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Miguel Oliveira, Victor Santos, Angel Sappa, & P. Dias. (2015). Scene Representations for Autonomous Driving: an approach based on polygonal primitives. In 2nd Iberian Robotics Conference ROBOT2015 (Vol. 417, pp. 503–515).
Abstract: In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. 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. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Keywords: Scene reconstruction; Point cloud; Autonomous vehicles
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J.Poujol, Cristhian A. Aguilera-Carrasco, E.Danos, Boris X. Vintimilla, Ricardo Toledo, & Angel Sappa. (2015). Visible-Thermal Fusion based Monocular Visual Odometry. In 2nd Iberian Robotics Conference ROBOT2015 (Vol. 417, pp. 517–528). Springer International Publishing.
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Keywords: Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
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M. Cruz, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, Ricardo Toledo, & Angel Sappa. (2015). Cross-spectral image registration and fusion: an evaluation study. In 2nd International Conference on Machine Vision and Machine Learning.
Abstract: This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
Keywords: multispectral imaging; image registration; data fusion; infrared and visible spectra
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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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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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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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Carles Sanchez, Debora Gil, R. Tazi, Jorge Bernal, Y. Ruiz, L. Planas, et al. (2015). Quasi-real time digital assessment of Central Airway Obstruction. In 3rd European congress for bronchology and interventional pulmonology ECBIP2015.
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Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, & Yoshua Bengio. (2015). FitNets: Hints for Thin Deep Nets. In 3rd International Conference on Learning Representations ICLR2015.
Abstract: While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network.
Keywords: Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing
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Hanne Kause, Patricia Marquez, Andrea Fuster, Aura Hernandez-Sabate, Luc Florack, Debora Gil, et al. (2015). Quality Assessment of Optical Flow in Tagging MRI. In 5th Dutch Bio-Medical Engineering Conference BME2015.
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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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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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Suman Ghosh, Lluis Gomez, Dimosthenis Karatzas, & Ernest Valveny. (2015). Efficient indexing for Query By String text retrieval. In 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 (pp. 1236–1240).
Abstract: This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings.
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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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Carles Sanchez, Jorge Bernal, F. Javier Sanchez, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2015). Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy. In 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 (Vol. 10, pp. 935–945).
Abstract: PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.
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Bogdan Raducanu, Alireza Bosaghzadeh, & Fadi Dornaika. (2015). Multi-observation Face Recognition in Videos based on Label Propagation. In 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 (pp. 10–17).
Abstract: In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the
selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video
sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods.
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