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Author | Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
Title | Growing Algorithm for Intersection Detection (GRAID) in branching patterns | Type | Journal Article | ||
Year | 2015 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 26 | Issue | 2 | Pages | 387-400 |
Keywords | Bifurcation ; Crossroad; Intersection ;Retina ; Vessel | ||||
Abstract | Analysis of branching structures represents a very important task in fields such as medical diagnosis, road detection or biometrics. Detecting intersection landmarks Becomes crucial when capturing the structure of a branching pattern. We present a very simple geometrical model to describe intersections in branching structures based on two conditions: Bounded Tangency condition (BT) and Shortest Branch (SB) condition. The proposed model precisely sets a geometrical characterization of intersections and allows us to introduce a new unsupervised operator for intersection extraction. We propose an implementation that handles the consequences of digital domain operation that,unlike existing approaches, is not restricted to a particular scale and does not require the computation of the thinned pattern. The new proposal, as well as other existing approaches in the bibliography, are evaluated in a common framework for the first time. The performance analysis is based on two manually segmented image data sets: DRIVE retinal image database and COLON-VESSEL data set, a newly created data set of vascular content in colonoscopy frames. We have created an intersection landmark ground truth for each data set besides comparing our method in the only existing ground truth. Quantitative results confirm that we are able to outperform state-of-the-art performancelevels with the advantage that neither training nor parameter tuning is needed. | ||||
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Notes | ;SIAI | Approved | no | ||
Call Number | Admin @ si @MBS2015 | Serial | 2777 | ||
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Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez | ||||
Title | GPU-based pedestrian detection for autonomous driving | Type | Abstract | ||
Year | 2015 | Publication | Programming and Tunning Massive Parallel Systems | Abbreviated Journal | PUMPS |
Volume | Issue | Pages | |||
Keywords | Autonomous Driving; ADAS; CUDA; Pedestrian Detection | ||||
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. | ||||
Address | Barcelona; Spain | ||||
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Series Editor | Series Title | Abbreviated Series Title | PUMPS | ||
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Area | Expedition | Conference | PUMPS | ||
Notes | ADAS; 600.076; 600.082; 600.085 | Approved | no | ||
Call Number | ADAS @ adas @ CSM2015 | Serial | 2644 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 24 | Issue | 12 | Pages | 5842-5853 |
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Abstract | The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | LAMP; 600.079;ISE | Approved | no | ||
Call Number | Admin @ si @ MoW2015b | Serial | 2689 | ||
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Author | Gerard Canal; Cecilio Angulo; Sergio Escalera | ||||
Title | Gesture based Human Multi-Robot interaction | Type | Conference Article | ||
Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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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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Address | Killarney; Ireland; July 2015 | ||||
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Area | Expedition | Conference | IJCNN | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | CAE2015a | Serial | 2651 | ||
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Author | Victor Ponce; Hugo Jair Escalante; Sergio Escalera; Xavier Baro | ||||
Title | Gesture and Action Recognition by Evolved Dynamic Subgestures | Type | Conference Article | ||
Year | 2015 | Publication | 26th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | 129.1-129.13 | ||
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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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Address | Swansea; uk; September 2015 | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ PEE2015 | Serial | 2657 | ||
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Author | Eloi Puertas; Sergio Escalera; Oriol Pujol | ||||
Title | Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 18 | Issue | 2 | Pages | 247-261 |
Keywords | Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification | ||||
Abstract | In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. | ||||
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Publisher | Springer-Verlag | Place of Publication | Editor | ||
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ISSN | 1433-7541 | ISBN | Medium | ||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ PEP2013 | Serial | 2251 | ||
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Author | Onur Ferhat; Arcadi Llanza; Fernando Vilariño | ||||
Title | Gaze interaction for multi-display systems using natural light eye-tracker | Type | Conference Article | ||
Year | 2015 | Publication | 2nd International Workshop on Solutions for Automatic Gaze Data Analysis | Abbreviated Journal | |
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Address | Bielefeld; Germany; September 2015 | ||||
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Area | Expedition | Conference | SAGA | ||
Notes | MV;SIAI | Approved | no | ||
Call Number | Admin @ si @ FLV2015b | Serial | 2676 | ||
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Author | Antonio Hernandez | ||||
Title | From pixels to gestures: learning visual representations for human analysis in color and depth data sequences | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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Abstract | The visual analysis of humans from images is an important topic of interest due to its relevance to many computer vision applications like pedestrian detection, monitoring and surveillance, human-computer interaction, e-health or content-based image retrieval, among others.
