| Records |
| Author |
Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
| Title |
Automatic Digital Biometry Analysis based on Depth Maps |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
64 |
Issue |
9 |
Pages  |
1316-1325 |
| Keywords |
Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
| Abstract |
World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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| Publisher |
Elsevier |
Place of Publication |
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Medium |
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Expedition |
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Conference |
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| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RCR2013 |
Serial |
2252 |
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| Author |
Aura Hernandez-Sabate; David Rotger; Debora Gil |
| Title |
Image-based ECG sampling of IVUS sequences |
Type |
Conference Article |
| Year |
2008 |
Publication |
Proc. IEEE Ultrasonics Symp. IUS 2008 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1330-1333 |
| Keywords |
Longitudinal Motion; Image-based ECG-gating; Fourier analysis |
| Abstract |
Longitudinal motion artifacts in IntraVascular UltraSound (IVUS) sequences hinders a properly 3D reconstruction and vessel measurements. Most of current techniques base on the ECG signal to obtain a gated pullback without the longitudinal artifact by using a specific hardware or the ECG signal itself. The potential of IVUS images processing for phase retrieval still remains little explored. In this paper, we present a fast forward image-based algorithm to approach ECG sampling. Inspired on the fact that maximum and minimum lumen areas are related to end-systole and end-diastole, our cardiac phase retrieval is based on the analysis of tissue density of mass along the sequence. The comparison between automatic and manual phase retrieval (0.07 ± 0.07 mm. of error) encourages a deep validation contrasting with ECG signals. |
| Address |
Beijing (China) |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
IAM;MILAB |
Approved |
no |
| Call Number |
IAM @ iam @ HRG2008 |
Serial |
1553 |
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| Author |
Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin-Yuste; Manel Sabate; Petia Radeva |
| Title |
Accurate coronary centerline extraction, caliber estimation and catheter detection in angiographies |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
| Volume |
16 |
Issue |
6 |
Pages |
1332-1340 |
| Keywords |
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| Abstract |
Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%. |
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Place of Publication |
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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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| ISSN |
1089-7771 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ HGE2012 |
Serial |
2141 |
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| Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
| Title |
Writer identification in handwritten musical scores with bags of notes |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
5 |
Pages |
1337-1345 |
| Keywords |
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| Abstract |
Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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Place of Publication |
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Series Issue |
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Edition |
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| ISSN |
0031-3203 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GFV2013 |
Serial |
2307 |
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| Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
| Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
| Volume |
16 |
Issue |
6 |
Pages |
1341-1352 |
| Keywords |
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| Abstract |
Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
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Place of Publication |
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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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| ISSN |
1089-7771 |
ISBN |
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Medium |
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| Area |
800 |
Expedition |
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Conference |
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| Notes |
MILAB; MV; OR;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ SDV2012 |
Serial |
2124 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
| Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
| Keywords |
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| Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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Edition |
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| ISSN |
0196-2892 |
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Conference |
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| Notes |
LAMP; 600.079;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
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| Author |
Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva |
| Title |
SRBF: Speckle Reducing Bilateral Filtering |
Type |
Journal Article |
| Year |
2010 |
Publication |
Ultrasound in Medicine and Biology |
Abbreviated Journal |
UMB |
| Volume |
36 |
Issue |
8 |
Pages |
1353-1363 |
| Keywords |
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| Abstract |
Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). |
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| Notes |
MILAB;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ BGP2010 |
Serial |
1314 |
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| Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez |
| Title |
A Coarse-to-fine Approach for fast Deformable Object Detection |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1353-1360 |
| Keywords |
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| Abstract |
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| Address |
Colorado Springs; USA |
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Conference |
CVPR |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ PVG2011 |
Serial |
1764 |
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| Author |
Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca |
| Title |
Large scale continuous visual event recognition using max-margin Hough transformation framework |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
117 |
Issue |
10 |
Pages |
1356–1368 |
| Keywords |
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| Abstract |
In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions. |
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Edition |
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| ISSN |
1077-3142 |
ISBN |
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Medium |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CGR2013 |
Serial |
2413 |
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| Author |
F. Javier Sanchez; Jorge Bernal |
| Title |
Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject |
Type |
Conference Article |
| Year |
2018 |
Publication |
4th International Conference of Higher Education Advances |
Abbreviated Journal |
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Issue |
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Pages |
1359-1366 |
| Keywords |
Monitoring; Evaluation tool; Gamification; Student motivation |
| Abstract |
The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject. |
| Address |
Valencia; June 2018 |
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Conference |
HEAD |
| Notes |
MV; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SaB2018 |
Serial |
3165 |
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| Author |
Arash Akbarinia; C. Alejandro Parraga |
| Title |
Feedback and Surround Modulated Boundary Detection |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
126 |
Issue |
12 |
Pages |
1367–1380 |
| Keywords |
Boundary detection; Surround modulation; Biologically-inspired vision |
| Abstract |
Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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| Notes |
NEUROBIT; 600.068; 600.072 |
Approved |
no |
| Call Number |
Admin @ si @ AkP2018b |
Serial |
2991 |
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| Author |
Ferran Diego; Joan Serrat; Antonio Lopez |
| Title |
Joint spatio-temporal alignment of sequences |
Type |
Journal Article |
| Year |
2013 |
Publication |
IEEE Transactions on Multimedia |
Abbreviated Journal |
TMM |
| Volume |
15 |
Issue |
6 |
Pages |
1377-1387 |
| Keywords |
video alignment |
| Abstract |
Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times. |
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| ISSN |
1520-9210 |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ DSL2013; ADAS @ adas @ |
Serial |
2228 |
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| Author |
Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning |
Type |
Conference Article |
| Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
1381-1390 |
| Keywords |
Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data |
| Abstract |
Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online. |
| Address |
Virtual; Waikoloa; Hawai; USA; January 2022 |
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Conference |
WACV |
| Notes |
DAG; 600.155; 302.105 |
Approved |
no |
| Call Number |
Admin @ si @ BGK2022 |
Serial |
3662 |
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| Author |
Fadi Dornaika; Angel Sappa |
| Title |
A Featureless and Stochastic Approach to On-board Stereo Vision System Pose |
Type |
Journal Article |
| Year |
2009 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
27 |
Issue |
9 |
Pages |
1382–1393 |
| Keywords |
On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping |
| Abstract |
This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach. |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ DoS2009b |
Serial |
1152 |
| Permanent link to this record |
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| Author |
Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg |
| Title |
Painting-91: A Large Scale Database for Computational Painting Categorization |
Type |
Journal Article |
| Year |
2014 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
25 |
Issue |
6 |
Pages |
1385-1397 |
| Keywords |
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| Abstract |
Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
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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| ISSN |
0932-8092 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
CIC; LAMP; 600.074; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ KBW2014 |
Serial |
2510 |
| Permanent link to this record |
