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Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title |
Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
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Conference Article |
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Year |
2012 |
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High Performance Computing and Simulation, International Conference on |
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182-187 |
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This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation. |
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Madrid |
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IEEE Xplore |
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978-1-4673-2359-8 |
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HPCS |
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MILAB;HuPBA |
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no |
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Admin @ si @ ISH2012a |
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2038 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
Self-Supervised Visual Representations for Cross-Modal Retrieval |
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Conference Article |
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2019 |
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ACM International Conference on Multimedia Retrieval |
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182–186 |
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Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. |
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Otawa; Canada; june 2019 |
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ICMR |
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DAG; 600.121; 600.129 |
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no |
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Admin @ si @ PGR2019 |
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3288 |
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Author |
Eduardo Aguilar; Petia Radeva |
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Title |
Class-Conditional Data Augmentation Applied to Image Classification |
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Conference Article |
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2019 |
Publication |
18th International Conference on Computer Analysis of Images and Patterns |
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11679 |
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182-192 |
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CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition |
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Abstract |
Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. |
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Salermo; Italy; September 2019 |
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CAIP |
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MILAB; no proj |
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no |
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Admin @ si @ AgR2019 |
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3366 |
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Author |
Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
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Title |
CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
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Journal |
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2020 |
Publication |
IEEE Transactions on Biometrics, Behavior, and Identity Science |
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TTBIS |
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2 |
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2 |
Pages |
182 - 193 |
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Abstract |
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 |
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HuPBA; no proj |
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no |
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Call Number |
Admin @ si @ ZLW2020 |
Serial |
3412 |
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Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
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Title |
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
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Journal Article |
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Year |
2022 |
Publication |
Journal of Thoracic Oncology |
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JTO |
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17 |
Issue |
9 |
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S182 |
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IAM |
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Admin @ si @ RBG2022b |
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3834 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas |
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Title |
Text Segmentation in Colour Posters from the Spanish Civil War Era |
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Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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181 - 185 |
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Abstract |
The extraction of textual content from colour documents of a graphical nature is a complicated task. The text can be rendered in any colour, size and orientation while the existence of complex background graphics with repetitive patterns can make its localization and segmentation extremely difficult.
Here, we propose a new method for extracting textual content from such colour images that makes no assumption as to the size of the characters, their orientation or colour, while it is tolerant to characters that do not follow a straight baseline. We evaluate this method on a collection of documents with historical
connotations: the Posters from the Spanish Civil War. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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DAG @ dag @ ClK2009 |
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1172 |
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Author |
H. Chouaib; Salvatore Tabbone; Oriol Ramos Terrades; F. Cloppet; N. Vincent; A.T. Thierry Paquet |
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Title |
Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost |
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Conference Article |
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2008 |
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Colloque International Francophone sur l'Ecrit et le Document |
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181-186 |
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Rouen, France |
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CIFED |
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DAG |
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no |
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Call Number |
Admin @ si @ CTR2008 |
Serial |
1874 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Title |
Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images |
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Conference Article |
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2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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181 - 185 |
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Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 601.223; 600.077 |
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Admin @ si @ RCO2014a |
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2545 |
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Author |
Javier Vazquez; C. Alejandro Parraga; Maria Vanrell |
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Title |
Ordinal pairwise method for natural images comparison |
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Journal Article |
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2009 |
Publication |
Perception |
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PER |
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38 |
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180 |
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Abstract |
38(Suppl.)ECVP Abstract Supplement
