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Author | Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci | ||||
Title | Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting | Type | Book Whole | ||
Year | 2016 | Publication | Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting | Abbreviated Journal | |
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Publisher | Elsevier | Place of Publication | Editor | ||
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ISSN | ISBN | 9780128110188 | Medium | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BZZ2016 | Serial | 2821 | ||
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Author | Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer | ||||
Title | Does Multimodality Help Human and Machine for Translation and Image Captioning? | Type | Conference Article | ||
Year | 2016 | Publication | 1st conference on machine translation | Abbreviated Journal | |
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Abstract | This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. | ||||
Address | Berlin; Germany; August 2016 | ||||
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Area | Expedition | Conference | WMT | ||
Notes | LAMP; 600.106 ; 600.068 | Approved | no | ||
Call Number | Admin @ si @ CAW2016 | Serial | 2761 | ||
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Author | Francesco Ciompi; Simone Balocco; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
Title | Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences | Type | Journal Article | ||
Year | 2016 | Publication | Medical Physics | Abbreviated Journal | MP |
Volume | 43 | Issue | 10 | Pages | |
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Abstract | Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identication of struts location and the denition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape. Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classication. The output of the classication 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents. Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts. Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions. |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ CBR2016 | Serial | 2819 | ||
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Author | Gerard Canal; Sergio Escalera; Cecilio Angulo | ||||
Title | A Real-time Human-Robot Interaction system based on gestures for assistive scenarios | Type | Journal Article | ||
Year | 2016 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 65-77 | |
Keywords | Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation | ||||
Abstract | Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEA2016 | Serial | 2768 | ||
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Author | Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu | ||||
Title | Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits | Type | Conference Article | ||
Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels | ||||
Abstract | We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. | ||||
Address | Amsterdam; The Netherlands; October 2016 | ||||
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Area | Expedition | Conference | ECCVW | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEG2016 | Serial | 2829 | ||
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Author | L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip | ||||
Title | Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation | Type | Journal Article | ||
Year | 2016 | Publication | Computers & Industrial Engineering | Abbreviated Journal | CIE |
Volume | 94 | Issue | Pages | 93-104 | |
Keywords | Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning | ||||
Abstract | In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one. | ||||
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Publisher | PERGAMON-ELSEVIER SCIENCE LTD | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | CIE | ||
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ISSN | 0360-8352 | ISBN | Medium | ||
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Notes | OR;MV; | Approved | no | ||
Call Number | Admin @ si @ CFG2016 | Serial | 2749 | ||
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Author | Esteve Cervantes; Long Long Yu; Andrew Bagdanov; Marc Masana; Joost Van de Weijer | ||||
Title | Hierarchical Part Detection with Deep Neural Networks | Type | Conference Article | ||
Year | 2016 | Publication | 23rd IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Object Recognition; Part Detection; Convolutional Neural Networks | ||||
Abstract | Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals. | ||||
Address | Phoenix; Arizona; USA; September 2016 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | LAMP; 600.106 | Approved | no | ||
Call Number | Admin @ si @ CLB2016 | Serial | 2762 | ||
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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 |
Keywords | 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 | Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa | ||||
Title | Fine-tuning based deep convolutional networks for lepidopterous genus recognition | Type | Conference Article | ||
Year | 2016 | Publication | 21st Ibero American Congress on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 467-475 | ||
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Abstract | This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. | ||||
Address | Lima; Perú; November 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | CIARP | ||
Notes | ADAS; 600.086 | Approved | no | ||
Call Number | Admin @ si @ CRS2016 | Serial | 2913 | ||
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Author | Francisco Cruz | ||||
Title | Probabilistic Graphical Models for Document Analysis | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models. In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Oriol Ramos Terrades | |
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ISSN | ISBN | 978-84-945373-2-5 | Medium | ||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ Cru2016 | Serial | 2861 | ||
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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 | Approved | no | ||
Call Number | Admin @ si @ DDB2016 | Serial | 2934 | ||
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Author | 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 | Type | 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 | Approved | no | ||
Call Number | Admin @ si @ DGC2016 | Serial | 3099 | ||
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Author | Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez | ||||
Title | A reduced feature set for driver head pose estimation | Type | Journal Article | ||
Year | 2016 | Publication | Applied Soft Computing | Abbreviated Journal | ASOC |
Volume | 45 | Issue | Pages | 98-107 | |
Keywords | Head pose estimation; driving performance evaluation; subspace based methods; linear regression | ||||
Abstract | Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. | ||||
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Notes | ADAS; 600.085; 600.076; | Approved | no | ||
Call Number | Admin @ si @ DHL2016 | Serial | 2760 | ||
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Author | Mariella Dimiccoli | ||||
Title | Figure-ground segregation: A fully nonlocal approach | Type | Journal Article | ||
Year | 2016 | Publication | Vision Research | Abbreviated Journal | VR |
Volume | 126 | Issue | Pages | 308-317 | |
Keywords | Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion | ||||
Abstract | We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas. | ||||
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ Dim2016b | Serial | 2623 | ||
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Author | Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan | ||||
Title | Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 27 | Issue | Pages | 511-527 | |
Keywords | particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging | ||||
Abstract | In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor quality data, particles and trajectories can be characterized by an a-contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that do not require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well established baseline show that the proposed approach outperforms the state of the art. |
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ DJM2016 | Serial | 2735 | ||
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