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 |
Front Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
View Article and Find Full Text PDFPLoS One
January 2025
College of Tourism, Hubei University, Wuhan, Hubei, China.
The study analyzed the spatial distribution characteristics, evolution rules, and driving factors of 138 China's national agricultural cultural heritage sites from 2013 to 2021 at the overall and regional levels, using kernel density analysis, Centres for standard deviation ellipse analyses, spatial autocorrelation analysis, and geographical detector analysis.The results showed that: ①From an overall perspective, the spatial pattern of China's national agricultural cultural heritage changed greatly from 2013 to 2021, with a highly uneven spatial distribution, gradually showing a distribution pattern of "widely distributed, locally concentrated". The spatial distribution of China's national agricultural cultural heritage is increasingly evident, and the spatial distribution type has evolved from discrete to clustered.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
College of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China.
During the rice harvesting process, severe occlusion and adhesion exist among multiple targets, such as rice, straw, and leaves, making it difficult to accurately distinguish between rice grains and impurities. To address the current challenges, a lightweight semantic segmentation algorithm for impurities based on an improved SegFormer network is proposed. To make full use of the extracted features, the decoder was redesigned.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Department of Physical Education and Research, Central South University, Changsha 410083, China.
This study estimates regional economic resilience in China from 2000 to 2022, focusing on economic resistance resilience, recovery resilience, and reorientation resilience. The entropy method, kernel density estimation, and spatial Durbin model are applied to examine the spatiotemporal evolution and influencing factors. The results show significant spatial clustering, with stronger resilience in the east and weaker resilience in the west.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.
With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states.
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