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1 Automatic Synthesis of Stitched Wide FOV Images for Reviewing FPV Abstract Videos Selecting and Grouping Images Based on Stitching Criteria Kenta MATSUI, Kazuaki KONDO, Takahiro KOIZUMI, and Yuichi NAKAMURA Kyoto University Yoshidahonmachi, Sakyo-ku, Kyoto-shi, Kyoto, Japan This paper reports a technique to automatically synthesize stitched wide FOV images from a first person view (FPV) video for reviewing it. For this purpose, we had proposed criteria to select suitable images for stitching. In this paper, we solve remained issues ; calculation cost and temporal segmentation for fully automatic synthesis. The proposed method approximately reduces the number of image combination (hypotheses) that must be evaluated for the image selection, based on pixel consistency among a small number of local images. The input video is temporally segmented by the image combination with highest stitching score, sequentially. Through the experiments to analyze degree of reduction and how many image combination with high score are dropped, we have confirmed that we can get enough well stitched wide FOV images from reduced hypotheses. Key words First person view video, Wide FOV images, video summarization 1. [1], [2] [3], [4] [5] [9]
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3 2. 2 ( 2) Q({y t}) = F (p(x {y t})) (1) p(x {y t }) = p(x)p({y t} x) p({y t }) Q x {y t} F ( ) {y t} {y t} (2) y = α(x + n(σ 1 )) + (1 α)u (3) x, y n(σ 1) u 0 < = α < = 1 i x(i) {y t(i)} p(x {y t }) = [ p(x(0) {y t (0)}) p(x(s) {y t (S)}) ] T (4) p(x(i) {y t (i)}) = p(x(i)) T { h(x(i), y t=1 t(i)) 1.0 y t(i) = φ h(x(i), y t (i)) = otherwise p(y t (i) x(i)) p(y t (i)) S T y t (i) = φ i t p(x(i)) = 1 X (3) p(y t (i) x(i)) = (5) αg(x(i) y t(i), σ 1) + (1 α) 1 G( ) X X F ( ) ( 3) x c x c q({y t}) = X p(x {y t})g(x x c, σ 2)dx (6) x c = argmax p(x {y t }) (7) σ 2 (6) S Q Q({y t }) = F (p(x {y t })) = S f(p(x(i) {y i t(i)})) = S log p(x(i) {y i t (i)})g(x(i) x c (i), σ 2 )dx X (8) σ 1, σ 2, α 3 σ 1, σ 2 (5) (6) [16] [19] p(x {y t }) (7) (6)
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6 0$*$1! 0$2$1! 0$3$1! 0$-$1! 0$.$1! 0$'$1!. "!. '!.+%/%#! 5! "# $%&#'()*+$! "# $%&#'()*+$. "!. '!.+%/%#!! "# $%&#'()*+$, &- $%&#'()*+$. "!!! f limit (1) N {I t } ( 5(a)) (2) 3-1 N N rep {I rep t } {I t } ( 5(b)) (3) f s = 0 (5(c)) (4) [I rep f s, I rep ], fe = max(f) s.t. f < f s +f limit f e ( 5(c)) (5) [I rep f s, I rep f e ] ( 5(d)) (i) {I abc } (ii) [I rep f s, I rep f e ] 1 I rep f s {I p } {I abc } (iii) {I p } I p Q(I p ) (iv) I best p = argmax Q(I p ) (6) f s = f latest + 1 ( 5(e)) f latest I p (7) (4)-(6) ( 5(e),(f)) (1) {I t } (2) {I rep t } {I t } (3) {I t }, {I rep t } 1 {I p }, {Ip rep } (4) {I p }, {Ip rep } 3 PointGrey IEEE σ 1 = 5, σ 2 = 30, α = 0.5 r f = 0.2, r b = 0.1 r rep = N({Irep p }) N({I p}) N( ) r th, w th 1 r th = w th 14 {I p }, {Ip rep } 10 () = ( ) 1 (5)
7 1 r th = w th r rep co th r tri Number of combinations original bin r rep bin Number of combinations original bin r tri bin (a) 6 (b) (a) 8 (b) 3 7 (a) (b) r th = w th = (c) r th = w th = (d) r th = w th = (1) {I t} (2) 3 {I abc } (3) {I t} 1 {I p} {I abc } {I tri p } (4) {I p}, {I tri } 50 r th = w th = r tri = N({Itri p }) N({I p }) p co th = 0.06 f limit = f limit = f limite 1 5.