In this dissertation we are interested in learning different visual representations of the human body that are helpful for the visual analysis of humans in images and video sequences. To that end, we analyze both RGB and depth image modalities and address the problem from three different research lines, at different levels of abstraction; from pixels to gestures: human segmentation, human pose estimation and gesture recognition. First, we show how binary segmentation (object vs. background) of the human body in image sequences is helpful to remove all the background clutter present in the scene. The presented method, based on Graph cuts optimization, enforces spatio-temporal consistency of the produced segmentation masks among consecutive frames. Secondly, we present a framework for multi-label segmentation for obtaining much more detailed segmentation masks: instead of just obtaining a binary representation separating the human body from the background, finer segmentation masks can be obtained separating the different body parts. At a higher level of abstraction, we aim for a simpler yet descriptive representation of the human body. Human pose estimation methods usually rely on skeletal models of the human body, formed by segments (or rectangles) that represent the body limbs, appropriately connected following the kinematic constraints of the human body. In practice, such skeletal models must fulfill some constraints in order to allow for efficient inference, while actually limiting the expressiveness of the model. In order to cope with this, we introduce a top-down approach for predicting the position of the body parts in the model, using a mid-level part representation based on Poselets. Finally, we propose a framework for gesture recognition based on the bag of visual words framework. We leverage the benefits of RGB and depth image modalities by combining modality-specific visual vocabularies in a late fusion fashion. A new rotation-variant depth descriptor is presented, yielding better results than other state-of-the-art descriptors. Moreover, spatio-temporal pyramids are used to encode rough spatial and temporal structure. In addition, we present a probabilistic reformulation of Dynamic Time Warping for gesture segmentation in video sequences. A Gaussian-based probabilistic model of a gesture is learnt, implicitly encoding possible deformations in both spatial and time domains. |
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Address | January 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Stan Sclaroff | |
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ISSN | ISBN | 978-84-940902-0-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ Her2015 | Serial | 2576 | ||
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Author | Adria Ruiz; Joost Van de Weijer; Xavier Binefa | ||||
Title | From emotions to action units with hidden and semi-hidden-task learning | Type | Conference Article | ||
Year | 2015 | Publication | 16th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 3703-3711 | ||
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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. | ||||
Address | Santiago de Chile; Chile; December 2015 | ||||
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Area | Expedition | Conference | ICCV | ||
Notes | LAMP; 600.068; 600.079 | Approved | no | ||
Call Number | Admin @ si @ RWB2015 | Serial | 2671 | ||
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Author | Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters | ||||
Title | Fractional Programming Weighted Decoding for Error-Correcting Output Codes | Type | Conference Article | ||
Year | 2015 | Publication | Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 38-50 | ||
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Abstract | In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments. | ||||
Address | Gunzburg; Germany; June 2015 | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | ||
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ISSN | ISBN | 978-3-319-20247-1 | Medium | ||
Area | Expedition | Conference | MCS | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ ISS2015 | Serial | 2601 | ||
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Author | Hongxing Gao | ||||
Title | Focused Structural Document Image Retrieval in Digital Mailroom Applications | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | In this work, we develop a generic framework that is able to handle the document retrieval problem in various scenarios such as searching for full page matches or retrieving the counterparts for specific document areas, focusing on their structural similarity or letting their visual resemblance to play a dominant role. Based on the spatial indexing technique, we propose to search for matches of local key-region pairs carrying both structural and visual information from the collection while a scheme allowing to adjust the relative contribution of structural and visual similarity is presented.
