Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Edge Detector Design II: Coefficient Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Approaches to Image Understanding
ACM Computing Surveys (CSUR)
Finding Edges and Lines in Images
Finding Edges and Lines in Images
Directional Analysis of Images in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving edge detection by an objective edge evaluation
SAC '92 Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model
Journal of Mathematical Imaging and Vision
Bar Code Waveform Recognition Using Peak Locations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality in image analysis
Journal of Visual Communication and Image Representation
Multi-edge detection by isotropical 2-D ISEF cascade
Pattern Recognition
Unbiased extraction of lines with parabolic and Gaussian profiles
Computer Vision and Image Understanding
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A method to detect, locate, and estimate edges in a one-dimensional signal is presented. It is inherently more accurate than all previous schemes as it explicitly models and corrects interaction between nearby edges. The method is iterative with initial estimation of edges provided by the zero crossings of the signal convolved with Laplacian of Gaussian (LoG) filter. The necessary computations necessitate knowledge of this convolved output only in a neighborhood around each zero crossing and in most cases, could be performed locally by independent parallel processors. Results on one-dimensional slices extracted from real images, and on images which have been proposed independently in the row and column directions are shown. An analysis of the method is provided including issues of complexity and convergence, and directions of future research are outlined.