VARIOUS METHODS IN DIGITAL IMAGE PROCESSING. S.Selvaragini 1, E.Venkatesan 2. BIST, BIHER,Bharath University, Chennai-73

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1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu VARIOUS METHODS IN DIGITAL IMAGE PROCESSING S.Selvaragini 1, E.Venkatesan 2 1 Asst.Professor, Department of MCA BIST, BIHER,Bharath University, Chennai-73 2 Asst.Professor, Department of MCA BIST, BIHER,Bharath University, Chennai venkatelumalai12@yahoo.co.in Abstract: In computer and image processing edge detection, playing a vital role. It is problems of fundamental importance in image analysis edge characterizes object boundaries and are useful for segmentation, registration and identification of objects in a picture. In this report we discuss feature extraction. Firstly, we put on the Gaussian noise removal method for getting rid of the noise from image collected. Secondly, some edge detection operator such as Binary Morphology, canny edge detection, log edge detection, differential edge detection are given and studied. According to simulated result the advantages and disadvantages of these edge detection operators are compared. It is recorded that the binary morphology operator can obtain better edge detection. Keywords: Boundary Detection, Digital image processing, operator, noise removal, Gaussian noise mode 1. Introduction The boundary is a set of those pixels whose gray have the tone change and rooftop change, and it exists between object and ground object and object region and the region between element and ingredient. When an image is acquired the factors as projection, mix, appearance and noise are produced. Above mention factors bring on image features blur and distortion, due to this it is hard to detect boundaries. In process of noising we use a Gaussian Noise removal of an icon along the local theatre after then we apply different operators e.g Binary Morphology operators, canny operator, log operator, and differential operator for edge detection [1]. The image can be borne on by noise inevitably in the procedure of saving and transmission and noise causes the negative result on the image processing and depth psychology.[16] For taking out these effects, it is necessary to take away or diminish the noise, at the same time preserve the image data as much as possible, such as edge and the grain 2. Picture De-Noising The Gaussian noise model has a very significant feature, it does not count how much the variance and histogram of the original image is it will always stick with the Gauss distribution. In Gaussian method firstly according to the feature that in the image the local neighborhood pixel in the same object is smooth, we figure whether the pixel detail is on the image edge, the noise point or the edge texture point [4]. And then, according to the local continuity of the image edge and the texture feature, using the continuity of the icon. And then turn up the interference stops. Lastly, for the noise whichh is not on the edge or the grain. Using the base value of the non-noise points in the adaptive neighborhood to get rid of the noise, and for the noise on the edge and texture region just using the pixel points of the neighborhood edge and grain to smooth. With the aid of this method we can get rid of the Gaussian noise in the image well and the number of the residual noise points decreases sharply.[23] (a) Noise Image (b) Mean Filter (c) Gaussian Filtering (d) Strong Gaussian (e) The Method of Gaussian Noise (f) Original Image Removal used as Paper 265

2 Figure 1 3. Edge Detection Edge detection is a problem of fundamental importance in image analysis. In typical images, edges characterize object boundaries and are therefore useful for segmentation, registration and identification of objects in a scene.[25] In other words, we can say that an edge is not a physical entity, just like a shadow. It is where the picture ends and the wall starts. It is where the vertical and the horizontal surfaces of an object meet. It has no width because between a bright window and the darken of the right. Basically edge detection contains the following two parts:-[26] (1) Using edge operators the edge point set extracted. (2) Some edge points in the edge point set are removed then the obtained edge points are connected to be a line [2]. Commonly we use following operators for edge detection, e.g. Binary morphology, Canny, Log and Differential operator. A. Binary Morphology Binary image is likewise known as black and white picture. Equally we know that objects[24] can be easily placed in dark background. In this method we can apply binary image and mathematical morphology, thus it is known as Binary morphology.[27] The basic idea of this method is that to measure and press out the corresponding shape from an image with structural elements having stated form. And then that the image analysis and processing can be completed [6]. The method as follows: Imagine that the region is evidenced in the course of the set A. Its political bias is bordering on criminal β (A). B is an appropriate structure element, and it is symmetrical around the origin [7]. Firstly, we corrupt A with B Recorded as A B= {x (B) x A}, where (B) x is a displacement B on the vector. The interior of the region is available with A B. And A-(A B) is the borderline naturally. Then β (A) is obtained. The equation of edge extraction can be said β (A) = A- (A B). Structuring element is larger; the edge gained will be wider. B. Canny Operator This method is not easily interrupted by noise and can preserve the good balance between noise and edge detection.[29] It can find the true weak edge. For twodimensional image, canny operator can produce [22]two information, including the border, gradient direction and strength [3]. Canny operator is actually using templates of different directions to do convolution of the image respectively. Then the mostly direction is chosen.[30] From the standpoint of positioning accuracy, canny operator is more serious than the other manipulators. The Canny operator is a sort of new edge detection operator [9]. It has respectable performance of finding edges, which delivers a broad application. The Canny operator edge detection is to seek for the partial maximum value of image gradient.[31] The gradient is calculated by the derivative of Gauss filter. The Canny operator uses two doors to detect strong edge and Weak edge respectively.[13] C. Log Operator The Log operator is a linear and time-invariant operator. It detects edge points through searching for spots which two-order differential coefficient is zero in the image gray levels.[14] The Log operator is the operation of filtering and counting differential coefficient for the picture. It defines the zero overlapping position of filter output using convolution of revolving symmetrical Log template [32]and the image [10]. The Log operator s template is presented in Fig Figure 2. Log Operators Table In the detection process of the Log operator, we firstly pre-smooth the image with a [33]Gaussian low-pass filter, and then see the steep edge in the image making use of the Log operator. Finally, we carry on Binarization[15] with zero gray level to give birth to closed, connected outline and do away with all internal spots [8]. But double pixel boundary usually appears using the Log operator to detect edge, and the operator 266

