Lopez. (2015). GPU-based pedestrian detection for autonomous driving. In Programming and Tunning Massive Parallel Systems. PUMPS.
Abstract: Pedestrian detection for autonomous driving has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. The real-time constraints in the field are tight, and regular processors are not able to handle the workload obtaining an acceptable ratio of frames per second (fps). Moreover, multiple cameras are required to obtain accurate results, so the need to speed up the process is even higher. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system. Further, we introduce significant algorithmic adjustments and optimizations to adapt the problem to the GPU architecture. The aim is to provide a system capable of running in real-time obtaining reliable results.
Keywords: Autonomous Driving; ADAS; CUDA; Pedestrian Detection
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Sergio Silva, Victor Campmany, Laura Sellart, Juan Carlos Moure, Antoni Espinosa, David Vazquez, et al. (2015). Autonomous GPU-based Driving. In Programming and Tunning Massive Parallel Systems.
Abstract: Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.
Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.
NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.
Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU.
Keywords: Autonomous Driving; ADAS; CUDA
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Onur Ferhat, Arcadi Llanza, & Fernando Vilariño. (2015). A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios. In Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 (Vol. 9117, pp. 569–576). LNCS. Springer International Publishing.
Abstract: We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system.
Keywords: Eye tracking; Gaze estimation; Natural light; Webcam
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Andres Traumann, Gholamreza Anbarjafari, & Sergio Escalera. (2015). Accurate 3D Measurement Using Optical Depth Information. EL - Electronic Letters, 51(18), 1420–1422.
Abstract: A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II.
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Kamal Nasrollahi, Sergio Escalera, P. Rasti, Gholamreza Anbarjafari, Xavier Baro, Hugo Jair Escalante, et al. (2015). Deep Learning based Super-Resolution for Improved Action Recognition. In 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 (pp. 67–72).
Abstract: Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.
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Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, & Sergio Escalera. (2015). The AutoML challenge on codalab. In IEEE International Joint Conference on Neural Networks IJCNN2015.
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Gerard Canal, Cecilio Angulo, & Sergio Escalera. (2015). Gesture based Human Multi-Robot interaction. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static
gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case.
A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a
natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates.
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Xavier Baro, Jordi Gonzalez, Junior Fabian, Miguel Angel Bautista, Marc Oliu, Hugo Jair Escalante, et al. (2015). ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 1–9).
Abstract: Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
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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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