21-04-2014, 02:19 PM
EDGE DETECTION
EDGE DETECTION.ppt (Size: 1.26 MB / Downloads: 27)
INTRODUCTION
Edge - Area of significant change in the image intensity / contrast
Edge Detection – Locating areas with strong intensity contrasts
Use of Edge Detection – Extracting information about the image. E.g. location of objects present in the image, their shape, size, image sharpening and enhancement
Steps in Edge Detection
Filtering – Filter image to improve performance of the Edge Detector wrt noise
Enhancement – Emphasize pixels having significant change in local intensity
Detection – Identify edges - thresholding
Localization – Locate the edge accurately, estimate edge orientation
METHODS OF EDGE DETECTION
First Order Derivative / Gradient Methods
Roberts Operator
Sobel Operator
Prewitt Operator
Second Order Derivative
Laplacian
Laplacian of Gaussian
Difference of Gaussian
Optimal Edge Detection
Canny Edge Detection
First Order Derivative Methods - Summary
Noise – simple edge detectors are affected by noise – filters can be used to reduce noise
Edge Thickness – Edge is several pixels wide for Sobel operator– edge is not localized properly
Roberts operator is very sensitive to noise
Sobel operator goes for averaging and emphasizes on the pixel closer to the center of the mask. It is less affected by noise and is one of the most popular Edge Detectors.
Optimal Edge Detector
Optimal edge detector depending on
Low error rate – edges should not be missed and there must not be spurious responses
Localization – distance between points marked by the detector and the actual center of the edge should be minimum
Response – Only one response to a single edge
One dimensional formulation
Assume that 2D images have constant cross section in some direction