This paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques.
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http://dx.doi.org/10.1109/TPAMI.2015.2408361 | DOI Listing |
Eco Environ Health
December 2024
MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
Plastic pollution is widely present in terrestrial and aquatic ecosystems, and microplastics (MPs) can be detected in organisms. detection methods for MPs in organisms have attracted widespread attention. Traditional imaging characterization methods of MPs, including stereo microscopes and fluorescence microscopy, are typically used to image artificially added microsphere standards under laboratory conditions.
View Article and Find Full Text PDFBioinformatics
June 2024
Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
Summary: Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions.
View Article and Find Full Text PDFJ Opt Soc Am A Opt Image Sci Vis
October 2023
To address problems such as the lack of accuracy in acquiring depth maps for dynamic fish 3D measurements by usual binocular vision or a time-of-flight (TOF) depth camera, a TOF-assisted binocular vision depth acquisition algorithm is used to obtain high-quality depth maps. The TOF depth energy function is designed to guide the binocular stereo matching process, which improves the correct matching rate of binocular matching in low-texture regions; the TOF and binocular stereo matching confidence weighting functions are designed to achieve the fusion of the two at pixel level to improve the matching quality of fish in the occluded overlapping regions. The experimental results show that the TOF-assisted binocular vision system improves the accuracy of fish size measurement compared to single binocular vision while reducing the measurement error when the fish body has a significant inclination along the depth axis.
View Article and Find Full Text PDFSensors (Basel)
November 2022
Contents Convergence Research Center, Korea Electronics Technology Institute, Seoul 03924, Korea.
Depth sensing is an important issue in many applications, such as Augmented Reality (AR), eXtended Reality (XR), and Metaverse. For 3D reconstruction, a depth map can be acquired by a stereo camera and a Time-of-Flight (ToF) sensor. We used both sensors complementarily to improve the accuracy of 3D information of the data.
View Article and Find Full Text PDFSensors (Basel)
August 2022
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece.
Biometrics have been used to identify humans since the 19th century. Over time, these biometrics became 3D. The main reason for this was the growing need for more features in the images to create more reliable identification models.
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