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Author |
C. Santa-Marta; Jaume Garcia; A. Bajo; J.J. Vaquero; M. Ledesma-Carbayo; Debora Gil |
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Title |
Influence of the Temporal Resolution on the Quantification of Displacement Fields in Cardiac Magnetic Resonance Tagged Images |
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Conference Article |
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2008 |
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XXVI Congreso Anual de la Sociedad Española de Ingenieria Biomedica |
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352–353 |
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It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possibl e. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%. |
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Valladolid |
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Roberto hornero, Saniel Abasolo |
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CASEIB |
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IAM; |
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IAM @ iam @ SGB2008 |
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1033 |
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Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez |
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Title |
Catastrophic interference in Disguised Face Recognition |
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Conference Article |
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2019 |
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9th Iberian Conference on Pattern Recognition and Image Analysis |
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11868 |
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64-75 |
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Neural network forgetness; Face recognition; Disguised Faces |
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It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. |
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IbPRIA |
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ISE; 600.098; 600.119 |
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Admin @ si @ AVG2019 |
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3416 |
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Author |
Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
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Title |
Learning local feature descriptors with triplets and shallow convolutional neural networks |
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Conference Article |
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2016 |
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27th British Machine Vision Conference |
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It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets. |
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York; UK; September 2016 |
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BMVC |
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ADAS; 600.086 |
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Admin @ si @ BRP2016 |
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2818 |
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Author |
Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari |
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Robust non-blind color video watermarking using QR decomposition and entropy analysis |
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Journal Article |
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2016 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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38 |
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838-847 |
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Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition |
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Abstract |
Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. |
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HuPBA;MILAB; |
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no |
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Admin @ si @RSA2016 |
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2766 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Hand pose aware multimodal isolated sign language recognition |
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Journal Article |
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Year |
2020 |
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Multimedia Tools and Applications |
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MTAP |
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80 |
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127–163 |
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Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model. |
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HUPBA; no menciona |
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no |
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Admin @ si @ RKE2020 |
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3524 |
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Author |
Debora Gil; Antoni Rosell |
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Title |
Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? |
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2019 |
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World Lung Cancer Conference |
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Invited speaker |
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Barcelona; September 2019 |
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IASLC WCLC |
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IAM; 600.139; 600.145 |
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no |
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Admin @ si @ GiR2019 |
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3361 |
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Author |
Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats |
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Title |
Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription |
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2017 |
Publication |
Journal of Medical Engineering & Technology |
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JMET |
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41 |
Issue |
6 |
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486-497 |
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Abstract |
INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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HUPBA; no menciona |
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no |
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Admin @ si @ EPB2017 |
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3010 |
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Author |
Ricard Balague |
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Title |
Exploring the combination of color cues for intrinsic image decomposition |
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Report |
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2014 |
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CVC Technical Report |
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178 |
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Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. |
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UAB; September 2014 |
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Master's thesis |
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CIC; 600.074 |
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Admin @ si @ Bal2014 |
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2579 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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Title |
The Photometry of Intrinsic Images |
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Conference Article |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CVPR |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
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2506 |
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Author |
Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
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Title |
Automatic Segmentation of Artery Wall in Coronary IVUS Images: A Probabilistic Approach |
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2000 |
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International Conference on Pattern Recognition |
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4 |
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352-355 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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IAM;MILAB |
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IAM @ iam @ GRS2000a |
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1537 |
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Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
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Automatic Segmentation of Artery Wall in Coronary IVUS Images: a Probabilistic Approach |
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2000 |
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Proceedings of CIC’2000 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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Cambridge, Massachussets |
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IAM;MILAB |
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IAM @ iam @ GRS2000 |
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1538 |
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Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva |
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Title |
Fast Rigid Registration of Vascular Structures in IVUS Sequences |
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2009 |
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IEEE Transactions on Information Technology in Biomedicine |
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13 |
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6 |
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106-1011 |
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Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation. |
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1089-7771 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ GPL2009 |
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1250 |
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Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
IVUS Tissue Characterization with Sub-class Error-correcting Output Codes |
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Conference Article |
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2008 |
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Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008. |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets. |
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CVPR |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2008 |
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1041 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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2009 |
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Journal of Signal Processing Systems |
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55 |
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1-3 |
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35–47 |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Author |
Carlo Gatta; Simone Balocco; Francesco Ciompi; R. Hemetsberger; Oriol Rodriguez-Leor; Petia Radeva |
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Real-time gating of IVUS sequences based on motion blur analysis: Method and quantitative validation |
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2010 |
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13th international conference on Medical image computing and computer-assisted intervention |
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II |
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59-67 |
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Intravascular Ultrasound (IVUS) is an image-guiding technique for cardiovascular diagnostic, providing cross-sectional images of vessels. During the acquisition, the catheter is pulled back (pullback) at a constant speed in order to acquire spatially subsequent images of the artery. However, during this procedure, the heart twist produces a swinging fluctuation of the probe position along the vessel axis. In this paper we propose a real-time gating algorithm based on the analysis of motion blur variations during the IVUS sequence. Quantitative tests performed on an in-vitro ground truth data base shown that our method is superior to state of the art algorithms both in computational speed and accuracy. |
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Springer-Verlag Berlin |
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MICCAI |
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MILAB |
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BCNPCL @ bcnpcl @ GBC2010 |
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1447 |
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