Inspection of Rubber Cap Using ISEF (Infinite Symmetric Exponential Filter): An Optimal Edge Detection Method

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1 Inspection of Rubber Cap Using ISEF (Infinite Symmetric Exponential Filter): An Optimal Edge Detection Method Kunal J. Pithadiya (Sr. Lecturer, Electronics &Communication Engg. Department, B & B Institute of Technology, V V Nagar-38812, Gujarat, India) Abstract: It is very important to have quality checking and measurement of every type of medical related drug containing object or liquid filled object. Medical goods and its measurement always directly affect the quality of drug or tablet and ultimately the human life. If quality of any drug or tablet made compromised then it proves that we are playing with life of any human beings or animals. So it is required to take precautionary measure to avoid misdirected circumstances. We have employed machine vision and image processing (Edge Detection) to check quality of rubber cap of bottle filled with medical liquid or drug. In this paper we are inspecting rubber cap dimensions with its radius called formation and circumscribe. We are measuring radius and dimensions of rubber cap using ISEF (Infinite Symmetric Exponential Filter) an optimal edge detection technique. Keywords: Quality inspection, Machine vision, ISEF (Infinite Symmetric Exponential Filter) (Shen-Castan algorithm), Edge detection, Rubber Cap Inspection. I. INTRODUCTION ISEF is one optimal edge detection technique like LoG and Canny[1] [6]. In [2] Shen Castan et al., described optimal edge detection used for step edge detection (ISEF). In [3] and [4] Alberto Martin et al., and Mohsen Sharifi et al., has described different template based and optimal edge detection techniques respectively. In [5] and [7] Kunal Pithadiya et al., have discussed different method to inspect liquid level in glass bottle [9]. In [8] Chintan K Modi et al., discuss, compare and found the best edge detection technique to inspect SMD capacitor physical dimensions. In [1] Kunal Pithadiya et al., inspecting quality of SMD resistor with optimal edge detection techniques [11]. From the above experiments we are inspired to work on quality evaluation of rubber cap using an optimal edge detection technique such as ISEF. In Section-II we discuss the Problem definition. In Section-III we discuss about ISEF optimal edge detection algorithms. In Section-IV we presented Experimental method used for inspection of rubber cap using ISEF &machine vision techniques and Results are discussed in Section-V. Section-VI concludes the paper. II. PROBLEM DEFINITION TO INSPECT RUBBER CAP In the field of medical industry, measurement of mechanical dimensions of any object or product is very important task to take care about. To do such a cumbersome task, machine vision system can be employed, to measure mechanical dimensions of any product. Fig.1 shows the rubber cap used to cover neck or upper portion of glass bottle contains medical liquid or drug. So it is very important to check physical quality of rubber cap like its area, center, circumference, depth, layers etc. In the field of machine vision system, it is very common to use circle detection algorithm to find circumference and line detection algorithm to find edge peaks. In this paper we have employed innovative techniques with ISEF edge detection method to inspect the physical quantity of rubber cap. We are measuring radius of rubber cap ultimately checking whether that cap is perfect from all side or not. If it is not then it will affect the quality of medical drug or liquid stored inside the bottle. We have used two images to show the bifurcation between good product and bad product for top view and side view. Fig.1 shows the sample image of good product and Fig.2 shows sample image of the bad product in Top view. Same way sample image in side view of rubber cap as shown in Fig.3 and Fig.4 for good and bad product respectively. Page 44

2 Fig.1 Sample Image of Good Rubber Cap (top view) Fig.2 Sample Image of Bad Rubber Cap (top view) Fig.3 Sample Image of Good Rubber Cap(side view) Fig.4 Sample Image of Bad Rubber Cap (side view) IS EF Edge Detection Technique IS EF Algorithm[2][3][4][5][6][7][8][9][1][11] Shen and Castan [2][6] maintains, that it s filter gives better signal to noise ratios than Canny s filter, and provides better localization. This could be because the implementation of Canny s algorithm [1] approximates his optimal filter by the derivative of a Gaussian, whereas Shen and Castan uses the optimal filter directly, or could be due to a difference in this way the different optimality criteria are reflected in reality. Shen-Castan s Infinite symmetric exponential filter based edge detector is an optimal edge detector which gives optimal filtered image. First the whole image will be filtered by the recursive ISEF filter in X direction and in Y direction, which can be implement by using equations as written below. Then the Laplacian image can be approximated by subtracting the filtered image from the original image. Recursion in x direction: 1 j I j j 1... N, i 1.. M y (1) 2 j b I j j N... 1, i 1.. M j y1 j y2 j 1 y (2) r (3) Recursion in y direction: Page 45 1, 1 j I i 1, j, i 1... M, j 1.. N y (4) 2 j b I i 1, j, i M... 1, j 1.. N y j y1 j y2 i 1, j (6) y (5) 1 b=thinning Factor (<b<1)

