SHEN_CASTAN_ED_CVIP

shen_castan_ed_cvip() - performs Shen- Casten edge detection.

SYNTAX
DESCRIPTION
REFERENCE
EXAMPLE
CREDITS

SYNTAX

[ after_hysteresis, edge_mag_suppressed,  before_suppression] = shen_castan_ed_cvip( input_image, a00 , low_thresh, high_thresh)

Input Parameters include:

input_image - Input image can be gray image or rgb image of MxN size.

a00 - Smoothing factor. A value in the interval (0,1).

low_thresh - The low threshold value for hystersis thresholding. A number in the range 0-10.

high_thresh - The high threshold value for hystersis thresholding. A number in the range 0-10.

Output parameters include:

after_hysteresis - The resultant image after hysteresis thresholding.

edge_mag_supressed - The resultant image with the edge magnitude supressed.

before_supression - The resultant image before the supression of the edge magnitude.

DESCRIPTION

This function performs the Shen-Casten edge detection operation on an input image. The Shen-Casten algorithm is similar to that of Canny's algorithm. The algorithm uses an optimum filter called infinite symmetric exponential filter to find the precise locations of the edge pixels.

REFERENCE

1. Scott E Umbaugh. DIGITAL IMAGE PROCESSING AND ANALYSIS: Applications with MATLAB and CVIPtools, 3rd Edition.

EXAMPLE

% Read image

 input = imread('butterfly.tif');

% Smoothing factor

  a00 = .9;

% Low threshold

  low_thresh = 1;

% High threshold

  high_thresh = 4;

% Calling function

 [ after_hysteresis, edge_mag_suppressed,  before_suppression] = ...
      shen_castan_ed_cvip( input, a00  , low_thresh, high_thresh);

% Display input image

 figure;imshow(input);title('Input image');

% Display output image

 figure; imshow(hist_stretch_cvip(before_suppression,0,1,0,0));title('Output image before supression');

 figure; imshow(hist_stretch_cvip(edge_mag_suppressed,0,1,0,0));title('Output edge magnitude supressed');

 figure; imshow(after_hysteresis,[]);title('Output image after hysteresis');

CREDITS