Sejong Univ. Edge Detection Introduction Simple Edge Detectors First Order Derivative based Edge Detectors Compass Gradient based Edge Detectors Second Order Derivative based Edge Detectors LoG (Laplacian of Gaussian)

Sejong Univ  Importance of edges in image:  Edges provide the basic information on location, shape, and size of objects in images.  Edge business, randomness, density, directionality, etc. are used for texture features.  The simplest descriptor of an image is the edges.  Normally used as the first step for Image Analysis, Segmentation, Registration, Region Adaptive Processing  Kinds of edges: Roof, Line, Step, Ramp Edges Edge Detection : Introduction

Sejong Univ  Homogeneity operator: y(m,n) = max{|x(m,n)-x(m-1,n)|, |x(m,n)-x(m,n-1)|, |x(m,n)-x(m+1,n)|, |x(m,n)-x(m,n+1)|, |x(m,n)-x(m-1,n-1)|, |x(m,n)-x(m-1,n+1)|, |x(m,n)-x(m+1,n-1)|, |x(m,n)-x(m+1,n+1)|}  Difference edge detector: y(m,n) = |x(m,n)-x(m-1,n)| + |x(m,n)-x(m,n-1)| + |x(m,n)-x(m+1,n)| + |x(m,n)-x(m,n+1)|  Normalization or thresholding follows for complete detection Too many edges are detected, p84, 85 Fig. 3.14, 15 Does not provide the information on edge orientations. Simple Edge Detectors

Sejong Univ  Calculate Hr & Hc by masking with Roberts, Prewitt, Sobel, Frei-Chen masks.  SQRT(Hr 2 +Hc 2 ) = edge strength, tan -1 (Hr/Hc) = edge orientation First Order Derivative based Edge Detectors HrHc Roberts Prewitt Sobel Frei-Chen

Sejong Univ  Laplacian operator: edge = zero crossing of the second derivatives => Masking with the following difference mask 2 times & detect zero-crossings Second Order Derivative based Edge Detectors

Sejong Univ  Implement Simple Edge Detector on page 3.  Implement First Order Derivative based Edge Detectors on page 4.  Implement Laplacian Edge Detector on page 5.  By using one of above edge detectors, display horizontal, vertical, diagonal, & off-diagonal edge, respectively.

Sejong Univ  Written Exam for the DIP Theory on Lecture Notes - Closed books  Program Implementation Test - Open books & source codes

Sejong Univ  Second Order Derivative based Edge Detectors are too much sensitive on noisy area.  Very useful concept for the most famous edge detector, Canny’s edge detector. step 1: Remove Noise by using Gaussian filter. LoG (Laplacian of Gaussian)

Sejong Univ step 2: 1-st differentiation. step 3: 2-nd differentiation. LoG (Laplacian of Gaussian)

Sejong Univ step 4: find zero crossing. For (-1)*[step 3]. No change on zero crossing. Then, i.e., LoG (Laplacian of Gaussian)

Sejong Univ dimensional Gaussian filter is & Looks like Mexican Hat (Fig. 3.21, p91). Therefore, LoG filter is also called Mexican Hat filter. Larger results more smoothing & less detected edges. LoG (Laplacian of Gaussian)

Sejong Univ  DoG (Difference of Gaussian): - Proposed as the simpler method resembling LoG. - Implemented by using the following difference of two Gaussian filters with different. Typically, is used.  LoG( =12.35) & DoG( =10, =16) are shown on p92 Fig  Typically, mask sizes of 7X7, 9X9, 35X35 are used for LoG & DoG as shown on p92. LoG (Laplacian of Gaussian)

Sejong Univ LoG (Laplacian of Gaussian)

Sejong Univ  Implement edge detector using 9X9 LoG filter shown on T extbook p92.  Detect your edges by changing. - You need to change your mask. How can you do this? - See the edge detector provided by the lecturer.

Sejong Univ  Find a paper for edge detection written in English, make your own computer program to implement it, and present the results including the paper’s advantages.