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Author |
Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
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Book Chapter |
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Year |
2015 |
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Artificial Intelligence Research and Development |
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277 |
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247 - 256 |
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Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. |
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IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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MILAB |
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Admin @ si @TVR2015 |
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2780 |
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Author |
Agata Lapedriza; David Masip; David Sanchez |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Emotions Classification using Facial Action Units Recognition |
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Conference Article |
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Year |
2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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55-64 |
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In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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978-1-61499-451-0 |
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OR;MV |
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Admin @ si @ LMS2014 |
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2622 |
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Author |
Maedeh Aghaei; Petia Radeva |
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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MILAB |
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Admin @ si @ AgR2015 |
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2607 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
A First Approach to Activity Recognition Using Topic Models |
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Conference Article |
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Year |
2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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74 - 82 |
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In this work, we present a first approach to activity patterns discovery by mean of topic models. Using motion data collected with a wearable device we prototype, TheBadge, we analyse raw accelerometer data using Latent Dirichlet Allocation (LDA), a particular instantiation of topic models. Results show that for particular values of the parameters necessary for applying LDA to a countinous dataset, good accuracies in activity classification can be achieved. |
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Cardona, Spain |
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978-1-60750-061-2 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ CPR2009e |
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1231 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
Measuring Interest of Human Dyadic Interactions |
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Conference Article |
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2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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45-54 |
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In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions. |
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Cardona (Spain) |
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978-1-60750-061-2 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ EPR2009b |
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1182 |
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Author |
Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva |
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Title |
Text Detection in Urban Scenes (video sample) |
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Conference Article |
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Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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35–44 |
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Abstract. Text detection in urban scenes is a hard task due to the high variability of text appearance: different text fonts, changes in the point of view, or partial occlusion are just a few problems. Text detection can be specially suited for georeferencing business, navigation, tourist assistance, or to help visual impaired people. In this paper, we propose a general methodology to deal with the problem of text detection in outdoor scenes. The method is based on learning spatial information of gradient based features and Census Transform images using a cascade of classifiers. The method is applied in the context of Mobile Mapping systems, where a mobile vehicle captures urban image sequences. Moreover, a cover data set is presented and tested with the new methodology. The results show high accuracy when detecting multi-linear text regions with high variability of appearance, at same time that it preserves a low false alarm rate compared to classical approaches |
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Cardona (Spain) |
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978-1-60750-061-2 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ EBV2009 |
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1181 |
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Author |
Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria |
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Title |
Generic Object Recognition in Urban Image Databases |
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Conference Article |
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2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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27-34 |
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In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (>500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations. |
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Cardona (Spain) |
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978-1-60750-061-2 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ VER2009 |
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1183 |
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Author |
Francesco Ciompi; Oriol Pujol; Oriol Rodriguez-Leor; Angel Serrano; J. Mauri; Petia Radeva |
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Title |
On in-vitro and in-vivo IVUS data fusion |
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Conference Article |
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2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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147-156 |
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The design and the validation of an automatic plaque characterization technique based on Intravascular Ultrasound (IVUS) usually requires a data ground-truth. The histological analysis of post-mortem coronary arteries is commonly assumed as the state-of-the-art process for the extraction of a reliable data-set of atherosclerotic plaques. Unfortunately, the amount of data provided by this technique is usually few, due to the difficulties in collecting post-mortem cases and phenomena of tissue spoiling during histological analysis. In this paper we tackle the process of fusing in-vivo and in-vitro IVUS data starting with the analysis of recently proposed approaches for the creation of an enhanced IVUS data-set; furthermore, we propose a new approach, named pLDS, based on semi-supervised learning with a data selection criterion. The enhanced data-set obtained by each one of the analyzed approaches is used to train a classifier for tissue characterization purposes. Finally, the discriminative power of each classifier is quantitatively assessed and compared by classifying a data-set of validated in-vitro IVUS data. |
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Cardona (Spain) |
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978-1-60750-061-2 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ CPR2009d |
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1204 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach |
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Journal Article |
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2009 |
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International Journal of Electronic Commerce |
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14 |
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1 |
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89-108 |
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The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach. |
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1086-4415 |
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ADAS |
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ADAS @ adas @ JSL2009b |
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1237 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
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Conference Article |
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2017 |
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26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Robotics and Vision; Vision and Perception |
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Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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Admin @ si @ SJH2017b |
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2966 |
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Author |
Javier Vazquez; C. Alejandro Parraga; Maria Vanrell; Ramon Baldrich |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset |
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Journal Article |
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2009 |
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Journal of Imaging Science and Technology |
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53 |
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3 |
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031105–9 |
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The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene. |
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CIC |
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CAT @ cat @ VPV2009a |
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1171 |
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Author |
Graham D. Finlayson; Javier Vazquez; Fufu Fang |
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Title |
The Discrete Cosine Maximum Ignorance Assumption |
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Conference Article |
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2021 |
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29th Color and Imaging Conference |
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13-18 |
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the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between
wavelengths (many of the assumed reflectances are, in reality, not possible).
In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where
all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely.
Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets.
Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set. |
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Virtual; November 2021 |
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Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
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Quality Improvement of Endoscopy Videos |
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2011 |
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Proceedings of the 8th IASTED International Conference on Biomedical Engineering |
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fernando @ fernando @ |
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Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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LIUM-CVC Submissions for WMT17 Multimodal Translation Task |
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2017 |
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2nd Conference on Machine Translation |
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This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. |
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LAMP; 600.106; 600.120 |
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Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
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Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits |
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2019 |
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International Journal of Social Science and Humanity |
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6-12 |
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Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones. |
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ISE; 600.119 |
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Admin @ si @ RGG2019 |
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