a comparative study of edge detection techniques … · a comparative study of edge detection...

Proceedings of International Conference on Recent Innovations in Engineering and Technology, Jaipur, India, 18th - 19th Feb’2017, ISBN: 978-93-86291-63-9

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A COMPARATIVE STUDY OF EDGE DETECTION TECHNIQUES APPLIED TO BATTLEFIELD IMAGERIES

1PARAMJEET SINGH, 2M. J. NIGAM

1,2Indian Institute of Technology, Roorkee, Uttarakhand ,India

Email: [email protected], [email protected] Abstract - Edge detection finds its use in numerous applications such as feature extraction, diagnosis in medical imaging and computer vision. Number of techniques are being used for filtering out the less relevant information from the images while preserving the basic structural properties. In this paper a comparative study of Sobel, Laplacian of Gaussian, Canny, Gabor Wavelets, Structured Forests and Morphological Fuzzy Logic techniques for edge detection has been carried out qualitatively with a motive to identify the best technique using battlefield images. Index terms - Edge detection, Sobel, Laplacian of Gaussian, Canny, Gabor wavelets, Structured forests, Morphological Fuzzy. I. INTRODUCTION Edge detection is one of the fundamental tasks in image processing because it has wide usage in several techniques such as segmentation, object recognition, feature extraction, military, medical diagnostics, robotics and meteorology etc. [1-4]. Edge detection deals with localization of abrupt changes in gray levels of an image and an edge is defined as the demarcation between two regions separated by two relatively distinct gray levels [5]. The edge-detection process is useful for simplifying the analysis of images by drastically reducing the amount of data to be processed [6]. An edge may occur because of variations in light absorption, color, shade, and texture, and these changes can be utilized to find out the depth, size, orientation, and surface properties of a digital image [7]. The aim of this study is to analyze Sobel, Laplacian of Gaussian, Canny, Gabor Wavelets, Structured Forests and Morphological Fuzzy Logic techniques for edge detection of battlefield images. II. SOBEL EDGE DETECTION The Sobel operator is one of the most common operator that is used in image processing, particularly in relevance to edge detection algorithms. Technically, it is a discrete differentiation operator, which computes an approximation of the gradient of the image intensity function f (x, y), where x and y are the two-dimensional coordinates. At each point in the image, the result of convolving the input images with the Sobel operator is either the corresponding gradient vector or the norm of this vector. Sobel operator comprises of two distinct 3x3 masks corresponding to x and y direction [8]. In order to suppress noise, a certain weight is correspondingly increased on the center point, and its digital gradient approximation equations may be described as under.

Generally, the gradient estimate can be calculated from the above components of gradient in x direction (Gx) and y direction (Gy) as under:

Where, g(x, y) is the resultant gradient of the input image. Its equivalent convolution operators can be represented by equation

where Sx and Sy denote the horizontal and vertical operators respectively. These two kernels are convolved with the input image separately to obtain the approximations of the derivatives in horizontal and vertical directions (Gx and Gy). The magnitude for the gradient is obtained using equation (3). The Sobel operators are preferred as compared to other traditional gradient operators like Roberts and Prewitt operators because they have better noise smoothing characteristics [9]. III. LAPLACIAN OF GAUSSIAN EDGE DETECTION The Laplacian operator can be used as an edge detection operator as it enhances the regions of sudden intensity variations in an image. As the Laplace of an image is quite sensitive to the noise along with the edges in an image, therefore the image needs to be smoothened first by applying a 2-D Gaussian filter which can be denoted by following kernel [10].

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A Comparative Study of Edge Detection Techniques Applied to Battlefield Imageries


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Where, G (x , y ) is the Gaussian kernel and is the standard deviation. Now if we take Laplacian of the above kernel while neglecting the factor, for simplifying the calculations, we get the following expression, which is also known as the LoG operator.

Where, ( , ) is the Laplacian of Gaussian operator and ∇ is the double derivative operator as mentioned under.

The LoG operator as obtained using equation (6), can be convolved with the input image to obtain the required edges. The expression for the same is as under.