3 At the location of an edge pixel, there will be zero crossing in the second derivative of the filtered image. The first derivative of the image function should have an extreme at the position corresponding to the edge in image and so the second derivative should be zero at the same position. And for thinning purpose apply non maxima suppression, as it is used in canny for false zero crossing. The gradient at the edge pixel is either a maximum or a minimum. If the second derivative changes sign from positive to negative, then this is called positive zero crossing and if it changes from negative to positive, it is called negative zero crossing. We will allow positive zero crossing to have positive gradient and negative zero crossing to have negative gradient, all other zero crossing we assumed to be false and are not considered to an edge. Now gradient applied image has been thinned, and ready for the thresholding. The simple thresholding can have only one cutoff but Shen -Castan suggests using Hysteresis thresholding. Spurious response to the single edge caused by noise usually creates a streaking problem that is very common in edge detection. The output of an edge detector is usually thresholded, to decide which edges are significant and streaking means the breaking up of the edge contour caused by the operator fluctuating above and below the threshold. Streaking can be eliminated by thresholding with Hysteresis. Individual weak responses usually correspond to noise, but if these points are connected to any of the pixels with strong responses, they are more likely to be actual edge in the image. Such connected pixels are treated as edge pixels if there response is above a low threshold. Finally thinning is applied to make edge of single pixel. The ISEF algorithm is given in Table I. Table I IS EF algorithm [5] [6] [7] No Steps 1 Apply ISEF Filter in X direction 2 Apply ISEF Filter in Y direction 3 Apply Binary Laplacian Technique 4 Apply Non Maxima Suppression 5 Find the Gradient 6 Apply Hysteresis Thresholding 7 Thinning EXPERIMENTAL METHOD [5][6][7][9][1] To inspect the quality of rubber cap, we have to find out circumscribe of rubber cap and its formation from all side. To do this, we have filtered all sample images shown in Fig-1, Fig-2, Fig-3, and Fig-4 using ISEF edge detection algorithm for top view and side view. Edge detected images are shown in Fig-7, Fig-9, Fig-11 and Fig-13. To find circumscribe of rubber cap, we have to measure radius from all points. So, to find radius of edge detected rubber cap images as shown in Fig-5 for top view, We have measured distance (in pixel) of circle line, starts from center to one point called A by applying mathematical equation of circle called radius. Then we proceed to find another radius also called distance from center to point called B and same way we have measured distances (in pixel) for all further points on circle accordingly. After application of this technique, we have different normalized radius points (in pixel) from center of circle to all different points on circle in edge detected image of rubber cap top view. To find formation of rubber cap, we have used edge detected image (side view) as shown in Fig-6. We have divided the total area of rubber cap in five parts: A, B,C, D and E. Area A, B,C,D and E are shown in the Fig-6. Then we have found distances for A, B, C, D and E from the reference line shown in blue color in Fig-6.The method, to find out the distances(shown in Appendix I & II) in pixel for rubber cap is shown in Table II. TABLE II [8][1] Algorithm to find Radius & Distances for Area A, B, C, D and E No. Steps 1 Acquire the image as shown in Fig-1 and Fig-3. 2 Apply ISEF optimal edge detection algorithm to all images. 3 Resulted Edge detected Image shown in Fig-5 and Fig-6. 4 Measure distance or radius of circle using mathematical equation of circle called radius r as shown in Fig -5 in terms of pixel. Page 46

4 AA 5 Measure distances A, B, C, D, and E from reference line (in pixel) as shown for Fig 6. Fig.5 IS EF filtered Image of Good Rubber Cap Circumscribe (top view) Fig.6 IS EF filtered Image of Good Rubber Cap Formation (side view) III. RESULTS AND DISCUSSION To inspect the quality of rubber cap, we have divided rubber cap in two parts 1. Find circumscribe-top view 2. Find formation-side view. First we have filtered top view and side view images for good and bad rubber cap using ISEF optimal edge detection algorithm. All the edge detected images are shown below as Fig.7, Fig.9, Fig.11 and Fig.13. To find circumscribe of rubber cap of Fig.7 and Fig.9, we have normalized all the distances of all given points like A,B etc shown in Fig.5. Then we have plot the line curve for all normalize points as shown in Fig.8. By seeing Fig.8, one can understand that if radius of rubber cap from the center is same for all point then approximately horizontal line will be plotted on graph. But if rubber cap s radius is not same for all points means bad quality product, then normalize plot will get disturbed in some pixel range as shown. That improper horizontal plot is shown in Fig.1. So, one can easily understand that Fig-7 is related to graph shown in Fig-8 of good quality product.fig-9 is related to Fig-1 which is plot of bad quality rubber cap. Fig-7 IS EF Edge detected Image of Good rubber cap (top view) 3 Fig-9 IS EF Edge detected Image of Bad rubber cap (top view) Fig-8 Normalize radius plot of good rubber cap Fig-1 Normalize radius plot of bad rubber cap Page 47

