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| Author | Jean-Pascal Jacob; Mariella Dimiccoli; Lionel Moisan | ||||
Title  |
Active skeleton for bacteria modeling | Type | Journal Article | ||
| Year | 2016 | Publication | Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization | Abbreviated Journal | CMBBE |
| Volume | 5 | Issue | 4 | Pages | 274-286 |
| Keywords | Bacteria modelling; medial axis; active contours; active skeleton; shape contraints | ||||
| Abstract | The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at this http URL | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ JDM2016 | Serial | 2711 | ||
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| Author | M. Ivasic-Kos; M. Pobar; Jordi Gonzalez | ||||
| Title |
Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures | Type | Conference Article | ||
| Year | 2019 | Publication | 13th International Conference on Signal Processing and Communication Systems | Abbreviated Journal | |
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| Abstract | In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques. The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player's appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF. The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons. | ||||
| Address | Gold Coast; Australia; December 2019 | ||||
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| Area | Expedition | Conference | ICSPCS2 | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ IPG2019 | Serial | 3415 | ||
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| Author | Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool | ||||
| Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation | Type | Conference Article | ||
| Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2312 - 2319 | ||
| Keywords | Semantic Segmentation | ||||
| Abstract | Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| ISSN | 1550-5499 | ISBN | Medium | ||
| Area | Expedition | Conference | ICCV | ||
| Notes | ADAS; 600.057 | Approved | no | ||
| Call Number | ADAS @ adas @ RBN2013 | Serial | 2377 | ||
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| Author | Hamed H. Aghdam; Abel Gonzalez-Garcia; Joost Van de Weijer; Antonio Lopez | ||||
| Title |
Active Learning for Deep Detection Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 18th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 3672-3680 | ||
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| Abstract | The cost of drawing object bounding boxes (ie labeling) for millions of images is prohibitively high. For instance, labeling pedestrians in a regular urban image could take 35 seconds on average. Active learning aims to reduce the cost of labeling by selecting only those images that are informative to improve the detection network accuracy. In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks. We propose a new image-level scoring process to rank unlabeled images for their automatic selection, which clearly outperforms classical scores. The proposed method can be applied to videos and sets of still images. In the former case, temporal selection rules can complement our scoring process. As a relevant use case, we extensively study the performance of our method on the task of pedestrian detection. Overall, the experiments show that the proposed method performs better than random selection. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | ADAS; LAMP; 600.124; 600.109; 600.141; 600.120; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ AGW2019 | Serial | 3321 | ||
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| Author | Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
| Title |
Active labeling: Application to wireless endoscopy analysis | Type | Conference Article | ||
| Year | 2012 | Publication | High Performance Computing and Simulation, International Conference on | Abbreviated Journal | |
| Volume | Issue | Pages | 174-181 | ||
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| Abstract | Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”. | ||||
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| ISSN | ISBN | 978-1-4673-2359-8 | Medium | ||
| Area | Expedition | Conference | HPCS | ||
| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RDS2012 | Serial | 2152 | ||
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| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title |
Active labeling application applied to food-related object recognition | Type | Conference Article | ||
| Year | 2013 | Publication | 5th International Workshop on Multimedia for Cooking & Eating Activities | Abbreviated Journal | |
| Volume | Issue | Pages | 45-50 | ||
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| Abstract | Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015]. |
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| Address | Barcelona; October 2013 | ||||
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| Area | Expedition | Conference | ACM-CEA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2013b | Serial | 2637 | ||
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| Author | T. Alejandra Vidal; Andrew J. Davison; Juan Andrade; David W. Murray | ||||
| Title |
Active Control for Single Camera SLAM | Type | Miscellaneous | ||
| Year | 2006 | Publication | IEEE International Conference on Robotics and Automation, 1930–1936 | Abbreviated Journal | |
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| Address | Orlando (Florida) | ||||
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| Notes | Approved | no | |||
| Call Number | DAG @ dag @ VDA2006 | Serial | 666 | ||
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| Author | Q. Xue; Laura Igual; A. Berenguel; M. Guerrieri; L. Garrido | ||||
| Title |
Active Contour Segmentation with Affine Coordinate-Based Parametrization | Type | Conference Article | ||
| Year | 2014 | Publication | 9th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 5-14 | |
| Keywords | Active Contours; Affine Coordinates; Mean Value Coordinates | ||||
| Abstract | In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments. |
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| Address | Lisboa; January 2014 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | OR;MILAB | Approved | no | ||
| Call Number | Admin @ si @ XIB2014 | Serial | 2452 | ||
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| Author | Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol | ||||
| Title |
Actions in Context: System for people with Dementia | Type | Conference Article | ||
| Year | 2013 | Publication | 2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 3-14 | ||
| Keywords | Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia | ||||
| Abstract | In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios. | ||||
| Address | Barcelona; September 2013 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-319-04177-3 | Medium | |
| Area | Expedition | Conference | ECCS | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PCE2013 | Serial | 2354 | ||
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| Author | Ignasi Rius; Jordi Gonzalez; Javier Varona; Xavier Roca | ||||
| Title |
Action-specific motion prior for efficient bayesian 3D human body tracking | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 42 | Issue | 11 | Pages | 2907–2921 |
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| Abstract | In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane. Towards this end, an action-specific motion model is trained from a database of real motion-captured performances. The learnt motion model is used within a particle filtering framework as a priori knowledge on human motion. First, our dynamic model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints. |
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ RGV2009 | Serial | 1159 | ||
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| Author | Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez | ||||
| Title |
Action-Based Representation Learning for Autonomous Driving | Type | Conference Article | ||
| Year | 2020 | Publication | Conference on Robot Learning | Abbreviated Journal | |
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| Abstract | Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). | ||||
| Address | virtual; November 2020 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ XCP2020 | Serial | 3487 | ||
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| Author | Ignasi Rius; Javier Varona; Jordi Gonzalez; Juan J. Villanueva | ||||
| Title |
Action Spaces for Efficient Bayesian Tracking of Human Motion | Type | Miscellaneous | ||
| Year | 2006 | Publication | International Conference on Pattern Recognition (ICPR´06), 1: 472–475 | Abbreviated Journal | |
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| Address | Hong Kong (China) | ||||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ RVG2006 | Serial | 770 | ||
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| Author | Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari | ||||
| Title |
Action recognition using single-pixel time-of-flight detection | Type | Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 4 | Pages | 414 |
| Keywords | single pixel single photon image acquisition; time-of-flight; action recognition | ||||
| Abstract | Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network. |
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ OHC2019 | Serial | 3319 | ||
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| Author | Cesar de Souza | ||||
| Title |
Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
| Address | April 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Naila Murray | |
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Sou2018 | Serial | 3127 | ||
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| Author | Jordi Gonzalez | ||||
| Title |
Action recognition in application domains | Type | Report | ||
| Year | 1999 | Publication | CVC Technical Report #13 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ Gon1999 | Serial | 357 | ||
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