![t!!"#$ %&$!"#$ t! %&$!"#$ %&$!"#$ 9 24680078 [1] K.Aizawa, S.Kawasaki, T.Ishikawa, and T.Yamasaki, Capture and retrieval of life log, in Proc.](/docs-images/91/106529975/images/8-1.jpg "of Int. Conf. on Artificial Reality and Telexistence (ICAT), pp. 49-55, 2004. [2] B. H. Prananto, I. Kim, and H.")
![[4],,,,,,,, D-II, vol 91, no. 1, pp. 65-77, 2008. [5] A. R. Doherty, A. F. Smeaton, K. Lee, and D. P.](/docs-images/91/106529975/images/8-3.jpg "Ellis, Multimodal Segmentation of Lifelog Data, In Proc. on RIAO2007 - Large-Scale Semantic Access to Content, 2007. [6],,,,, D-II, vol.")
![82, no. 10, pp. 1543-1551, 1999. [7] H. Luo, J. Fan, J. Yang, W. Ribarsky, and S.](/docs-images/91/106529975/images/8-4.jpg "Satoh, Exploring Large-Scale Video News via Interactive Visualization, IEEE Symposium On Visual Analytics Science And Technology (VAST2006),")
![pp. 75-82, 2006. [8],, DEIM2010 2, E-9, 2010. [9] C. Barnes, D. B. Goldman, E. Shechtman, and A.](/docs-images/91/106529975/images/8-5.jpg "Finkelstein, Video tapestries with continuous temporal zoom, Proc. of ACM SIGGRAPH2010, Vol. 29 Issue 4, No. 89,2010.")
![[10],,,,,, PRMU2013-32, pp. 77-82, 2013. [11],,,, - -,, MVE2013-21, pp. 17-22, 2013. [12] Y. Y. Schechner and S. K.](/docs-images/91/106529975/images/8-6.jpg "Nayar, Generalized Mosaicing: Polarization Panorama, IEEE transactions on PAMI, Vol. 27, No. 4, pp. 631-636. [13] A. Sibiryakov and M.")
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![[15] M. Brown and D. Lowe., Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision. vol.](/docs-images/91/106529975/images/8-8.jpg "74, No. 1, pages 59-73, 2007. [16] Michael E.Tipping and Christopher M.")
8 t!!"#$ %&$!"#$ t! %&$!"#$ %&$!"#$ [1] K.Aizawa, S.Kawasaki, T.Ishikawa, and T.Yamasaki, Capture and retrieval of life log, in Proc. of Int. Conf. on Artificial Reality and Telexistence (ICAT), pp , [2] B. H. Prananto, I. Kim, and H. Kim, Multi-level Experience Retrieval for the Personal Lifelog Media System, in Proc. of Third Int. IEEE Conf. on Signal-Image Technologies and Internet-Based System (SITIS), [3],,,,,, - -,, PRMU , pp , [4],,,,,,,, D-II, vol 91, no. 1, pp , [5] A. R. Doherty, A. F. Smeaton, K. Lee, and D. P. Ellis, Multimodal Segmentation of Lifelog Data, In Proc. on RIAO Large-Scale Semantic Access to Content, [6],,,,, D-II, vol. 82, no. 10, pp , [7] H. Luo, J. Fan, J. Yang, W. Ribarsky, and S. Satoh, Exploring Large-Scale Video News via Interactive Visualization, IEEE Symposium On Visual Analytics Science And Technology (VAST2006), pp , [8],, DEIM2010 2, E-9, [9] C. Barnes, D. B. Goldman, E. Shechtman, and A. Finkelstein, Video tapestries with continuous temporal zoom, Proc. of ACM SIGGRAPH2010, Vol. 29 Issue 4, No. 89,2010. [10],,,,,, PRMU , pp , [11],,,, - -,, MVE , pp , [12] Y. Y. Schechner and S. K. Nayar, Generalized Mosaicing: Polarization Panorama, IEEE transactions on PAMI, Vol. 27, No. 4, pp [13] A. Sibiryakov and M. Bober, Graph-based multiple panorama extraction from unordered image sets, in Proc. of SPIE 6498, Computational Imaging V, [14] M.Brown and D.G.Lowe, Recognising Panoramas, In Proc. of the 9th International Conference on Computer Vision. Nice, vol. 2, pp , [15] M. Brown and D. Lowe., Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision. vol. 74, No. 1, pages 59-73, [16] Michael E.Tipping and Christopher M.Bishop, Bayesian Image Super-resolution, In NIPS 15, MIT Press 2003, pp [17],,,,,, vol. 93, no. 9, pp , [18] G. K Chantas, N. P Galatsanos, N. A Woods, Superresolution based on fast registration and maximum a posteriori reconstruction, IEEE Transactions on Image Processing, vol. 16, no. 7, pp , [19],,, 24, 2D1-02, 2009.
(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
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