An edge detection technique using local smoothing and statistical hypothesis testing
Pattern Recognition Letters
Computing Joint Distributions of 2D Moving Median Filters With Applications to Detection of Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-Preserving Image Denoising and Estimation of Discontinuous Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting discontinuities in nonparametric regression curves and surfaces
Statistics and Computing
Quick multivariate kernel density estimation for massive data sets: Research Articles
Applied Stochastic Models in Business and Industry - Business, Industry and Government (BIG) Statistics
Detection of linear and circular shapes in image analysis
Computational Statistics & Data Analysis
3D object segmentation using B-Surface
Image and Vision Computing
Estimating and testing zones of abrupt change for spatial data
Statistics and Computing
IEEE Transactions on Image Processing
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A new procedure is proposed to estimate the jump location curve and surface in the two-dimensional (2D) and three-dimensional (3D) nonparametric jump regression models, respectively. In each of the 2D and 3D cases, our estimation procedure is motivated by the fact that, under some regularity conditions, the ridge location of the rotational difference kernel estimate (RDKE; Qiu in Sankhy驴 Ser. A 59, 268---294, 1997, and J. Comput. Graph. Stat. 11, 799---822, 2002; Garlipp and Müller in Sankhy驴 Ser. A 69, 55---86, 2007) obtained from the noisy image is asymptotically close to the jump location of the true image. Accordingly, a computational procedure based on the kernel smoothing method is designed to find the ridge location of RDKE, and the result is taken as the jump location estimate. The sequence relationship among the points comprising our jump location estimate is obtained. Our jump location estimate is produced without the knowledge of the range or shape of jump region. Simulation results demonstrate that the proposed estimation procedure can detect the jump location very well, and thus it is a useful alternative for estimating the jump location in each of the 2D and 3D cases.