Where, g(x,y) is the resulting gradient, I(x,y) is the input grayscale image, and ∗ denotes convolution operation. Here the size and standard deviation of the LoG operator yields different results, we have taken the size as 5 x 5, and σ as 0.5. IV. CANNY EDGE DETECTION Canny edge detection is a technique to detect edges of an image and was developed by John F. Canny. Canny has found that the requirements for the application of edge detection on diverse vision systems are relatively similar. Thus, an edge detection solution to address these requirements can be implemented in a wide range of situations. The Canny edge detection follows a multi-stage algorithm which enables it to function over a wide range of input images. The algorithm can be summed up as shown in following basic steps: 1) Use a Gaussian filter to remove the noise and make the image smooth. The Gaussian kernel given in equation (5) can be used for the filter. Smoothened image, Ig(x,y) is obtained as.

2) Compute the gradients using the operators given in equation (4), as under.

The resultant gradient, g(x,y) can be obtained using equation (3), and the angle of gradient vector, can be calculated as under.

3) Edge thinning is performed by applying non-maxima suppression to the gradient. The gradient so obtained is scanned along the gradient direction, and

if pixels are not part of the local maxima they are set to zero. 4) Detect and connect edges by the application of double thresholding or hysteresis [11] and connectivity analysis [9]. Apply thresholding to image obtained previous step by using two different thresholds, 1and 2 (where 1 < 2 ) to obtain two binary images say, T1 and T2 respectively. Observe that T2 with greater threshold will have lesser noise and fewer false edges but greater gaps between edge segments, when compared to T1 with smaller threshold. Link edge segments in T2 to form continuous edges. To do so, trace each segment in T2 to its end and then search its neighbors in T1 to find any edge segment in T1 to bridge the gap until reaching another edge segment in T2. V. GABOR WAVELET EDGE DETECTION The human visual system may be viewed as a filter bank. The responses of these filters can be modeled using different Gabor functions [12]. The Gabor features are particularly effective for texture representation and discrimination. These have been successfully used for object detection and face recognition [12-16]. In the spatial domain, a 2-Dimensional Gabor filter is given by a Gaussian kernel function which is modulated by a sinusoidal plane wave, represented as follows.

Gabor wavelets enhances the edges and diminishes the background noise when the wave vector is perpendicular to the edge and the resultant image of the convolution demonstrates the local properties showing the edge of the input image [12]. Kernels related to different orientations can be obtained by setting angle factor. To detect the edges in an image, which have different directions, θ is set to have eight different orientations, i.e.,

Where, k = 0,1,2… 7. After obtaining these edges in different directions, we can combine them and obtain the required edges. However, this method proves to be a bit computationally expensive. VI. STRUCTURED FORESTS EDGE DETECTION Edges in a small patch are highly interdependent, and often consists of well-known patterns, such as straight



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lines, junctions (Y or T) [19]. For such problems which exhibit similar types of characteristics, a structured learning approach can be applied using random forest framework [20]. Here edge detection is taken up as prediction of local segmentation masks for given patches of input image. An approach for learning decision trees uses labels, which are mapped to a discrete space for determining the splitting function at each branch in the tree. All predictions are aggregated to compute the final edge map [21]. A brief about the random forests and structured random forests is as under. A. Random Decision Forests



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VII. MORPHOLOGICAL FUZZY LOGIC EDGE DETECTION This technique of edge detection is based on generalized type-2 fuzzy logic and the morphological gradient, which enables better modeling related to the uncertainty that is present in processing digital images [25].



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VIII. RESULTS

Fig. 3. Resultant Edges for Images: Tank, Helicopter and Artillery Gun



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The edge detection techniques used for comparative study have been applied to battlefield images such as Tank, Helicopter and Artillery Gun to obtain the corresponding edges. Results are shown in Fig. 3. Comparison of results clearly show that the edges obtained by Morphological Fuzzy technique detects almost all the edges of the images used. The resultant edges of the images using Sobel, LoG and Canny techniques are quite comparable to each other and the results obtained by Structured Forests is better than these three methods. Quality of edges using Gabor Wavelet is found to be better than the results of Structured Forests techniques but slightly inferior to the quality of edges obtained by Morphological Fuzzy method. However, the execution time of Structured Forests method is least out of all the other techniques compared. CONCLUSION A comparative study of Sobel, Laplacian of Gaussian, Canny, Gabor Wavelets, Structured Forests and Morphological Fuzzy Logic techniques for edge detection has been carried out using battlefield images. From the qualitative comparison of the resultant edges, it has been identified that the Morphological Fuzzy technique yields the best results. REFERENCES [1] L. R. Liang, C. G. Looney,” Competitive fuzzy edge

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a comparative study of edge detection techniques … · a comparative study of edge detection...

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