Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–20] |
Records | |||||
---|---|---|---|---|---|
Author | Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus | ||||
Title | Color Naming | Type | Book Chapter | ||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
Volume | Issue | 17 | Pages | 287-317 | |
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ WBV2012 | Serial | 2063 | ||
Permanent link to this record | |||||
Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Photometric Invariance by Machine Learning | Type | Book Chapter | ||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
Volume | 7 | Issue | Pages | 113-134 | |
Keywords | road detection | ||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | iConcept Press Ltd | Place of Publication | Editor | Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ AlL2012 | Serial | 2186 | ||
Permanent link to this record | |||||
Author | Murad Al Haj; Carles Fernandez; Zhanwu Xiong; Ivan Huerta; Jordi Gonzalez; Xavier Roca | ||||
Title | Beyond the Static Camera: Issues and Trends in Active Vision | Type | Book Chapter | ||
Year | 2011 | Publication | Visual Analysis of Humans: Looking at People | Abbreviated Journal | |
Volume | Issue | 2 | Pages | 11-30 | |
Keywords | |||||
Abstract | Maximizing both the area coverage and the resolution per target is highly desirable in many applications of computer vision. However, with a limited number of cameras viewing a scene, the two objectives are contradictory. This chapter is dedicated to active vision systems, trying to achieve a trade-off between these two aims and examining the use of high-level reasoning in such scenarios. The chapter starts by introducing different approaches to active cameras configurations. Later, a single active camera system to track a moving object is developed, offering the reader first-hand understanding of the issues involved. Another section discusses practical considerations in building an active vision platform, taking as an example a multi-camera system developed for a European project. The last section of the chapter reflects upon the future trends of using semantic factors to drive smartly coordinated active systems. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer London | Place of Publication | Editor | Th.B. Moeslund; A. Hilton; V. Krüger; L. Sigal | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-0-85729-996-3 | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ AFX2011 | Serial | 1814 | ||
Permanent link to this record | |||||
Author | Debora Gil; Petia Radeva | ||||
Title | Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling | Type | Book Chapter | ||
Year | 2003 | Publication | Energy Minimization Methods In Computer Vision And Pattern Recognition | Abbreviated Journal | LNCS |
Volume | 2683 | Issue | Pages | 357-372 | |
Keywords | Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature | ||||
Abstract | Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer, Berlin | Place of Publication | Lisbon, PORTUGAL | Editor | Springer, B. |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 3-540-40498-8 | Medium | |
Area | Expedition | Conference | |||
Notes | IAM;MILAB | Approved | no | ||
Call Number | IAM @ iam @ GIR2003b | Serial | 1535 | ||
Permanent link to this record | |||||
Author | Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza | ||||
Title | Evolving weighting schemes for the Bag of Visual Words | Type | Journal Article | ||
Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
Volume | 28 | Issue | 5 | Pages | 925–939 |
Keywords | Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision | ||||
Abstract | The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | Springer | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HUPBA;MV; no menciona | Approved | no | ||
Call Number | Admin @ si @ EPE2017 | Serial | 2743 | ||
Permanent link to this record | |||||
Author | Marina Alberti | ||||
Title | Detection and Alignment of Vascular Structures in Intravascular Ultrasound using Pattern Recognition Techniques | Type | Book Whole | ||
Year | 2013 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | In this thesis, several methods for the automatic analysis of Intravascular Ultrasound
(IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques. First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classication problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are rened using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability. Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at dierent moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological proles from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm - is applied to the proles and adapted to the specic clinical problem. Two dierent robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the prole extraction. The benets of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method. Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a rst classication phase (the identi- cation of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set. |
||||
Address | Barcelona | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Simone Balocco;Petia Radeva | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ Alb2013 | Serial | 2215 | ||
Permanent link to this record | |||||
Author | Victor Ponce | ||||
Title | Evolutionary Bags of Space-Time Features for Human Analysis | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis Universitat de Barcelona, UOC and CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture | ||||
Abstract | The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice | ||||
Address | June 2016 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Xavier Baro;Hugo Jair Escalante | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HuPBA | Approved | no | ||
Call Number | Pon2016 | Serial | 2814 | ||
Permanent link to this record | |||||
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 | |
Volume | Issue | Pages | |||
Keywords | |||||
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. |
||||
Address | January 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Stan Sclaroff | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-940902-0-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ Her2015 | Serial | 2576 | ||
Permanent link to this record | |||||
Author | Meysam Madadi | ||||
Title | Human Segmentation, Pose Estimation and Applications | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Automatic analyzing humans in photographs or videos has great potential applications in computer vision, including medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task.
