Showing papers on "Edge detection published in 1975"
TL;DR: Methods of detecting “edges,” i.e., boundaries between regions in a picture, are reviewed and both parallel and sequential methods are reviewed.
849 citations
TL;DR: In order to speed up several picture processing operations, including edge detection, a “pyramid” (hierarchy of fine to coarse resolution versions of a picture) is produced because of the low spatial frequencies preserved in coarse pictures.
483 citations
TL;DR: A technique for the quantitative evaluation of edge detection schemes is used to assess the performance of three such schemes using a specially-generated set of images containing noise to relate the quantitative comparison to real-life imagery.
Abstract: A technique for the quantitative evaluation of edge detection schemes is presented. It is used to assess the performance of three such schemes using a specially-generated set of images containing noise. The ability of human subjects to distinguish the edges in the presence of noise is also measured and compared with that of the edge detection schemes. The edge detection schemes are used on a high-resolution satellite photograph with varying degrees of noise added in order to relate the quantitative comparison to real-life imagery.
177 citations
01 Jun 1975
176 citations
TL;DR: A nonlinear method of estimating pictorial data from noisy observations is considered and an edge detection technique using likelihood functions is used to decide if an incoming picture element is a member of the object or the background.
Abstract: A nonlinear method of estimating pictorial data from noisy observations is considered. The data is assumed to be composed of an object with one texture in the background. An edge detection technique using likelihood functions is used to decide if an incoming picture element is a member of the object or the background. Then, the picture element is directed to one of the two Kalman filters designed based upon the statistics of the object or the background accordingly.
49 citations
TL;DR: In this article, a procedure for extracting edge information from real photographs of both flat-faced and curved three-dimensional objects is described, which is based on edge detection operator by Hueckel.
Abstract: A procedure for extracting edge information from real photographs of both flat-faced and curved three-dimensional objects is described. Initially the photograph is subjected to a nonlinear transformation which is based on an edge detection operator by Hueckel. The gray-value variations of a photograph are transformed into short vectors (strokes), which are arranged in a two-dimensional array that has substantially fewer entries than the original image array. The display of a stroke array provides "outline" information that is highly intelligible to the human eye. Using graph searching techniques, strokes are assembled into structures called streaks. Streaks are digital curves which exhibit cellular as well as polygonal properties. The representation of streaks as well as other data can be marked directly in the stroke array, thus making the array usable as an associative memory. This greatly facilitates further processing.
25 citations
TL;DR: In this paper, a computationally simple iterative edge detection procedure is described and illustrated, where one-pixel edge thickness is mostly achieved in approximately four iterations with a 5 × 5 local operator neighborhood.
23 citations
TL;DR: A new algorithm for the detection of edges in grey level pictures is presented, based on the amount of clustering of the grey levels in a square neighborhood, which is first developed for binary regions and then extended to include grey level regions.
18 citations
TL;DR: A method of detecting step edges in noisy one-dimensional input data is described, which remains reliable when edges occur close to one another.
Abstract: A method of detecting step edges in noisy one-dimensional input data is described. The method involves examination of differences in average gray level over ranges of positions and sizes. Unlike previously described methods, it remains reliable when edges occur close to one another.
11 citations
TL;DR: This work is part of a larger project the goal of which is to model and quantify the three dimensional geometrical characteristics of normal and pathological spinal columns as well as to theoretically analyze the mechanics of the diseased spine.
8 citations
TL;DR: In this paper, the authors point out a possible advantage of using combinations of gradient and Laplacian operators for edge detection, and propose a method to detect edges in pictures.
TL;DR: In this paper, a new technique was developed to perform automatic terrain classification based on edge detection of terrain regions characterized by spatial signatures, where boundary maps were developed by derivative operations on digitized photographic imagery.