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The biggest challenge of texture filtering is to smooth the strong gradient textures while maintaining the weak structures, which is difficult to achieve with current methods. Based on this, we propose a scale-adaptive texture filtering algorithm in this paper. First, the four-directional detection with gradient information is proposed for structure measurement. Second, the spatial kernel scale for each pixel is obtained based on the structure information; the larger spatial kernel is for pixels in textural regions to enhance the smoothness, while the smaller spatial kernel is for pixels on structures to maintain the edges. Finally, we adopt the Fourier approximation of range kernel, which reduces computational complexity without compromising the filtering visual quality. By subjective and objective analysis, our method outperforms the previous methods in eliminating the textures while preserving main structures and also has advantages in structure similarity and visual perception quality.
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http://dx.doi.org/10.3389/fnbot.2022.729924 | DOI Listing |
Scientifica (Cairo)
August 2025
Department of Biology, School of Bioscience and Technology, College of Natural Sciences, Wollo University, Dessie, Ethiopia.
The gelada (), Ethiopia's only endemic primate and the last surviving graminivorous cercopithecid, was studied in Susgen Natural Forest, South Wollo, to examine seasonal variations in activity budgets and ranging ecology. From February to August 2023, encompassing both dry and wet seasons, 3519 behavioral scans were collected from 1680 group observations using instantaneous scan sampling at 15-min intervals (07:00-17:00 h). Data were analyzed with descriptive statistics and nonparametric tests (Kruskal-Wallis and Mann-Whitney ), while home ranges were mapped via minimum convex polygon (MCP) and kernel density estimation (KDE).
View Article and Find Full Text PDFBiometrika
December 2024
Department of Biostatistics, Johns Hopkins University, 605 N Wolfe Street, Baltimore, Maryland 21215, U.S.A.
This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized least squares estimator using a working covariance matrix from a Matérn or squared exponential kernel, in the presence of spatial confounding.
View Article and Find Full Text PDFNat Commun
September 2025
Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient. Neuromorphic disciplines view the brain as a coevolved system, simultaneously optimizing the hardware and the algorithms running on it. There are clear efficiency gains when bringing the computations into a physical substrate, but we presently lack theories to guide efficient implementations.
View Article and Find Full Text PDFEnviron Pollut
September 2025
Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China; School of Public Health, Southern Medical University, Guangzhou, 510515, China. Electronic address:
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants that are widely detected in human serum worldwide, and are associated with reduced vaccine-induced antibody responses. However, existing research has primarily focused on the effects of prenatal and adolescent PFAS exposures on antibody levels or disease incidence. A critical gap remains in understanding the association between serum PFAS concentrations and antibody levels in children.
View Article and Find Full Text PDFIET Syst Biol
September 2025
School of Computer and Information Techonology, Xinyang Normal University, Xinyang, China.
Accurate polyp segmentation is crucial for computer-aided diagnosis and early detection of colorectal cancer. Whereas feature pyramid network (FPN) and its variants are widely used in polyp segmentation, inherent limitations existing in FPN include: (1) repeated upsampling degrades fine details, reducing small polyp segmentation accuracy and (2) naive feature fusion (e.g.
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