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Author Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov
Title An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification Type Journal Article
Year 2023 Publication IEEE Sensors Journal Abbreviated Journal IEEE-SENS
Volume 23 Issue (up) 24 Pages
Keywords Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov
Abstract Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.
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Notes LAMP Approved no
Call Number Admin @ si @ CQL2023 Serial 3884
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Author T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger
Title The achromatic locus: Effect of navigation direction in color space Type Journal Article
Year 2014 Publication Journal of Vision Abbreviated Journal VSS
Volume 14 (1) Issue (up) 25 Pages 1-11
Keywords achromatic; unique hues; color constancy; luminance; color space
Abstract 5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes.
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Notes DAG; 600.077 Approved no
Call Number Admin @ si @ CPX2014 Serial 2418
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Author Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez
Title A Closer Look at Embedding Propagation for Manifold Smoothing Type Journal Article
Year 2022 Publication Journal of Machine Learning Research Abbreviated Journal JMLR
Volume 23 Issue (up) 252 Pages 1-27
Keywords Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification
Abstract Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and
self-supervised learning performance.
Address 9/2022
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Notes Approved no
Call Number Admin @ si @ VRG2022 Serial 3762
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Author Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis
Title Advances in Tracking and Recognition of Human Motion Type Book Chapter
Year 2009 Publication Encyclopedia of Information Science and Technology Abbreviated Journal
Volume I Issue (up) 2nd edition Pages 65–71
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Notes ADAS Approved no
Call Number ADAS @ adas @ ASG2009 Serial 1143
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Author J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat
Title Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos. Type Journal
Year 1993 Publication Optica Pura y Aplicada. Abbreviated Journal
Volume 26 Issue (up) 3 Pages 685–691
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Notes ADAS Approved no
Call Number ADAS @ adas @ PYC1993 Serial 149
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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 International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal
Volume 16 Issue (up) 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 International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 18 Issue (up) 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 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 (up) 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 Antonio Lopez; Ernest Valveny; Juan J. Villanueva
Title Real-time quality control of surgical material packaging by artificial vision Type Journal Article
Year 2005 Publication Assembly Automation Abbreviated Journal
Volume 25 Issue (up) 3 Pages
Keywords
Abstract IF: 0.061)
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Notes ADAS;DAG Approved no
Call Number ADAS @ adas @ LVV2005 Serial 552
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Author A. Sanfeliu; Juan J. Villanueva
Title An approach of visual motion analysis Type Journal Article
Year 2005 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 26 Issue (up) 3 Pages 355–368
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Abstract IF: 1.138
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Notes Approved no
Call Number ISE @ ise @ SaV2005 Serial 561
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Author Jaume Amores; N. Sebe; Petia Radeva
Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue (up) 3 Pages 201–209
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Notes ADAS;MILAB Approved no
Call Number ADAS @ adas @ ASR2006 Serial 643
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Author V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich
Title Adaptive Correlation Filters for Pattern Recognition Type Journal
Year 2006 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume 16 Issue (up) 3 Pages 425-431
Keywords Pattern recognition, Correlation filters, A adaptive filters
Abstract Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
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Notes ISE Approved no
Call Number ISE @ ise @ KMA2006a Serial 673
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Author A. Diplaros; N. Vlassis; Theo Gevers
Title A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation Type Journal
Year 2007 Publication IEEE Transactions on Neural Networks Abbreviated Journal
Volume 18 Issue (up) 3 Pages 798-808
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Notes ISE Approved no
Call Number Admin @ si @ DVG2007 Serial 947
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Author Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez
Title An Efficient Approach to Onboard Stereo Vision System Pose Estimation Type Journal Article
Year 2008 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 9 Issue (up) 3 Pages 476–490
Keywords Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system
Abstract This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results.
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Publisher IEEE Place of Publication Editor
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Notes ADAS Approved no
Call Number ADAS @ adas @ SDP2008 Serial 1000
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Author Jaume Garcia; Debora Gil; Sandra Pujades; Francesc Carreras
Title Valoracion de la Funcion del Ventriculo Izquierdo mediante Modelos Regionales Hiperparametricos Type Journal Article
Year 2008 Publication Revista Española de Cardiologia Abbreviated Journal
Volume 61 Issue (up) 3 Pages 79
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Abstract La mayoría de la enfermedades cardiovasculares afectan a las propiedades contráctiles de la banda ventricular helicoidal. Esto se refleja en una variación del comportamiento normal de la función ventricular. Parámetros locales tales como los strains, o la deformación experimentada por el tejido, son indicadores capaces de detectar anomalías funcionales en territorios específicos. A menudo, dichos parámetros son considerados de forma separada. En este trabajo presentamos un marco computacional (el Dominio Paramétrico Normalizado, DPN) que permite integrarlos en hiperparámetros funcionales y estudiar sus rangos de normalidad. Dichos rangos permiten valorar de forma objetiva la función regional de cualquier nuevo paciente. Para ello, consideramos secuencias de resonancia magnética etiquetada a nivel basal, medio y apical. Los hiperparámetros se obtienen a partir del movimiento intramural del VI estimado mediante el método Harmonic Phase Flow. El DPN se define a partir de en una parametrización del Ventrículo Izquierdo (VI) en sus coordenadas radiales y circunferencial basada en criterios anatómicos. El paso de los hiperparámetros al DPN hace posible la comparación entre distintos pacientes. Los rangos de normalidad se definen mediante análisis estadístico de valores de voluntarios sanos en 45 regiones del DPN a lo largo de 9 fases sistólicas. Se ha usado un conjunto de 19 (14 H; E: 30.7±7.5) voluntarios sanos para crear los patrones de normalidad y se han validado usando 2 controles sanos y 3 pacientes afectados de contractilidad global reducida. Para los controles los resultados regionales se han ajustado dentro de la normalidad, mientras que para los pacientes se han obtenido valores anormales en las zonas descritas, localizando y cuantificando así el diagnóstico empírico.
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Notes IAM; Approved no
Call Number IAM @ iam @ GRP2008 Serial 1032
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