Results 231 to 240 of about 303,248 (261)
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Proceedings of the Royal Society of London. Series B. Biological Sciences, 1980
Abstract A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is ...
D, Marr, E, Hildreth
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Abstract A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is ...
D, Marr, E, Hildreth
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Fuzzy Integrals for Edge Detection
2023This work was partially supported with grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by ”ERDF A way of making Europe”, Conseller´ıa d’Innovaci´o, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future), grant from the Reseach Services of ...
C. Marco-Detchart +8 more
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International Journal of Pattern Recognition and Artificial Intelligence, 1998
The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels.
Vitulano, S. +2 more
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The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels.
Vitulano, S. +2 more
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Graphical Models and Image Processing, 1993
Abstract This paper deals with the problem of extracting edge structure from compressed image representations. As image coding techniques become more common in image manipulation systems, it is reasonable to develop methods of analyzing an image using operations on the compressed image representation rather than the reconstructed image.
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Abstract This paper deals with the problem of extracting edge structure from compressed image representations. As image coding techniques become more common in image manipulation systems, it is reasonable to develop methods of analyzing an image using operations on the compressed image representation rather than the reconstructed image.
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Edge Detection by Adaptive Splitting
Journal of Scientific Computing, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bernardo Llanas, Sagrario Lantarón
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Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Agnès Desolneux +2 more
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Improved Codebook Edge Detection
Graphical Models and Image Processing, 1995Abstract A technique for detecting edges directly from a vector-quantized image representation, called codebook edge detection was proposed by Mclean (CVGIP: Graphical Models Image Process. 55, 1993, 48-57). With this method, the edges can be detected by a simple table lookup, a so-called "edge codebook." In this paper, we propose an improved method ...
Jie Zhou 0001 +2 more
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Edge Detection and Edge Images
1995This chapter gives a summary of edge detection methods in gray-level images based on [Bru90]. We also introduce unions and bit-fields in C++.
Dietrich W. R. Paulus, Joachim Hornegger
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Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1990
Abstract In this paper, we have described a new robust edge detection algorithm which performs equally well under a wide variety of noisy situations and a broad range of edges. The algorithm is executed in three phases. In phase 1, the step and linear edges are detected from the noise corrupted image.
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Abstract In this paper, we have described a new robust edge detection algorithm which performs equally well under a wide variety of noisy situations and a broad range of edges. The algorithm is executed in three phases. In phase 1, the step and linear edges are detected from the noise corrupted image.
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Recent advances on image edge detection: A comprehensive review
Neurocomputing, 2022Changming Sun +2 more
exaly