We developed a new psychophysical method to compare different colour appearance models when applied to natural scenes. The method was as follows: two images (processed by different algorithms) were displayed on a CRT monitor and observers were asked to select the most natural of them. The original images were gathered by means of a calibrated trichromatic digital camera and presented one on top of the other on a calibrated screen. The selection was made by pressing on a 6-button IR box, which allowed observers to consider not only the most natural but to rate their selection. The rating system allowed observers to register how much more natural was their chosen image (eg, much more, definitely more, slightly more), which gave us valuable extra information on the selection process. The results were analysed considering both the selection as a binary choice (using Thurstone's law of comparative judgement) and using Bradley-Terry method for ordinal comparison. Our results show a significant difference in the rating scales obtained. Although this method has been used in colour constancy algorithm comparisons, its uses are much wider, eg to compare algorithms of image compression, rendering, recolouring, etc. |
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CIC |
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CAT @ cat @ VPV2009b |
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1191 |
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C. Alejandro Parraga; Javier Vazquez; Maria Vanrell |
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Title |
A new cone activation-based natural images dataset |
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Journal Article |
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2009 |
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Perception |
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PER |
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36 |
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180 |
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We generated a new dataset of digital natural images where each colour plane corresponds to the human LMS (long-, medium-, short-wavelength) cone activations. The images were chosen to represent five different visual environments (eg forest, seaside, mountain snow, urban, motorways) and were taken under natural illumination at different times of day. At the bottom-left corner of each picture there was a matte grey ball of approximately constant spectral reflectance (across the camera's response spectrum,) and nearly Lambertian reflective properties, which allows to compute (and remove, if necessary) the illuminant's colour and intensity. The camera (Sigma Foveon SD10) was calibrated by measuring its sensor's spectral responses using a set of 31 spectrally narrowband interference filters. This allowed conversion of the final camera-dependent RGB colour space into the Smith and Pokorny (1975) cone activation space by means of a polynomial transformation, optimised for a set of 1269 Munsell chip reflectances. This new method is an improvement over the usual 3 × 3 matrix transformation which is only accurate for spectrally-narrowband colours. The camera-to-LMS transformation can be recalculated to consider other non-human visual systems. The dataset is available to download from our website. |
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CIC |
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CAT @ cat @ PVV2009 |
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1193 |
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Author |
Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Xavier Carrillo; R. Hemetsberger; Petia Radeva; J. Mauri; A. Bayes |
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Canvis de volum a la arteria radial despres de la administracio de dos tractaments vasodilatadors. Avaluacio mitjançant ecografia intravascular |
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Conference Article |
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2010 |
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22nd Congres Societat Catalana de Cardiologia, |
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179 |
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Barcelona (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ SFC2010a |
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1367 |
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Eduardo Tusa; Arash Akbarinia; Raquel Gil Rodriguez; Corina Barbalata |
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Title |
Real-Time Face Detection and Tracking Utilising OpenMP and ROS |
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Conference Article |
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2015 |
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3rd Asia-Pacific Conference on Computer Aided System Engineering |
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179 - 184 |
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RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP |
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Abstract |
The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in
low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction. |
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Quito; Ecuador; July 2015 |
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APCASE |
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NEUROBIT |
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no |
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Admin @ si @ TAG2015 |
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2659 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva |
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Title |
Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions |
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Book Chapter |
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2006 |
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11th Iberoamerican Congress on Pattern Recognition |
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4225 |
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178–187 |
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Abstract |
This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns. |
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Cancun (Mexico) |
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Springer Verlag |
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Berlin Heidelberg |
Editor |
.F. Mart ́ınez-Trinidad et al |
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800 |
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Notes |
MV;OR;MILAB;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f |
Serial |
728 |
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Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
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Title |
Improving HOG with Image Segmentation: Application to Human Detection |
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Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
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Volume |
7517 |
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Pages |
178-189 |
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Keywords |
Segmentation; Pedestrian Detection |
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Abstract |
In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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Brno, Czech Republic |
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Springer Berlin Heidelberg |
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Editor |
J. Blanc-Talon et al. |
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English |
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0302-9743 |
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978-3-642-33139-8 |
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ACIVS |
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ADAS;ISE |
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no |
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ADAS @ adas @ SLV2012 |
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1980 |
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Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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Title |
LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
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Conference Article |
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Year |
2015 |
Publication |
22th IEEE International Conference on Image Processing |
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Issue |
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Pages |
178 - 181 |
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Address |
Quebec; Canada; September 2015 |
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ICIP |
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ADAS; 600.076 |
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Call Number |
Admin @ si @ AST2015 |
Serial |
2630 |
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