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The curvature scale-space (CSS) technique is suitable for extracting curvature features from objects with noisy boundaries. To detect corner points in a multiscale framework, Rattarangsi and Chin investigated the scale-space behavior of planar-curve corners. Unfortunately, their investigation was based on an incorrect assumption, viz., that planar curves have no shrinkage under evolution. In the present paper, this mistake is corrected. First, it is demonstrated that a planar curve may shrink nonuniformly as it evolves across increasing scales. Then, by taking into account the shrinkage effect of evolved curves, the CSS trajectory maps of various corner models are investigated and their properties are summarized. The scale-space trajectory of a corner may either persist, vanish, merge with a neighboring trajectory, or split into several trajectories. The scale-space trajectories of adjacent corners may attract each other when the corners have the same concavity, or repel each other when the corners have opposite concavities. Finally, we present a standard curvature measure for computing the CSS maps of digital curves, with which it is shown that planar-curve corners have consistent scale-space behavior in the digital case as in the continuous case.
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http://dx.doi.org/10.1109/TPAMI.2008.295 | DOI Listing |
Sci Rep
July 2025
College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
Texture is a crucial visual and sensory attribute in understanding the world. The complexity of imaging environments, variations in acquisition angles and distances, and differences in resolution make representing multi-scale texture features a core challenge in texture analysis. However, most existing multi-scale methods are overly complex and redundant, often neglecting the correlation of texture features across different scales.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
May 2025
Differential equations have demonstrated intrinsic connections to network structures, linking discrete network layers through continuous equations. Most existing approaches focus on the interaction between ordinary differential equations (ODEs) and feature transformations, primarily working on input signals. In this paper, we study the partial differential equation (PDE) model of neural networks, viewing the neural network as a functional operating on a base model provided by the last layer of the classifier.
View Article and Find Full Text PDFAm J Primatol
January 2025
School of Resources and Environmental Engineering, Anhui University, Hefei, Anhui, China.
Many animals face significant challenges in locating and acquiring resources that are unevenly distributed in space and time. In the case of nonhuman primates, it remains unclear how individuals remember goal locations and whether they navigate using a route-based or a coordinate-based mental representation when moving between out-of-sight feeding and resting sites (i.e.
View Article and Find Full Text PDFJ Mammal
October 2024
Department of Biology and Centre d'Études Nordiques, Université Laval, 1045 Avenue de la Médecine, Québec, QC G1V 0A6, Canada.
Space use by small mammals should mirror their immediate needs for food and predator shelters but can also be influenced by seasonal changes in biotic and abiotic factors. Lemmings are keystone species of the tundra food web, but information on their spatial distribution in relation to habitat heterogeneity is still scant, especially at a fine scale. In this study, we used spatially explicit capture-recapture methods to determine how topography, hydrology, vegetation, and soil characteristics influence the fine-scale spatial variations in summer density of brown lemmings ().
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