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Author |
Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol |
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Title |
Actions in Context: System for people with Dementia |
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Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems |
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3-14 |
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Keywords |
Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia |
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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. |
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Barcelona; September 2013 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-04177-3 |
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ECCS |
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HUPBA;MILAB |
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no |
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Admin @ si @ PCE2013 |
Serial |
2354 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Abstract |
Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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ACDC |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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3-10 |
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In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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LNCS |
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0302-9743 |
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978-3-662-44853-3 |
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Notes |
DAG; 600.045; 600.055; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ RKL2014 |
Serial |
2700 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels |
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Conference Article |
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Year |
2016 |
Publication |
29th Canadian Conference on Artificial Intelligence |
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9673 |
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3-14 |
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Abstract |
In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect |
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Victoria; Canada; May 2016 |
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Springer International Publishing |
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AI |
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Notes |
HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @ BGE2016b |
Serial |
2770 |
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Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
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Title |
Video Description Using Bidirectional Recurrent Neural Networks |
Type |
Conference Article |
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Year |
2016 |
Publication |
25th International Conference on Artificial Neural Networks |
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Volume |
2 |
Issue |
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Pages |
3-11 |
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Keywords |
Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
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Abstract |
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
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Barcelona; September 2016 |
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ICANN |
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MILAB; |
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no |
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Call Number |
Admin @ si @ PBR2016 |
Serial |
2833 |
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Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
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Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
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Conference Article |
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Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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Volume |
13639 |
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Pages |
3-12 |
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Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
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Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Call Number |
Admin @ si @ GBS2022 |
Serial |
3733 |
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Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
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Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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Volume |
14191 |
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3-17 |
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Keywords |
Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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Abstract |
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ NFC2023 |
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3859 |
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Author |
Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva |
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Title |
CCLM: Class-Conditional Label Noise Modelling |
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Conference Article |
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2023 |
Publication |
11th Iberian Conference on Pattern Recognition and Image Analysis |
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14062 |
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3-14 |
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The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness. |
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Alicante; Spain; June 2023 |
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IbPRIA |
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MILAB |
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no |
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Admin @ si @ TNM2023 |
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3925 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Advances in Vision-Based Human Body Modeling |
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2004 |
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3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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ADAS |
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ADAS @ adas @ SAG2004a |
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458 |
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Josep Llados; Dorothea Blostein |
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Special Issue on Graphics Recognition |
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2007 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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9 |
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1 |
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1–2 |
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DAG @ dag @ LlB2007 |
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781 |
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Maya Dimitrova; Ch. Roumenin; Siya Lozanova; David Rotger; Petia Radeva |
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An Interface System Based on Multimodal Principle for Cardiological Diagnosis Assistance |
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2007 |
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International Conference On Computer Systems And Technologies |
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IIIB.4 |
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Bulgaria |
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CompSysTech’07 |
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MILAB |
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BCNPCL @ bcnpcl @ DRL2007 |
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833 |
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Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich |
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Image Segmentation in the Presence of Shadows and Highligts |
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2008 |
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10th European Conference on Computer Vision |
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5305 |
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1–14 |
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Marseille (France) |
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ECCV |
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CAT;CIC |
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CAT @ cat @ VVB2008b |
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1013 |
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Agata Lapedriza; David Masip; Jordi Vitria |
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On the Use of Independent Tasks for Face Recognition |
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2008 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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1–6 |
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CVPR |
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OR; MV |
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BCNPCL @ bcnpcl @ LMV2008b |
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1043 |
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Ariel Amato; Mikhail Mozerov; Ivan Huerta; Jordi Gonzalez; Juan J. Villanueva |
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Title |
ackground Subtraction Technique Based on Chromaticity and Intensity Patterns |
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Conference Article |
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Year |
2008 |
Publication |
19th International Conference on Pattern Recognition, |
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1–4 |
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Tampa (Florida) |
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ICPR |
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ISE |
Approved |
no |
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Call Number |
ISE @ ise @ AMH2008 |
Serial |
1071 |
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| Permanent link to this record |
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Author |
Murad Al Haj; Francisco Javier Orozco; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Automatic Face and Facial Features Initialization for Robust and Accurate Tracking |
Type |
Conference Article |
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Year |
2008 |
Publication |
19th International Conference on Pattern Recognition. |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1– 4 |
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Keywords |
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Abstract |
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Address |
Tampa (Florida) |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Conference |
ICPR |
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Notes |
ISE |
Approved |
no |
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Call Number |
ISE @ ise @ AOG2008 |
Serial |
1072 |
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| Permanent link to this record |