Based on the fact that the structure of documents is tightly linked with the distance among their elements, we firstly introduce an efficient detector named Distance Transform based Maximally Stable Extremal Regions (DTMSER). We illustrate that this detector is able to efficiently extract the structure of a document image as a dendrogram (hierarchical tree) of multi-scale key-regions that roughly correspond to letters, words and paragraphs. We demonstrate that, without benefiting from the structure information, the key-regions extracted by the DTMSER algorithm achieve better results comparing with state-of-the-art methods while much less amount of key-regions are employed. We subsequently propose a pair-wise Bag of Words (BoW) framework to efficiently embed the explicit structure extracted by the DTMSER algorithm. We represent each document as a list of key-region pairs that correspond to the edges in the dendrogram where inclusion relationship is encoded. By employing those structural key-region pairs as the pooling elements for generating the histogram of features, the proposed method is able to encode the explicit inclusion relations into a BoW representation. The experimental results illustrate that the pair-wise BoW, powered by the embedded structural information, achieves remarkable improvement over the conventional BoW and spatial pyramidal BoW methods. To handle various retrieval scenarios in one framework, we propose to directly query a series of key-region pairs, carrying both structure and visual information, from the collection. We introduce the spatial indexing techniques to the document retrieval community to speed up the structural relationship computation for key-region pairs. We firstly test the proposed framework in a full page retrieval scenario where structurally similar matches are expected. In this case, the pair-wise querying method achieves notable improvement over the BoW and spatial pyramidal BoW frameworks. Furthermore, we illustrate that the proposed method is also able to handle focused retrieval situations where the queries are defined as a specific interesting partial areas of the images. We examine our method on two types of focused queries: structure-focused and exact queries. The experimental results show that, the proposed generic framework obtains nearly perfect precision on both types of focused queries while it is the first framework able to tackle structure-focused queries, setting a new state of the art in the field. Besides, we introduce a line verification method to check the spatial consistency among the matched key-region pairs. We propose a computationally efficient version of line verification through a two step implementation. We first compute tentative localizations of the query and subsequently employ them to divide the matched key-region pairs into several groups, then line verification is performed within each group while more precise bounding boxes are computed. We demonstrate that, comparing with the standard approach (based on RANSAC), the line verification proposed generally achieves much higher recall with slight loss on precision on specific queries. |
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Address | January 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Dimosthenis Karatzas;Marçal Rusiñol | |
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ISSN | ISBN | 978-84-943427-0-7 | Medium | ||
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Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ Gao2015 | Serial | 2577 | ||
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Author | Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio | ||||
Title | FitNets: Hints for Thin Deep Nets | Type | Conference Article | ||
Year | 2015 | Publication | 3rd International Conference on Learning Representations ICLR2015 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing | ||||
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. | ||||
Address | San Diego; CA; May 2015 | ||||
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Area | Expedition | Conference | ICLR | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ RBK2015 | Serial | 2593 | ||
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Author | Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual | ||||
Title | FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization | Type | Conference Article | ||
Year | 2015 | Publication | Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 61-68 | ||
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Address | Munich; Germany; October 2015 | ||||
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Area | Expedition | Conference | MLMI | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ NSJ2015 | Serial | 2674 | ||
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Author | Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca | ||||
Title | Factorized appearances for object detection | Type | Journal Article | ||
Year | 2015 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 138 | Issue | Pages | 92–101 | |
Keywords | Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts | ||||
Abstract | Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure. Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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Notes | ISE; 600.063; 600.078 | Approved | no | ||
Call Number | Admin @ si @ GPG2015 | Serial | 2705 | ||
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Author | Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh | ||||
Title | Facial expression recognition based on multi observations with application to social robotics | Type | Book Chapter | ||
Year | 2015 | Publication | Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance | Abbreviated Journal | |
Volume | Issue | Pages | 153-166 | ||
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Abstract | Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression. |
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Publisher | Nova Science publishers | Place of Publication | Editor | Bruce Flores | |
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Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ DRB2015 | Serial | 2720 | ||
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