A COMBINATORIAL APPROACH FOR EDGE DETCTION

Available Online at www.ijpret.com 361

INTERNATIONAL JOURNAL OF PURE AND

APPLIED RESEARCH IN ENGINEERING AND

TECHNOLOGY

A PATH FOR HORIZING YOUR INNOVATIVE WORK

A COMBINATORIAL APPROACH FOR EDGE DETCTION

SAMIRA VENGURLEKAR, PROF. VINEET JAIN

Computer Science Department, Goa College of Engineering, Farmagudi, Ponda Goa.

Accepted Date: 05/03/2015; Published Date: 01/05/2015

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Abstract:Edge detection plays an important role in object identification. Application ranges from machine vision, image processing to pattern recognition. There exist many algorithms for detecting edges such as sobel, prewitt, canny, morphological operators etc. Canny operator is more susceptible to noise. This paper analyses the advantages and disadvantages of Canny operator and Morphological operator in the aspect of image edge extraction, and proposes a new edge extraction algorithm by combining two methods. The image edges are extracted using Morphological operator and Canny operator respectively. Then the Canny edges are restraint using the morphological edge. The edge points which are present in Canny edge but not in the morphological edge are discarded so that the noise in the Canny edge is eliminated. Morphological operator finds the structuring element from the image itself using freeman chain code, which is then followed by the Morphological Gradient method to detect edges. The proposed algorithm is anti-noise, simple, efficient and fast.

Keywords: Canny operator, Edge detection, Freeman chain code, Morphological operator, Prewitt, Sobel.

Corresponding Author: MISS. SAMIRA VENGURLEKAR

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Available Online at www.ijpret.com 362 INTRODUCTION

Edge detection plays an important role in image processing to find the boundaries of objects with an image. Edges are the changes or discontinuities of intensity in an image. Changes in physical aspects can occur in a variety of ways, including intensity, color and texture changes. These features are used in many applications including areas of image processing, pattern recognition, machine vision and computer vision. Convolving the image with an operator is a classical method of edge detection which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There exist many algorithms for detecting edges such as Sobel, Prewitt and Laplacian of Gaussian operator which are not effective for noisy images. Large numbers of edge detection operators are available which are designed to be sensitive to certain types of edges. Canny operator is the edge extraction algorithm designed according to the three optimal criteriafor edge extraction and the optimal step-type edge extraction operator theoretically, but it is sensitive to noise and less effective in the noisy images [2]. Difficulty in edge detection is detected when an image is noisy, since both the edges and noise contain high frequency content. The operator must be chosen in such a way that they are responsive to such a gradual change. Sometimes there are problems of missing true edges, false edge detection, edge localization, high computational time and problems due to noise etc. Canny edge detection algorithm removes noise to some extent but does not give perfect edge in all the cases. Morphological edge detectors here plays a important role in case of noisy images but these detectors requires the selection of the SE which is a small matrix of pixels, each with a value of zero or one. The smaller the size of SE, lesser is the noise removing capacity and more the ability to detect fine edges. This paper proposes a new edge extraction algorithm combining Canny operator and Morphological operator. The improved algorithm is anti-noise, simple, efficient and fast.

RELATED WORK

Available Online at www.ijpret.com 363 been proposed by Chanda et al. [4]. The proposed detector has better noise immunity and orientation and positional response compared to most of the conventional morphologic edge detectors. Rama Bai et al. [5] have proposed a novel approach for noise removal cum edge detection for both gray scale and binary images using morphological operations - erosion and dilation. Anna Wang et al have proposed A Novel Edge Extraction Algorithm Based on Canny Operator Combining Wavelet Transform. It analyses the advantages and disadvantages of Canny operator and wavelet transform in the aspect of image edge extraction, and proposes a new edge extraction algorithm combining the two methods.

Many authors have used fixed size and fixed shape SE for applying morphological operators on images.These SEs works very well with one type of images but fails completely for other type of images. Larger the size of the SE larger will be noise removing capacity but produces thicker images. smaller the size of SE less will be the noise removal capacity but produces thin edges. Similarly, square shaped SE are chosen to detect straight lines and round SE is chosen for detecting circular shapes. It is difficult to obtain appropriate shape and size of SE to get the desired edges of an image.

Lee et al. [6] have proposed a new morphological edge detector using amoebas, dynamic SEs which adapts their shapes to image contours. The proposed methods have less sensitivity to noise while detecting more details of image than other morphological edge detectors with a fixed SE. Mahesh Kumar et al. [7] have suggested a novel mathematical morphological edge detection algorithm based on multi shape structure. Algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The proposed work aims at generating SE dynamically from the image itself using chain code for morphological edge detector.

