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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
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Book Chapter |
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2016 |
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Recent Trends in Image Processing and Pattern Recognition |
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709 |
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RTIP2R |
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DAG |
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Admin @ si @ HTR2016 |
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2956 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
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Title |
Unraveling the enigmas of chromosome territoriality during spermatogenesis |
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Conference Article |
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2017 |
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IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia |
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UAB; Barcelona; June 2017 |
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IAM; 600.145 |
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Admin @ si @ SBG2017b |
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2959 |
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Author |
Xinhang Song; Luis Herranz; Shuqiang Jiang |
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Title |
Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs |
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Conference Article |
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2017 |
Publication |
31st AAAI Conference on Artificial Intelligence |
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RGB-D scene recognition; weakly supervised; fine tune; CNN |
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Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. |
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San Francisco CA; February 2017 |
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AAAI |
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LAMP; 600.120 |
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Admin @ si @ SHJ2017 |
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2967 |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title |
Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study |
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Conference Article |
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Year |
2017 |
Publication |
International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) |
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An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identi cation of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
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Quebec; Canada; September 2017 |
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MICCAIW |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ BCR2017 |
Serial |
2968 |
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Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote |
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Title |
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB |
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Conference Article |
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Year |
2017 |
Publication |
1st International Workshop on Physics Based Vision meets Deep Learning |
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Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However,
most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset. |
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Venice; Italy; October 2017 |
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ICCV-PBDL |
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Notes |
LAMP; 600.109; 600.106; 600.120 |
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no |
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Call Number |
Admin @ si @ AWG2017 |
Serial |
2969 |
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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Occlusion Aware Hand Pose Recovery from Sequences of Depth Images |
Type |
Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs. |
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HUPBA; ISE; 602.143; 600.098; 600.119 |
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Call Number |
Admin @ si @ MEC2017 |
Serial |
2970 |
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Author |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Title |
Image vignetting reduction via a maximization of fuzzy entropy |
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Conference Article |
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Year |
2017 |
Publication |
IEEE International Conference on Fuzzy Systems |
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In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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LAMP; 600.120 |
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no |
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Admin @ si @ LMG2017 |
Serial |
2972 |
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Author |
Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen |
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Title |
Bandwidth Limited Object Recognition in High Resolution Imagery |
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Conference Article |
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2017 |
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IEEE Winter conference on Applications of Computer Vision |
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This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. |
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Santa Rosa; CA; USA; March 2017 |
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WACV |
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LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 |
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Admin @ si @ LBW2017 |
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2973 |
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Author |
Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen |
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Title |
Multi-modal Deep Learning Approach for Flood Detection |
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2017 |
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MediaEval Benchmarking Initiative for Multimedia Evaluation |
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In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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Dublin; Ireland; September 2017 |
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MediaEval |
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LAMP; 600.084; 600.109; 600.120 |
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Admin @ si @ LWB2017a |
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2974 |
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Author |
Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov |
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Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks |
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2018 |
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Journal of Medical Robotics Research |
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JMRR |
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3 |
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2 |
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convolutional neural networks; colonoscopy; computer aided diagnosis |
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Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
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MV; no menciona |
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BZM2018 |
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2976 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy |
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2017 |
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25th United European Gastroenterology Week |
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Barcelona, October 2017 |
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ESGE |
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MV; no menciona |
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no |
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Admin @ si @ ABS2017c |
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2978 |
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Cristina Sanchez Montes; F. Javier Sanchez; Cristina Rodriguez de Miguel; Henry Cordova; Jorge Bernal; Maria Lopez Ceron; Josep Llach; Gloria Fernandez Esparrach |
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Title |
Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results |
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Conference Article |
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2017 |
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25th United European Gastroenterology Week |
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polyps; histology; computer vision |
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during colonoscopy, clinicians perform visual inspection of the polyps to predict histology. Kudo’s pit pattern classification is one of the most commonly used for optical diagnosis. These surface patterns present a contrast with respect to their neighboring regions and they can be considered as bright regions in the image that can attract the attention of computational methods. |
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Barcelona; October 2017 |
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ESGE |
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MV; no menciona |
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Admin @ si @ SSR2017 |
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2979 |
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Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
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A survey on deep learning based approaches for action and gesture recognition in image sequences |
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Conference Article |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. |
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Washington; USA; May 2017 |
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HUPBA; no proj |
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Call Number |
Admin @ si @ ACB2017b |
Serial |
2982 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Color representation in CNNs: parallelisms with biological vision |
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Conference Article |
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Year |
2017 |
Publication |
ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision |
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Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN
[2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions
of them to quantify color tuning properties of artificial neurons to provide a classification of the network population.
We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first
layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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Address |
Venice; Italy; October 2017 |
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ICCV-MBCC |
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Notes |
CIC; 600.087; 600.051 |
Approved |
no |
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Call Number |
Admin @ si @ RaV2017 |
Serial |
2984 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
Type |
Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
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Address |
Kyoto; Japan; November 2017 |
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Conference |
GREC |
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Notes |
DAG; 600.097; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ JRL2017b |
Serial |
3073 |
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| Permanent link to this record |
