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| Author | Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca | ||||
| Title | Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits | Type | Journal | ||
| Year | 2019 | Publication | International Journal of Social Science and Humanity | Abbreviated Journal  |
IJSSH |
| Volume | 9 | Issue | 1 | Pages | 6-12 |
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| Abstract | 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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| Notes | ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RGG2019 | Serial | 3414 | ||
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| Author | Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin | ||||
| Title | Mathematics learning opportunities when playing a Tower Defense Game | Type | Journal | ||
| Year | 2015 | Publication | International Journal of Serious Games | Abbreviated Journal |
IJSG |
| Volume | 2 | Issue | 4 | Pages | 57-71 |
| Keywords | Tower Defense game; learning opportunities; mathematics; problem solving; game design | ||||
| Abstract | A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. | ||||
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| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ HJG2015 | Serial | 2730 | ||
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| Author | Josep Llados; Gemma Sanchez | ||||
| Title | Graph Matching vs. Graph Parsing in Graphics Recognition: A Combined Approach | Type | Journal | ||
| Year | 2004 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal |
IJPRAI |
| Volume | 18 | Issue | 3 | Pages | 455–473 |
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| Notes | DAG; IF: 0.588 | Approved | no | ||
| Call Number | DAG @ dag @ LlS2004 | Serial | 445 | ||
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| Author | Bogdan Raducanu; Jordi Vitria | ||||
| Title | Face Recognition by Artificial Vision Systems: A Cognitive Perspective | Type | Journal | ||
| Year | 2008 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal |
IJPRAI |
| Volume | 22 | Issue | 5 | Pages | 899–913 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RaV2008b | Serial | 1007 | ||
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| Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
| Title | Embedding of Graphs with Discrete Attributes Via Label Frequencies | Type | Journal Article | ||
| Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal |
IJPRAI |
| Volume | 27 | Issue | 3 | Pages | 1360002-1360029 |
| Keywords | Discrete attributed graphs; graph embedding; graph classification | ||||
| Abstract | Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GVB2013 | Serial | 2305 | ||
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| Author | Santiago Segui; Laura Igual; Jordi Vitria | ||||
| Title | Bagged One Class Classifiers in the Presence of Outliers | Type | Journal Article | ||
| Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal |
IJPRAI |
| Volume | 27 | Issue | 5 | Pages | 1350014-1350035 |
| Keywords | One-class Classifier; Ensemble Methods; Bagging and Outliers | ||||
| Abstract | The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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| Notes | OR; 600.046;MV | Approved | no | ||
| Call Number | Admin @ si @ SIV2013 | Serial | 2256 | ||
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| Author | Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta | ||||
| Title | On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal |
IJPRAI |
| Volume | 26 | Issue | 5 | Pages | 1263002-126027 |
| Keywords | Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 | ||||
| Abstract | 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ LRF2012 | Serial | 2128 | ||
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| Author | Mariano Vazquez; Ruth Aris; Guillaume Hozeaux; R.Aubry; P.Villar;Jaume Garcia ; Debora Gil; Francesc Carreras | ||||
| Title | A massively parallel computational electrophysiology model of the heart | Type | Journal Article | ||
| Year | 2011 | Publication | International Journal for Numerical Methods in Biomedical Engineering | Abbreviated Journal |
IJNMBE |
| Volume | 27 | Issue | Pages | 1911-1929 | |
| Keywords | computational electrophysiology; parallelization; finite element methods | ||||
| Abstract | This paper presents a patient-sensitive simulation strategy capable of using the most efficient way the high-performance computational resources. The proposed strategy directly involves three different players: Computational Mechanics Scientists (CMS), Image Processing Scientists and Cardiologists, each one mastering its own expertise area within the project. This paper describes the general integrative scheme but focusing on the CMS side presents a massively parallel implementation of computational electrophysiology applied to cardiac tissue simulation. The paper covers different angles of the computational problem: equations, numerical issues, the algorithm and parallel implementation. The proposed methodology is illustrated with numerical simulations testing all the different possibilities, ranging from small domains up to very large ones. A key issue is the almost ideal scalability not only for large and complex problems but also for medium-size meshes. The explicit formulation is particularly well suited for solving this highly transient problems, with very short time-scale. | ||||
| Address | Swansea (UK) | ||||
| Corporate Author | John Wiley & Sons, Ltd. | Thesis | |||
| Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | ||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ VAH2011 | Serial | 1198 | ||
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| Author | Qingshan Chen; Zhenzhen Quan; Yifan Hu; Yujun Li; Zhi Liu; Mikhail Mozerov | ||||
| Title | MSIF: multi-spectrum image fusion method for cross-modality person re-identification | Type | Journal Article | ||
| Year | 2023 | Publication | International Journal of Machine Learning and Cybernetics | Abbreviated Journal |
IJMLC |
