Edge Detection Techniques-An Overview

By Djemel Ziou, S. Tabbone. 1998

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Fast Edge Detection Using Structured Forests (2014, background): “F”
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection (2014, methodology): “e supervised SVM learning [5], gPb relies on spectral graph theory. Other spectral-based methods include normalized cuts [26] and PMI [12]. A detailed survey of edge detection methods can be found in [4,30]. These prior approaches share a bottom-up architecture where low-level features based on text”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “For texture features the pixel values of the variance filtered green channel [63], and the pixel values of the gradient magnitude filtered green channel are used.”
Fractional differentiation for edge detection (2003, methodology): “In image processing, edge detection often makes use ofinteger-order di�erentiation operators, especially order 1 used by the gradient and order 2 by the Laplacian [2,4,5,10,13,16,25,26,28, 31 ].”
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection (2019, methodology): “These models are capable of predicting an edge-map from a given image just like the low level based methods [42], with better performance.”
Merging chrominance and luminance in early, medium, and late fusion using Convolutional Neural Networks (2015, background): “For an extended overview about edge detectors see [Ziou and Tabbone, 1998].”
Structured Forests for Fast Edge Detection (2013, methodology): “Traditional approaches to edge detection use a variety of methods for computing color gradient magnitudes followed by non-maximal suppression [5, 14, 38].”
Image registration methods: a survey (2003, methodology): “The survey of existing edge detection method together with their evaluation can be found in [222].”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, unknown): “Although the IPTs-based damage detection method is eﬀective and fast, it can detect only one damage type and its robustness left much to be desired when noises, primarily from lighting and distortion, substantially aﬀected the results (Koziarski & Cyganek, 2017; Ziou & Tabbone, 1998).”
Edge and line oriented contour detection: State of the art (2011, background): “We make distinction between the following classes of contour detectors (i) region-oriented approaches [3,4], in which regions of constant or slowly varying texture or color are first identified; contours are then straightforwardly determined as closed region boundaries; (ii) edge and line oriented a”
A Shearlet Approach to Edge Analysis and Detection (2009, unknown): “Several concepts, such as those in [15] and [36], could be further explored and adapted for their use with”
Enhancing multiple‐point geostatistical modeling: 1. Graph theory and pattern adjustment (2016, methodology): “For this aim, one utilizes one of the available edge detection techniques [Canny, 1986; Ziou and Tabbone, 1998; Umbaugh, 2010] to organize the patterns as a set of curved lines.”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, methodology): “For texture features the pixel values of the variance ﬁltered green channel [63], and the pixel values of the gradient magnitude ﬁltered green channel are used.”