5 Distance Distance Kunal J. Pithadiya, International Journal of Research in Engineering and Social Sciences, ISSN , Impact To find formation of rubber cap using Fig.11 and Fig.13 shown below, we have divided complete side view image in five different parts like A, B, C, D and E as discussed earlier [6] [7] [8] [9] [1]. Now from the given horizontal reference line, we have measured the distances in terms of pixel up to the edge of rubber cap for all different parts shown such as A,B,C,D and E. If the formation is good then one will get the curve as shown in Fig.12. But if formation is not good or bad, then one will get improper curve for all parts as shown in Fig.14. It means that Fig-11 is related to Fig-12 of good quality product and Fig-13 is related to Fig-14 of bad quality product. After this experiment, one can easily decide whether the quality of rubber cap is acceptable or not. Both the techniques discussed above, can be used in real time machine vision system. Fig-11 Edge detected Image of good rubber cap (side view) ISEF Operated Pixel Fig-12 Distance Plot of good rubber cap (side view) Fig-13 Edge detected Image of bad rubber cap (side view) ISEF Operated Pixel Fig-14Distance Plot of bad rubber cap (side view) IV. CONCLUSION In the result, we have found the rubber cap distances for Area A, B, C, D and E to find formation (shown in Appendix I and II) and radius (in pixel) to find circumscribe using ISEF optimal edge detection method. ISEF algorithm is an optimal and more reliable edge detection technique. Here we have inspected the quality of rubber cap for top view and side view as discussed earlier. This is also applicable to any machine vision system or automation system where formation and circumscribe of any rubber cap or circular product physical dimensions required to be measure. Based on upper threshold given, a machine can decide to accept or reject the rubber cap easily. It is one of the effective, reliable, accurate and applicable in real time system for better quality inspection especially for non-variable illumination area. V. REFERENCES [1] Canny, J., A Computational Approach to EdgeDetector, IEEE Transactions on PAMI, pp: ,(1986). [2] Shen.J.Castan, An optimal linear edge detector for step edge detection, Computer Graphics and Image Processing: Graphical models and understanding.,vol.54,no.2,(1992) [3] Alberto Martin, Image Processing Techniques for machinevision, Miam Florida, (2). [4] Mohsen Sharif A Classified and Comparative Study of Edge Detection Algorithms Proceedin gs of the International Conference on Information Technology: Coding and Computing, IEEE (22). [5] Kunal J Pithadiya et al. Comparison of optimal edge detection algorithms for liquid level inspection in bottles International Conference on Emerging Trends in engineering and Technology, ICETET-9, Nagpur (29). [6] Kunal Pithadiya et al Performance evaluation of ISEF and Canny edge detector in Acrylic fiber quality control production, Proceedings of National Conference on Innovations in Mechatronics Engineering,GCET, V VNagar, India, pp (29) Page 48

6 [7] Kunal J. Pithadiya et al., Selecting the Most Favorable Edge Detection Technique for Liquid Level Inspection in Bottles, International Journal of Computer Information Systems and Industrial Management Application s. (IJCISIM),Volume 3, pp: 34-44, ISSN: , (211) [8] Chintan K. Modi et al., "Selecting the Most Favorable Edge Detection Technique for Multi-layer Chip Capacitor", SRESA Journal, Life Cycle Reliability and Safety Engineering Vol-2, Issue 2, 53-62,ISSN , (213) [9] J D Chauhan et al. Location M estimator for liquid level inspection using machine vision ICSSC-29, DEC-21-23, page , Anna University, Chennai (29) [1] Kunal J Pithadiya et al. Evaluating the Most Efficient Edge Detection Technique for Inspection of Chip Resistor, Vol. 3, Issue 9, September 215, IJIRCCE, (215) [11] Ketan S Patel et al., Comparative Analysis of Different Estimation Method with Optimal EDGE Detection Techniques, IJFTET, Vol-2(3), (215) Appendix-I Columns distances of good Rubber Cap for ISEF (ISEF A=12, ISEF B= 63, ISEF C=169, ISEF D=59, ISEF E=16) Region A B C D E Page 49

7 Appendix-II Columns distances of bad Rubber Cap for ISEF (ISEF A=17, ISEF B=62, ISEF C=16, ISEF D=49, ISEF E=43) Region B C D E A Page 5