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| Abstract | Sketch-RGB cross-modality person re-identification (ReID) is a challenging task that aims to match a sketch portrait drawn by a professional artist with a full-body photo taken by surveillance equipment to deal with situations where the monitoring equipment is damaged at the accident scene. However, sketch portraits only provide highly abstract frontal body contour information and lack other important features such as color, pose, behavior, etc. The difference in saliency between the two modalities brings new challenges to cross-modality person ReID. To overcome this problem, this paper proposes a novel dual-stream model for cross-modality person ReID, which is able to mine modality-invariant features to reduce the discrepancy between sketch and camera images end-to-end. More specifically, we propose a multi-spectrum image fusion (MSIF) method, which aims to exploit the image appearance changes brought by multiple spectrums and guide the network to mine modality-invariant commonalities during training. It only processes the spectrum of the input images without adding additional calculations and model complexity, which can be easily integrated into other models. Moreover, we introduce a joint structure via a generalized mean pooling (GMP) layer and a self-attention (SA) mechanism to balance background and texture information and obtain the regional features with a large amount of information in the image. To further shrink the intra-class distance, a weighted regularized triplet (WRT) loss is developed without introducing additional hyperparameters. The model was first evaluated on the PKU Sketch ReID dataset, and extensive experimental results show that the Rank-1/mAP accuracy of our method is 87.00%/91.12%, reaching the current state-of-the-art performance. To further validate the effectiveness of our approach in handling cross-modality person ReID, we conducted experiments on two commonly used IR-RGB datasets (SYSU-MM01 and RegDB). The obtained results show that our method achieves competitive performance. These results confirm the ability of our method to effectively process images from different modalities. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ CQH2023 | Serial | 3885 | ||
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| Author | Carme Julia; Felipe Lumbreras; Angel Sappa | ||||
| Title | A Factorization-based Approach to Photometric Stereo | Type | Journal Article | ||
| Year | 2011 | Publication | International Journal of Imaging Systems and Technology | Abbreviated Journal |
IJIST |
| Volume | 21 | Issue | 1 | Pages | 115-119 |
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| Abstract | This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ JLS2011; ADAS @ adas @ | Serial | 1711 | ||
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| Author | Khalid El Asnaoui; Petia Radeva | ||||
| Title | Automatically Assess Day Similarity Using Visual Lifelogs | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Intelligent Systems | Abbreviated Journal |
IJIS |
| Volume | 29 | Issue | Pages | 298–310 | |
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| Abstract | Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | AsR2020 | Serial | 3409 | ||
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| Author | Oriol Pujol; Petia Radeva | ||||
| Title | Texture Segmentation by Statistical Deformable Models | Type | Journal | ||
| Year | 2004 | Publication | International Journal of Image and Graphics | Abbreviated Journal |
IJIG |
| Volume | 4 | Issue | 3 | Pages | 433-452 |
| Keywords | Texture segmentation, parametric active contours, statistic snakes | ||||
| Abstract | Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model. | ||||
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PuR2004a | Serial | 505 | ||
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| Author | Maria Elena Meza de Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas | ||||
| Title | A Social-Aware Assistant to support individuals with visual impairments during social interaction: A systematic requirements analysis | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Human-Computer Studies | Abbreviated Journal |
IJHC |
| Volume | 122 | Issue | Pages | 50-60 | |
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| Abstract | Visual impairment affects the normal course of activities in everyday life including mobility, education, employment, and social interaction. Most of the existing technical solutions devoted to empowering the visually impaired people are in the areas of navigation (obstacle avoidance), access to printed information and object recognition. Less effort has been dedicated so far in developing solutions to support social interactions. In this paper, we introduce a Social-Aware Assistant (SAA) that provides visually impaired people with cues to enhance their face-to-face conversations. The system consists of a perceptive component (represented by smartglasses with an embedded video camera) and a feedback component (represented by a haptic belt). When the vision system detects a head nodding, the belt vibrates, thus suggesting the user to replicate (mirror) the gesture. In our experiments, sighted persons interacted with blind people wearing the SAA. We instructed the former to mirror the noddings according to the vibratory signal, while the latter interacted naturally. After the face-to-face conversation, the participants had an interview to express their experience regarding the use of this new technological assistant. With the data collected during the experiment, we have assessed quantitatively and qualitatively the device usefulness and user satisfaction. | ||||
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| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MTR2019 | Serial | 3142 | ||
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| Author | Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li | ||||
| Title | Enhanced Asymmetric Bilinear Model for Face Recognition | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Distributed Sensor Networks | Abbreviated Journal |
IJDSN |
| Volume | Issue | Pages | Article ID 218514 | ||
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| Abstract | Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. | ||||
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| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GZG2015 | Serial | 2592 | ||
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| Author | Josep Llados; Dorothea Blostein | ||||
| Title | Special Issue on Graphics Recognition | Type | Journal | ||
| Year | 2007 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal |
IJDAR |
| Volume | 9 | Issue | 1 | Pages | 1–2 |
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| Publisher | Guest Editors | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ LlB2007 | Serial | 781 | ||
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