MORPHOLOGICAL OPERATOR

 Erosion: The erosion of a set X by a structuring element B is denoted by EB(X) and is defined as the locus of points x, such that B is included in X when its origin is placed at x: EB(X) = {x | Bx ⊆ X}

 Dilation: The dilation of a set X by a structuring element B is denoted by DB(X) and is defined as the locus of points x such that B hits X when its origin coincides with x: DB(X) = {x | Bx ∩ X ≠ ∅}

Available Online at www.ijpret.com 364 PB(X)=EB(X) - DB(X)

The problem faced in the various methods for detection of edges using morphological operator is the selection structural element, usually the shape and size of the structuring element are chosen by trial and error method. The proposed methodology comprises of following steps:

i. Pre-processing:

Initial step includes cleaning of an image using median filter to reduce noise if any. Salt and pepper noise is removed using median filter then followed by converting color image to gray scale images.

Figure 1. Morphological operator [10]

ii. Binirization:

This step identifies the various regions in an image. Input image is binarized using Global thresholding.

iii. Extract chain code:

Available Online at www.ijpret.com 365 Figure 2 Freeman chain code using 4-connectivity and 8-connectivity [10]

iv. Generate Structuring elements:

The chain code obtained is used to extract the structural element from the chain code dynamically. Method used to built SEs is given below:

a. Method 1:

 Extract Freeman Chain code from the binary image.

 Declare a 3x3 mask representing the 8-connected Freeman Chain code.

 Calculate the difference between adjacent values of Freeman Chain code

 If difference is positive then update the same structuring element with value 1 in the same position w.r.t the 3x3 mask.

 Else create a new SE and insert the value 1 in the same position w.r.t the 3x3 mask.

 Repeat the process till the end of the Freeman chain code.

 Combine all the resulting SE into one.

v. Edge Detection:

Final step is to use this dynamic SEs to extract edges in an image using morphological gradient method.

CANNY OPERATOR

Available Online at www.ijpret.com 366 partial derivative. Canny operator adopts a non-maximum restrained mechanism when processing. Finally, it uses two thresholds to link the edges. The steps are as follows [8]:

Suppose G(x,y) denotes 2D Gaussian filter. After Gaussian filter, the derivatives of x direction and y direction of the image:

Calculate the magnitude and direction of image gradient

Where A(i,j) reflects the intensity of the image edge; α(i,j) reflects the direction of the edge, make to A(i,j) obtain a local maximum. Because overall gradient is not sufficient to determine the image edge, it is necessary to retain the local gradient maximum points suppress the non-maxima suppression.

After the above treatment, there are two thresholds in Dual-threshold algorithm for non-maxima suppression, Th and Tl, Tl≈0.4Th, and detect any candidate edge pixels points, if the gradient magnitude at that point is higher than Th, that is a certain edge point, if not, it is definitely not the edge point, set its gray value to 0. So we can get two point sets, f1(i,j) and f2(i,j). f1(i, j) is the set of points higher than Th, that is the edge points, contains very few false edges, but intermittent. f2(i, j) is the points less than Tl, the non-edge points, with more false edges. For the points between the two thresholds, if its gradient magnitude higher than Th, then it is an edge point, add it to image f1(i,j); Otherwise, add it to image f2(i,j). Repeat the above process until all the points in the original image is computed.

PROPOSED METHODOLOGY COMBINING MORPHOLOGICAL OPERATOR AND CANNY OPERATOR

Available Online at www.ijpret.com 367 its anti-noise ability is low [9]. Considering the former problems, this paperproposes a new edge extraction algorithm combined morphological operator and Canny operator, and themethod can not only effectively suppress noise, but also have high positioning accuracy.The idea of the improved algorithm: As the Canny edge extraction method hashigh positioning precision, but the anti-noise ability is low, we can use the Canny edge as the original edge, and restrain the Canny edge by morphological edge to eliminate the noise in the Canny edge. Because if there is an edge in the Canny edge image, there must be an corresponding edge in the vicinity of this location in the morphological edge image; but if there is a noise point in the Canny edge, there is no edge point in the vicinity of this location in the wavelet edge, so the noise point can be removed from the Canny edge and the image edge can be obtained. Given the special circumstance: There may be a noise point which is very close to a near-by edge in the Canny edge, when checking the 8-neighborhood in the morphological edge, some points of the edge corresponding to the near-by edge may be contained in the 8-neighborhood, so the noise point can’t be removed. These points must be removed at last, because the pixels of noise are small and a small fixed value L can be set; when the length of the edge is smaller than L, remove the edge, so the influence of this kind of noise is disappeared. Finally connect the breakpoints to form the final edge. The steps of the method are:

(1) Input the image for edge extraction;

(2) Extract the edge of the image using canny operator and morphological operator respectively, get edge 1 and edge 2;

(3) For each point in edge 1, traverse the 8-neiborhood points in edge 2, if there is no edge point, this point is not the edge. Remove the point from edge 1; otherwise, this point is the edge. Retain this point in edge 1, and the processed edge 1 is the basic edge 3;

(4) Further process edge 3, remove the edge whose length is less than 3 pixels, and connect the breakpoints of the edge, to form the final edge.

CONCLUSION

Available Online at www.ijpret.com 368 REFERENCES

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