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Browsing Centres by Author "243"
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    BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction
    ( 2018) Alba Castro,JL ; Pedro Miguel Carvalho ; Martins,I ; Luís Corte Real ; 4358 ; 243
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    Boosting color similarity decisions using the CIEDE2000_PF Metric
    ( 2022) Américo José Pereira ; Pedro Miguel Carvalho ; Luís Corte Real ; 243 ; 4358 ; 6078
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    Efficient CIEDE2000-based Color Similarity Decision for Computer Vision
    ( 2019) Luís Corte Real ; Américo José Pereira ; Pedro Miguel Carvalho ; Coelho,G ; 6078 ; 243 ; 4358
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    Efficient CIEDE2000-Based Color Similarity Decision for Computer Vision
    ( 2020) Américo José Pereira ; Pedro Miguel Carvalho ; Luís Corte Real ; 6078 ; 4358 ; 243
    Color and color differences are critical aspects in many image processing and computer vision applications. A paradigmatic example is object segmentation, where color distances can greatly influence the performance of the algorithms. Metrics for color difference have been proposed in the literature, including the definition of standards such as CIEDE2000, which quantifies the change in visual perception of two given colors. This standard has been recommended for industrial computer vision applications, but the benefits of its application have been impaired by the complexity of the formula. This paper proposes a new strategy that improves the usability of the CIEDE2000 metric when a maximum acceptable distance can be imposed. We argue that, for applications where a maximum value, above which colors are considered to be different, can be established, then it is possible to reduce the amount of calculations of the metric, by preemptively analyzing the color features. This methodology encompasses the benefits of the metric while overcoming its computational limitations, thus broadening the range of applications of CIEDE2000 in both the computer vision algorithms and computational resource requirements.
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    Face Detection in Thermal Images with YOLOv3
    ( 2019) Silva,G ; Monteiro,R ; Ferreira,A ; Pedro Miguel Carvalho ; Luís Corte Real ; 4358 ; 243
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    Stereo vision system for human motion analysis in a rehabilitation context
    ( 2019) Matos,AC ; Teresa Cristina Terroso ; Luís Corte Real ; Pedro Miguel Carvalho ; 6217 ; 4358 ; 243
    The present demographic trends point to an increase in aged population and chronic diseases which symptoms can be alleviated through rehabilitation. The applicability of passive 3D reconstruction for motion tracking in a rehabilitation context was explored using a stereo camera. The camera was used to acquire depth and color information from which the 3D position of predefined joints was recovered based on: kinematic relationships, anthropometrically feasible lengths and temporal consistency. Finally, a set of quantitative measures were extracted to evaluate the performed rehabilitation exercises. Validation study using data provided by a marker based as ground-truth revealed that our proposal achieved errors within the range of state-of-the-art active markerless systems and visual evaluations done by physical therapists. The obtained results are promising and demonstrate that the developed methodology allows the analysis of human motion for a rehabilitation purpose. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
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    Texture collinearity foreground segmentation for night videos
    ( 2020) Martins,I ; Pedro Miguel Carvalho ; Luís Corte Real ; Luis Alba Castro,JL ; 243 ; 4358
    One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an N-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail. © 2020 Elsevier Inc.