AUTHORS: Ruri Suko Basuki

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ABSTRACT: Motion vector is acquired by calculating the conclusive dissimilarity of blocks in the current frame and search block on next frames. Block Matching Algorithm (BMA) is employed to obtain the motion vector value. The result is added to pixel coordinates in current frame correlated with user constraints. Next, the object segmentation process is performed by matting techniques, after constraint scribble automatically occupies the next frame. However, matte extraction reveals a high error rate value after evaluation of segmentation results, caused by motion vector calculation which is as the driving of constraint parameter conducted in entire block. As result, position of pixel scribble is extending and far from object expected when motion vector value is applied. To solve the problem, calculation of motion vector performance is only on the block directly correlated to pixels scribble. This research presents an approach estimating constraint on semi-automatic segmentation of video object and the aims is to estimate the constraint in driving position of pixels scribble, where in the object extraction in a single frame is done with image matting, while the temporal domain motion estimation algorithm performed by Exhaustive Search of the BMA, but it is not robust algorithms for motion estimation on the label (scribble). Thus, in this study improved with the ES algorithms are developing and applying adaptive block SAD (Sum of Absolute Difference) to determine the distance vector. At final, the motion vector value is used to move the label from current frame to next frame. The result reveals accuracy improvement of 71.19%.

KEYWORDS: object segmentation, constraint estimation, motion vector prediction

REFERENCES:

[1] H. Tsai-Tsung and P. Yu-Nan, “High Efficiency Architecture Design of Real-Time QFHD for H.264/AVC Fast Block Motion Estimation,” IEEE Trans. Circuits Syst. Video Technol., pp. 1446–1658, 2011.

[2] A. Yong-Jo, H. Tae-Jin, S. Dong-Gyu, and H. Woo-Jin, “Implementation of Fast HEVC Encoder Based on SIMD and Data-level Parallelism,” EURASIP J. Image Video Process., vol. 16, pp. 1–19, 2014.

[3] O. Jens-Rainer, G. J. Sullivan, H. Schwarz, T. Tan, and T. Wiegand, “Comparison of the Coding Efficiency of Video Coding Standards - Including High Efficiency Video Coding (HEVC),” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1669–1684, 2013.

[4] G. Sullivan, O. Jens-Rainer, W. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649–1668, 2012.

[5] K. Chen, Y. Duan, L. Yan, J. Sun, and Z. Guo, “Efficient SIMD Optimization of HEVC Encoder Over X86 Processors,” in Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012.

[6] G. Clare, F. Henry, and S. Pateux, “Wavefront parallel processing for HEVC encoding and decoding,” ITU-T/ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC) document JCTVC-F274, 2011.

[7] A. Bovic, The Hand Book of Image and Video Processing, 2nd ed. San Diego: Academic Press, 2000.

[8] R. Basuki, M. Soeleman, M. Hariadi, M. Purnomo, A. Yogananti, and R. Pramunendar, “Spectral-Based Video Object Segmentation using Alpha Matting and Background Subtraction,” in IIEEJ the 4th International Workshop on Image Electronics and Visual Computing, 2014.

[9] C. Toklu, A. Tekalp, and A. Erdem, “SemiAutomatic Video Object Segmentation in the Presence of Occlusion,” IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 4, pp. 624– 629, 2000.

[10] M. Hariadi, H. Loy, and T. AOKI, “SemiAutomatic Video Object Segmentation using LVQ with Color and Spatial Features,” IEICE Trans. Inf. Syst., vol. E88D, no. 7, pp. 1553– 1560, 2005.

[11] Y. Tsaig and A. Averbuch, “Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 7, pp. 597–612, 2002.

[12] H. Li and K. Ngan, “Automatic Video Segmentation and Tracking for ContentBased Applications,” Adv. Vis. Content Anal. Adapt. Multimed. Commun., pp. 27–33, 2007.

[13] T. Meier and K. Ngan, “Automatic Segmentation of Moving Objects for Video Object Plane Generation,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 525–538, 1998.

[14] A. Levin, D. Lischinski, and Y. Weiss, “A Closed-Form Solution to Natural Image Matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 1–15, 2008.

[15] A. Levin, A. Rav-Acha, and D. Lischinski, “Spectral Matting,” Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1699–1712, 2008.

[16] B. Arroh, “Block Matching for Motion Estimation,” IEEE DIP 6620 Spring 2004 Final Project Paper, 2004.

[17] C. Yong-Sheng, H. Yi-Ping, and F. ChiouShann, “Fast Block Matching Algorithm Based on the Winner-Update Strategy,” IEEE Trans. Image Process., vol. 10, no. 8, 2001.

[18] H. Seung-Ryong, T. Yamasaki, and K. Aizawa, “Time-Varying Mesh Compression Using an Extended Block Matching Algorithm,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 11, pp. 1506–1518, 2007.

[19] H. Jiang, G. Zhang, H. Wang, and H. Bao, “Spatio-Temporal Video Segmentation of Static Scenes and Its Applications,” IEEE Trans. Multimed., vol. 17, no. 1, pp. 3–15, 2015.

[20] N. Apostoloff and A. Fitzgibbon, “Bayesian Video Matting Using Learnt Image Priors,” in IEEE Conf. Computer Vision and Pattern Recognition, 2004.

[21] Y. Chuang, A. Agarwala, B. Curless, D. Salesin, and R. Szeliski, “Video Matting of Complex Scenes,” ACM Trans. Graph., vol. 21, no. 3, pp. 243–248, 2002.

[22] J. Sun, J. Jia, C. Tang, and H. Shum, “Poisson Matting,” ACM Trans. Graph., vol. 23, no. 3, pp. 315–321, 2004.

[23] R. S. Basuki, M. Hariadi, E. M. Yuniarno, and M. H. Purnomo, “Spectral-Based Temporal-Constraint Estimation for SemiAutomatic Video Object Segmentation,” Int. Rev. Comput. Softw., vol. 10, no. 9, pp. 959– 965, 2015.

[24] T. Porter and T. Duff, “Compositing digital images,” Comput. Graph. (ACM)., vol. 18, no. 3, pp. 253–259, 1984.