07-05-2014, 04:52 PM
CANNY EDGE DETECTION USING MATLAB SIMULATON & FPGA IMPLEMENTATION
CANNY EDGE DETECTION.pptx (Size: 247.74 KB / Downloads: 20)
Introduction To Canny Algorithm
The Canny edge detection operator was developed by John F. Canny in 1986 and uses a multi-stage algorithm to detect a wide range of edges in images. Most importantly, Canny also produced a computational theory of edge detection explaining why the technique works.
Introduction To Edge Detection
Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene.
Classical methods of edge detection involve convolution of the image with a operator, which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions.
Description
1.Calculating the horizontal gradient and vertical gradient at each pixel location by convolving the image with partial derivatives of a 2D Gaussian function.
2.smoothing the input image by Gaussian mask. The output smoothed the image.
3. Computing the gradient magnitude and direction at each pixel location.
4. Applying non-maximum suppression (NMS) to thin edge.
5. Computing the hysteresis high and low thresholds based on the histogram of the magnitudes of the gradients of the entire image.
6.Performing hysteresis thresholding to determine the edge map.
7. The low pass filtering is achieved by taking the average pixel values.
DESCRIPTION
1.Noise reduction or Smoothing- The first step is to filter out any noise in the original image before trying to locate and detect any edges. And because the Gaussian filter can be computed using a simple mask, it is used exclusively in the Canny algorithm. Once a suitable mask has been calculated, the Gaussian smoothing can be performed using standard convolution methods. A convolution mask is usually much smaller than the actual image. As a result, the mask is slid over the image, manipulating a square of pixels at a time. The larger the width of the Gaussian mask, the lower is the detector's sensitivity to noise. The localization error in the detected edges also increases slightly as the Gaussian width is increased. The Canny edge detector uses a filter based on the first derivative of a Gaussian, because it is susceptible to noise present on raw unprocessed image data, so to begin with, the raw image is convolved with a Gaussian filter. The result is a slightly blurred version of the original which is not affected by a single noisy pixel to any significant degree.
Other Applications of Canny Detector:
•As a conclusion, wewillusuallyuse thecannyedgedetector in someofthesefields, whichare highlyinterrelated:
-Computervision
-Digital imageprocessing
-Featureextraction
-Edgedetection
-Scalespace