A novel fast block matching algorithm considering cost function and stereo algorithms 2-3

1. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014 INTERNATIONAL JOURNAL OF ELECTRONICS AND 17 – 19, July 2014, Mysore, Karnataka, India COMMUNICATION ENGINEERING TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 8, August (2014), pp. 127-131 © IAEME: http://www.iaeme.com/IJECET.asp Journal Impact Factor (2014): 7.2836 (Calculated by GISI) www.jifactor.com 127 IJECET © I A E M E A NOVEL FAST BLOCK MATCHING ALGORITHM CONSIDERING COST FUNCTION AND STEREO ALGORITHMS Bindu N S1, Harshitha N U2, Rashmi H U3, Praveen Kumar4 1, 2, 3, 4Dept of ECE, Vidyavardhaka College of Engineering, Mysore, Karnataka, India ABSTRACT Stereo matching is an active research area in computer vision. In this paper a Novel Fast stereo Matching Algorithm is proposed by considering Cost Function. In this approach, a dynamic programming based algorithm has been used to find dense disparity map and this approach also reduces the computational cost. In each step, a new disparity map is obtained by interpolation. The new dense disparity map will be updated only in selected areas, instead of the whole map, according to the local matching cost and the depth difference among neighboring areas. The proposed approach is able to obtain a smooth dense disparity map and also aims at preserving discontinuity. The proposed approach is evaluated using a pair of rectified stereo image pair and a better and high quality results are achieved and also running speed is also improved. Keywords: Dynamic Programming, Fast Algorithm, Stereo Matching. 1. INTRODUCTION Stereo vision is one of the active research areas which are studied widely past from many decades. The correspondence problem in stereo vision is also one of the active research areas. The main aim in stereo matching is to find unique mapping of various points belonging to two or more images when same scene is considered. As we know that stereo vision techniques are capable of converting 2D images to 3D images, they are extensively used and applied in many industries. Stereo vision techniques are used in industries to build 3D models of objects in computer graphics. It is also used to find the corresponding locations of objects in order to understand the semantic representation among different objects. In this work, we mainly concentrate on vegetation conditions. A vegetation condition includes the location and height of trees which is to be considered in large scale. For this purpose we are considering a large sized video and images within a specified time, for which we are developing a fast stereo matching algorithm. In [1], the author Scharstein and Szeliski has presented taxonomy of two frame stereo matching methods. It has provided an outstanding comparison of dense stereo correspondence algorithms. Stereo matching algorithms are broadly classified into two categories, namely local stereo algorithm and global stereo algorithms. In [2, 3, 4, 5, 6], the author

2. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014 17 – 19, July 2014, Mysore, Karnataka, India has presented various local based stereo algorithms namely SAD, NCC, SSD and many more. In [7, 8, 9, 10, 11, 12], the author has presented various global based stereo algorithms and some of them are global optimization and dynamic programming (DP). Local based stereo algorithms are also similar to area based stereo algorithms, where only a smaller window is considered in order to avoid unwanted smoothing. A larger window is considered in the areas of low texture because it contains enough intensity variations so that a reliable stereo matching can be obtained. Area based stereo algorithms mainly focuses on the aggregation of the matching cost. Alternatively, a global algorithm makes a smoothness assumption first and then solves an optimization problem. In order to prevent the problem of over smoothing, an energy function is necessary. Many methods are proposed past from many decades in order to minimize the global cost. Graph cuts is an abstract representation of a set of objects, where several pairs of the objects are connected by links. It is a mathematical structure and is used to model Pair wise relations between objects from a certain collection. A different class of global optimization algorithms is those based on dynamic programming. Dynamic programming can find the global minimum for independent scan lines in polynomial time. Dynamic programming was first used for stereo vision in sparse, edge-based methods (Baker and Binford, 1981; Ohta and Kanade, 1985). Most of the global corresponding methods are very expensive sometimes which in turn needs a huge set of parameters to determine. The motivation of this research is to develop an algorithm for fast stereo matching that is able to produce smooth dense depth maps and preserve enough depth discontinuity. 2. AREA BASED STEREO MATCHING ALGORITHMS 128 The main aim of the area based stereo matching algorithm is to estimate the similarities between two or more images in order to obtain a dense disparity map from these stereo images. Ideally, the block is very large enough to cover sufficient intensity variation so that the similarity estimation is robust to noise. Similarity function plays a very important role in fast stereo matching. Similarity function is also called as cost function. The cost function should be very robust to noise and also illumination. Past from many decades most of the researchers have designed various cost functions namely SAD, SSD, NCC, ZSAD, ZSSD, LSAD, and LSSD. Among these, SAD and SSD are the popular cost functions and most widely used because of its simplicity in implementation. But these two cost functions are very sensitive to illumination and camera gain. This is illustrated in Figure.1. Figure.1: Difference caused by various Camera Bias The ZNCC stereo algorithm is used in order to deal with different camera bias. Even though the ZNCC is very expensive, most of the researchers use this. As we all know that ZSAD is very insensitive to differences in camera gain and computation is very less, we use this stereo matching algorithm in our experiments. Further for the comparisons we use SAD, SSD, ZNCC, and ZSAD.

3. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014 17 – 19, July 2014, Mysore, Karnataka, India 129

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7. % 3. ESTIMATION OF THE DISPARITY MAP We consider a top layer of winner take all (WTA) in order to start disparity map estimation. The result in terms of disparity map of winner take all contains an error when considering the flat areas. Even though the window size is large we can see these errors. In Fig.2 we can see such errors. As we can see in the figure that, the top layer is very small compare to the original image, so we are applying dynamic programming in order to obtain a better quality estimation of the disparity map. The cost measure can be defined as ' ' ( )* Where C(x, y, d) is the cost measure at position (x, y) and d is the disparity and S is the window size. I1 and I2 are the intensities of left and right stereo images. I1 and I 2 are the mean values of intensities. We then apply interpolation on the disparity map of the top layer to obtain the initial estimation of the disparity map for the second top layer. If the disparity difference among neighboring positions exceeds a threshold value or the cost measure at the position for the estimated disparity is too large, the disparity at that position will be updated. The criteria for updating disparity are given as follows |c(x, y)-c(x-1, y|μ, (6) |c(x, y)-c(x, y-1)|μ, (7) c(x, y, d)v, (8) Where μ and v are thresholds and d (x, y) is the disparity at position (x, y). The updating is a local process in which the continuity with two causal neighbors only is under the consideration. 4. EXPERIMENTAL RESULTS We have evaluated the proposed fast stereo matching algorithm by considering several real stereo image pairs. The first stereo pairs we have considered is Tsukuba stereo pair. This is one of the widely used dataset to evaluate the stereo matching algorithms because this contains objects in

8. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM 17 – 19, July 2014, Mysore, Karnataka, India different depths. Our proposed stereo algorithm provides a very good quality in terms of disparity as we can see in the Fig.2. Fig.2: The estimation of the disparity map: (a) Left image of the Tsukuba. (b) The dispa obtained by the proposed algorithm The second example which we have considered in our evaluation is the images of road as we can see in the fig.1. As we can see in the image that it contain trees and buildings. An illumination difference in both the images play an important role as it is very significant. Except the cost function both the disparity maps are evaluated and it is obtained by proposed algorithm called fast stereo algorithm. The disparity map is shown if Fig.3. The results which SAD based cross correlation algorithms (cost function). The results which is shown in fig.4. Is obtain using ZSAD based cross correlation algorithms (cost function). Fig.3: The estimation of the disparity Fig.4: The estimation of the disparity map using ZSAD 5. CONCLUSION This paper presents a novel fast stereo matching algorithm. In order to get the initial estimation of disparity map, the dynamic disparity map will be updated only in selected areas according to the local matching cost and the depth difference among neighboring areas. The proposed algorithm is evaluated using rectified stereo images. From the proposed algorithm we could obtain a better and very good result in terms of high quality and also the speed. Experimental results shows that the ZSAD based stereo algorithms are very robust to illumination and also robust to camera gain. 130 ained wn is shown in fig.3. Is obtain using tion map using SAD-based cost function ZSAD-based cost function programming is applied on the top layer. The new dense ages. -2014 disparity map wn ion

9. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014 17 – 19, July 2014, Mysore, Karnataka, India 131 REFERENCE [1] D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International Journal of Computer Vision, vol. 47, no. 1/2/3, pp. 7– 42, April-June 2002. [2] L. D. Stefano, M. Marchionni, S. Mattoccia, and G. Neri, “A fast area-based stereo matching algorithm,” 2002. [3] C. Sun, “Fast stereo matching using rectangular subregioning and 3d maximum-surface techniques,” International Journal of Computer Vision, vol. 47, no. 1/2/3, pp. 99–117,May 2002. [4] J. Banks, M. Bennamoun, and P. Corke, “Non-Parametric Techniques for Fast and Robust Stereo Matching,” in Proceedings of IEEE TENCON - Speech and Image Technologies for Computing and Telecommunications, 1997, pp. 365–368. [5] H. H. B. R. C. Bolles and M. J. Hannah, “The JISCT Stereo Evaluation,” in Proceedings of DARPA Image Understanding Workshop, 1993, pp. 263–274. [6] R. Zabih and J. Woodfill, “Non-Parametric Local Transforms for Computing Visual Correspondence,”in Proceedings of ECCV, 1994, pp. 151–158. [7] C. L. Zitnick and T. Kanade, “A cooperative algorithm for stereo matching and occlusion detection,” IEEE Trans. on pattern analysis and machine intelligence, vol. 22, no. 7, pp. 675–684, July 2000. [8] O. V. Yuri Boykov and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. on pattern analysis and machine intelligence, vol. 23, no. 11, pp. 1222 – 1239, November 2001. [9] D. Terzopoulos, “Regularization of inverse visual problems involving discontinuities,” IEEE Trans. on pattern analysis and machine intelligence, vol. 8, no. 4, pp. 413–424, April 1986. [10] P. N. Belhumeur, “A bayesian approach to binocular stereopsis,” International Journal of Computer Vision, vol. 19, no. 3, pp. 237– 260, 1996. [11] S. Birchfield and C. Tomasi, “A pixel dissimilarity measure that is insensitive to image sampling,” IEEE Trans. on pattern analysis and machine intelligence, vol. 20, no. 4, pp. 401–406, April 1998. [12] S. B. R. I. J. Cox, S. L. Hingorani and B. M. Maggs, “A maximum likelihood stereo algorithm,” CVIU, vol. 63, no. 3, pp. 542–567, 1996.

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