In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. nonrigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures. |
||||
Address | October 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Jordi Gonzalez | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-945373-3-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ Mad2017 | Serial | 3017 | ||
Permanent link to this record | |||||
Author | Sergio Escalera; Ralf Herbrich | ||||
Title | The NeurIPS’18 Competition: From Machine Learning to Intelligent Conversations | Type | Book Whole | ||
Year | 2020 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | Sergio Escalera; Ralf Hebrick | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2520-1328 | ISBN | 978-3-030-29134-1 | Medium | |
Area | Expedition | Conference | |||
Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ HeE2020 | Serial | 3328 | ||
Permanent link to this record | |||||
Author | Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio | ||||
Title | Introduction to NIPS 2017 Competition Track | Type | Book Chapter | ||
Year | 2018 | Publication | The NIPS ’17 Competition: Building Intelligent Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1-23 | ||
Keywords | |||||
Abstract | Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer | Place of Publication | Editor | Sergio Escalera; Markus Weimer | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-94042-7 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EWB2018 | Serial | 3200 | ||
Permanent link to this record | |||||
Author | Albert Clapes | ||||
Title | Learning to recognize human actions: from hand-crafted to deep-learning based visual representations | Type | Book Whole | ||
Year | 2019 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Action recognition is a very challenging and important problem in computer vision. Researchers working on this field aspire to provide computers with the abil ity to visually perceive human actions – that is, to observe, interpret, and under stand human-related events that occur in the physical environment merely from visual data. The applications of this technology are numerous: human-machine interaction, e-health, monitoring/surveillance, and content-based video retrieval, among others. Hand-crafted methods dominated the field until the apparition of the first successful deep learning-based action recognition works. Although ear lier deep-based methods underperformed with respect to hand-crafted approaches, these slowly but steadily improved to become state-of-the-art, eventually achieving better results than hand-crafted ones. Still, hand-crafted approaches can be advan tageous in certain scenarios, specially when not enough data is available to train very large deep models or simply to be combined with deep-based methods to fur ther boost the performance. Hence, showing how hand-crafted features can provide extra knowledge the deep networks are notable to easily learn about human actions.
This Thesis concurs in time with this change of paradigm and, hence, reflects it into two distinguished parts. In the first part, we focus on improving current suc cessful hand-crafted approaches for action recognition and we do so from three dif ferent perspectives. Using the dense trajectories framework as a backbone: first, we explore the use of multi-modal and multi-view input data to enrich the trajectory de scriptors. Second, we focus on the classification part of action recognition pipelines and propose an ensemble learning approach, where each classifier leams from a different set of local spatiotemporal features to then combine their outputs following an strategy based on the Dempster-Shaffer Theory. And third, we propose a novel hand-crafted feature extraction method that constructs a rnid-level feature descrip tion to better modellong-term spatiotemporal dynarnics within action videos. Moving to the second part of the Thesis, we start with a comprehensive study of the current deep-learning based action recognition methods. We review both fun damental and cutting edge methodologies reported during the last few years and introduce a taxonomy of deep-leaming methods dedicated to action recognition. In particular, we analyze and discuss how these handle the temporal dimension of data. Last but not least, we propose a residual recurrent network for action recogni tion that naturally integrates all our previous findings in a powerful and prornising framework. |
||||
Address | January 2019 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-948531-2-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ Cla2019 | Serial | 3219 | ||
Permanent link to this record | |||||
Author | Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez | ||||
Title | Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance | Type | Conference Article | ||
Year | 2013 | Publication | Proceedings of the International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | 1 | Issue | Pages | 153--161 | |
Keywords | Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model | ||||
Abstract | Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability | ||||
Address | Barcelona; February 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | SciTePress | Place of Publication | Portugal | Editor | Sebastiano Battiato and José Braz |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-989-8565-47-1 | Medium | ||
Area | 800 | Expedition | Conference | VISAPP | |
Notes | IAM;MV; 600.044; 600.047; 600.060; 605.203 | Approved | no | ||
Call Number | IAM @ iam @ SGR2013 | Serial | 2123 | ||
Permanent link to this record | |||||
Author | Oriol Ramos Terrades | ||||
Title | Linear Combination of Multiresolution Descriptors: Application to Graphics Recognition | Type | Book Whole | ||
Year | 2006 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC & Universite Nancy 2 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Salvatore Antoine Tabbone;Ernest Valveny | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ Ram2006 | Serial | 713 | ||
Permanent link to this record | |||||
Author | F. Javier Sanchez; Jordi Vitria | ||||
Title | ViLi + : Extended Lisp for image Processing and Computer Vision. | Type | Conference Article | ||
Year | 1994 | Publication | Progress in Image Analysis and Processing III | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | World Scientific | Place of Publication | Editor | S.Impedovo | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 981-02-1552-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | MV;OR | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 | Serial | 114 | ||
Permanent link to this record |