Abstract
Non-dense image correspondence estimation algorithms are known for their speed, robustness and accuracy. However, current evaluation methods evaluate correspondences point-wise and consider only correspondences that are actually estimated. They cannot evaluate the fact that some algorithms might leave important scene correspondences undetected - correspondences which might be vital for succeeding applications. Additionally, often the reference correspondences for real world scenes are also sparse. Outliers that do not hit a reference measurement can remain undetected with the current, point-wise evaluation methods. To assess the quality of correspondence fields we propose a histogram based evaluation metric that does not rely on point-wise comparison and is therefore robust to sparsity in estimate as well as reference.
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Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. IJCV 92, 1–31 (2011)
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. JMIV 40, 120–145 (2011)
Bruhn, A., Weickert, J.: A confidence measure for variational optic flow methods. In: Geometric Properties for Incomplete Data, pp. 283–298 (2006)
Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: CVPR, vol. 2, pp. 807–814 (2005)
Kondermann, C., Kondermann, D., Jähne, B., Garbe, C.: An Adaptive Confidence Measure for Optical Flows Based on Linear Subspace Projections. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 132–141. Springer, Heidelberg (2007)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)
Schill, F., Mahony, R., Corke, P.: Estimating Ego-Motion in Panoramic Image Sequences with Inertial Measurements. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. STAR, vol. 70, pp. 87–101. Springer, Heidelberg (2011)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. IJCV 12, 43–77 (1994)
Klette, R., Kruger, N., Vaudrey, T., Pauwels, K., Van Hulle, M., Morales, S., Kandil, F., Haeusler, R., Pugeault, N., Rabe, C.: Performance of correspondence algorithms in vision-based driver assistance using an online image sequence database. IEEE T-VT, 1 (2011)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: CVPR, vol. 1, p. I–195. IEEE (2003)
Morales, S., Klette, R.: Ground Truth Evaluation of Stereo Algorithms for Real World Applications. In: Koch, R., Huang, F. (eds.) ACCV 2010 Workshops, Part II. LNCS, vol. 6469, pp. 152–162. Springer, Heidelberg (2011)
Reulke, R., Luber, A., Haberjahn, M., Piltz, B.: Validierung von mobilen Stereokamerasystemen in einem 3D-Testfeld. In: 3D-NordOst, vol. 12 (2009)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? In: CVPR, Providence, USA (2012)
Szeliski, R.: Prediction error as a quality metric for motion and stereo. In: ICCV, vol. 2, pp. 781–788. IEEE (1999)
Stein, F.J.: Efficient Computation of Optical Flow Using the Census Transform. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 79–86. Springer, Heidelberg (2004)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of the Conf. on Art. Intelligence (1981)
Liu, Z., Klette, R.: Approximated Ground Truth for Stereo and Motion Analysis on Real-world Sequences. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 874–885. Springer, Heidelberg (2009)
Steingrube, P., Gehrig, S.K., Franke, U.: Performance Evaluation of Stereo Algorithms for Automotive Applications. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 285–294. Springer, Heidelberg (2009)
Stricker, M., Orengo, M.: Similarity of color images. In: Proc. SPIE Storage and Retrieval for Image and Video Databases, vol. 2420, pp. 381–392 (1995)
Rubner, Y., Tomasi, C., Guibas, L.: A metric for distributions with applications to image databases. In: ICCV, pp. 59–66. IEEE (1998)
Zhang, Y.: Solving large-scale linear programs by interior-point methods under the matlab environment. Technical Report TR96-01, Department of Mathematics and Statistics, University of Maryland (1995)
Sellent, A., Lauer, P.S., Kondermann, D., Wingbermühle, J.: A toolbox to visualize dense image correspondences. Technical report, Heidelberg Collaboratory for Image Processing, HCI (2012)
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Sellent, A., Wingbermühle, J. (2012). Quality Assessment of Non-dense Image Correspondences. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_12
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DOI: https://doi.org/10.1007/978-3-642-33868-7_12
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