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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 (up) International Journal of Human-Computer Studies Abbreviated Journal IJHC
Volume 122 Issue Pages 50-60
Keywords
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 Oriol Pujol; Petia Radeva
Title Texture Segmentation by Statistical Deformable Models Type Journal
Year 2004 Publication (up) 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 Oriol Pujol; Petia Radeva
Title Texture Segmentation by Statistic Deformable Models Type Journal
Year 2003 Publication (up) International Journal of Image and Graphics (IJIG) Abbreviated Journal
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Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ PuR2003 Serial 432
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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 (up) International Journal of Imaging Systems and Technology Abbreviated Journal IJIST
Volume 21 Issue 1 Pages 115-119
Keywords
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 Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization Type Journal Article
Year 2021 Publication (up) International Journal of Imaging Systems and Technology Abbreviated Journal IMA
Volume 31 Issue 4 Pages 2170-2188
Keywords
Abstract Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ DGR2021a Serial 3630
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Author J. Nuñez; Xavier Otazu; M.T. Merino
Title A Multiresolution-Based Method for the Determination of the Relative Resolution between Images. First Application to Remote Sensing and Medical Images Type Journal
Year 2005 Publication (up) International Journal of Imaging Systems and Technology, 15(5): 225–235 (IF: 0.439) Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ NOM2005 Serial 645
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Author Angel Sappa
Title Splitting up Panoramic Range Images into Compact 2½D Representations Type Journal
Year 2006 Publication (up) International Journal of Imaging Systems and Technology, 16(3): 85–91 Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ Sap2006b Serial 721
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Author Khalid El Asnaoui; Petia Radeva
Title Automatically Assess Day Similarity Using Visual Lifelogs Type Journal Article
Year 2020 Publication (up) International Journal of Intelligent Systems Abbreviated Journal IJIS
Volume 29 Issue Pages 298–310
Keywords
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 David Masip; Jordi Vitria
Title Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition Type Journal
Year 2005 Publication (up) International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657) Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2005 Serial 562
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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 (up) International Journal of Machine Learning and Cybernetics Abbreviated Journal IJMLC
Volume Issue Pages
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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 A. Pujol; Juan J. Villanueva
Title A supervised Modification of the Hausdorff distance for visual shape classification Type Journal
Year 2002 Publication (up) International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal
Volume 16 Issue 3 Pages 349-359
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Abstract (IF: 0.359)
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Notes ISE Approved no
Call Number PuV2002 Serial 273
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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 (up) 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 (up) 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 (up) 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 (up) 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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