title: Direct curvature scale space: theory and corner detection.
abstract: The Curvature Scale Space (CSS) technique is considered to be a modern tool in image processing and computer vision. Direct Curvature Scale Space (DCSS) is defined as the CSS that results from convolving the curvature of a planar curve with a Gaussian kernel directly. In this paper we present a theoretical analysis of DCSS in detecting corners on planar curves. The scale space behavior of isolated single and double corner models is investigated and a number of model properties are specified which enable us to transform a DCSS image into a tree organization and, so that corners can be detected in a multiscale sense. To overcome the sensitivity of DCSS to noise, a hybrid strategy to apply CSS and DCSS is suggested.
authors.affiliation: Department of Mathematics, Nanjing University of Aeronautics & astronautics, Nanjing, China. zhbj@nuaa.edu.cn
authors.forename: Baojiang
authors.initials: B
authors.lastname: Zhong
authors.forename: Wenhe
authors.initials: W
authors.lastname: Liao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*