3 is really sensitive to disturbance. So the Log operator is frequently used to judge that Edge pixels lie in either bright section or dark section of the picture.[16] D. Differential Operator Differential operator can outstand grey change. There are some points where gray [34]change is larger. And the value computed in these levels is higher applying[20] the derivative operator [5]. And then these different values may be viewed as relevant edge intensity and gather the points go down off the edge through setting thresholds[10] for these differential values. 4. Simulative Result Analysis In parliamentary law to cognize about the advantages and disadvantages of these edge detection operators, [28]we detect edge using these different operators respectively. The results of the simulation are shown in Fig. 3. Original Image Edge Extraction Detecting edge with Binary Morphology Original Image Canny Operator Original Image Image after Differential Operator Log Operator Figure 3. Simulation Results From the simulation results we can conclude that: the effect of detecting edges with Sobel operator after Gaussian de-noising and with Binary morphology directly is more dependable. And then these two methods can be applied. But ultimately we choose a Binary Morphology method based on specific measurement errors.[17] 5. End These edge detection operators can have better edge effect under the circumstances of obvious edge and low interference. Only the actual collected image has lots of disturbances. So many noises may be considered as edge to be found. In order to Solve the problem; [11]wavelet transformation is applied to the racket in the paper. Even so its effect will be better if those simulation images processed above are again processed through edge thinning and tracking. Although on that point are various edge Detection methods in the field of image edge detection certain disadvantages always exist[18]. For example, keeping noise and keeping detail can t achieve optimal effect simultaneously. Hence we will acquire satisfactory result if choosing a suitable edge detection operator according to the specific position in practice.[12] References [1] Udayakumar R., Kaliyamurthie K.P., Khanaa, Thooyamani K.P., Data mining a boon: Predictive system for university topper women in academia, World Applied Sciences Journal, v-29, i-14, pp-86-90, [2] Kaliyamurthie K.P., Parameswari D., Udayakumar R., QOS aware privacy preserving location monitoring in wireless sensor network, Indian Journal of Science and Technology, v-6, i-suppl5, pp , [3] Brintha Rajakumari S., Nalini C., An efficient cost model for data storage with horizontal layout in the cloud, Indian Journal of Science and Technology, v-7, i-, pp-45-46, [4] Brintha Rajakumari S., Nalini C., An efficient data mining dataset preparation using aggregation in relational database, Indian Journal of Science and Technology, v-7, i-, pp-44-46, [5] Khanna V., Mohanta K., Saravanan T., Recovery of link quality degradation in wireless mesh networks, Indian Journal of Science and Technology, v-6, i- SUPPL.6, pp , [6] Khanaa V., Thooyamani K.P., Udayakumar R., A secure and efficient authentication system for distributed wireless sensor network, World Applied Sciences Journal, v-29, i-14, pp , [7] Udayakumar R., Khanaa V., Saravanan T., Saritha G., Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction, Middle - East Journal of Scientific Research, v-16, i-12, pp , [8] Khanaa V., Mohanta K., Saravanan. T., Performance analysis of FTTH using GEPON in direct and external modulation, Indian Journal of Science and Technology, v-6, i-suppl.6, pp , [9] Kaliyamurthie K.P., Udayakumar R., Parameswari D., Mugunthan S.N., Highly secured online voting system over network, Indian Journal of Science and Technology, v-6, i-suppl.6, pp ,

4 [10] Thooyamani K.P., Khanaa V., Udayakumar R., Efficiently measuring denial of service attacks using appropriate metrics, Middle - East Journal of Scientific Research, v-20, i-12, pp , [11] R.Kalaiprasath, R.Elankavi, Dr.R.Udayakumar, Cloud Information Accountability (Cia) Framework Ensuring Accountability Of Data In Cloud And Security In End To End Process In Cloud Terminology, Technology (Ijciet) Volume 8, Issue 4, Pp , April [12] R.Elankavi, R.Kalaiprasath, Dr.R.Udayakumar, A fast clustering algorithm for high-dimensional data, Technology (Ijciet), Volume 8, Issue 5, Pp , [13] R. Kalaiprasath, R. Elankavi and Dr. R. Udayakumar. Cloud. Security and Compliance - A Semantic Approach in End to End Security, International Journal Of Mechanical Engineering And Technology (Ijmet), Volume 8, Issue 5, pp , [14] Thooyamani K.P., Khanaa V., Udayakumar R., [15] Udayakumar R., Thooyamani K.P., Khanaa, Random projection based data perturbation using geometric transformation, World Applied Sciences Journal, v-29, i-14, pp-19-24, [16] Thooyamani K.P., Khanaa V., Udayakumar R., [17] Udayakumar R., Thooyamani K.P., Khanaa, Deploying site-to-site VPN connectivity: MPLS Vs IPSec, World Applied Sciences Journal, v-29, i-14, pp- 6-10, [18] Udayakumar R., Kaliyamurthie K.P., Khanaa, Thooyamani K.P., Data mining a boon: Predictive system for university topper women in academia, World Applied Sciences Journal, v-29, i-14, pp-86-90, [19] Kaliyamurthie K.P., Parameswari D., Udayakumar R., QOS aware privacy preserving location monitoring in wireless sensor network, Indian Journal of Science and Technology, v-6, i-suppl5, pp , [20] Brintha Rajakumari S., Nalini C., An efficient cost model for data storage with horizontal layout in the cloud, Indian Journal of Science and Technology, v-7, i-, pp-45-46, [21] Brintha Rajakumari S., Nalini C., An efficient data mining dataset preparation using aggregation in relational database, Indian Journal of Science and Technology, v-7, i-, pp-44-46, [22] Khanna V., Mohanta K., Saravanan T., Recovery of link quality degradation in wireless mesh networks, Indian Journal of Science and Technology, v-6, i- SUPPL.6, pp , [23] Khanaa V., Thooyamani K.P., Udayakumar R., A secure and efficient authentication system for distributed wireless sensor network, World Applied Sciences Journal, v-29, i-14, pp , [24] Udayakumar R., Khanaa V., Saravanan T., Saritha G., Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction, Middle - East Journal of Scientific Research, v-16, i-12, pp , [25] Khanaa V., Mohanta K., Saravanan. T., Performance analysis of FTTH using GEPON in direct and external modulation, Indian Journal of Science and Technology, v-6, i-suppl.6, pp , [26] Kaliyamurthie K.P., Udayakumar R., Parameswari D., Mugunthan S.N., Highly secured online voting system over network, Indian Journal of Science and Technology, v-6, i-suppl.6, pp , [27] Thooyamani K.P., Khanaa V., Udayakumar R., Efficiently measuring denial of service attacks using appropriate metrics, Middle - East Journal of Scientific Research, v-20, i-12, pp , [28] R.Kalaiprasath, R.Elankavi, Dr.R.Udayakumar, Cloud Information Accountability (Cia) Framework Ensuring Accountability Of Data In Cloud And Security In End To End Process In Cloud Terminology, Technology (Ijciet) Volume 8, Issue 4, Pp , April [29] R.Elankavi, R.Kalaiprasath, Dr.R.Udayakumar, A fast clustering algorithm for high-dimensional data, Technology (Ijciet), Volume 8, Issue 5, Pp , [30] R. Kalaiprasath, R. Elankavi and Dr. R. Udayakumar. Cloud. Security and Compliance - A Semantic Approach in End to End Security, International Journal Of Mechanical Engineering And Technology (Ijmet), Volume 8, Issue 5, pp , [31] Thooyamani K.P., Khanaa V., Udayakumar R., [32] Udayakumar R., Thooyamani K.P., Khanaa, Random projection based data perturbation using geometric transformation, World Applied Sciences Journal, v-29, i-14, pp-19-24, [33] Thooyamani K.P., Khanaa V., Udayakumar R., [34] Udayakumar R., Thooyamani K.P., Khanaa, Deploying site-to-site VPN connectivity: MPLS Vs IPSec, World Applied Sciences Journal, v-29, i-14, pp- 6-10,

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