Abstract
The detection of basic events such as turning points in object trajectories is an important low-level task of image sequence analysis. We propose extending the SUSAN algorithm to the spatio-temporal domain for a context-free detection of salient events, which can be used as a starting point for further motion analysis. While in the static 2D-case SUSAN returns a map indicating edges and corners, we obtain in a straight forward extension of SUSAN a 2D+1D saliency map indicating edges and corners in both space and time. Since the mixture of spatial and temporal structures is still unsatisfying, we propose a modification better suited for event analysis.
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Martin, D.R., Fowlkes, C.C., Makik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Trans. on Pattern Analysis and Machine Intelligence vol. 26(1) (2004)
Reisfeld, D., Wolfson, H., Yeshurun, Y.: Context-Free Attentional Operators: The Generalized Symmetry Transform. Int. J. of Computer Vision 14, 119–130 (1995)
Goldberger, J., Greenspan, H.: Context-Based Segmentation of Image Sequences. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(3), 463–468 (2006)
Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. 4th Alvey Vision Conf., pp. 147–151 (1988)
Laptev, I., Lindeberg, T.: Space-time Interest Points. In: Proc. ICCV 2003. pp. 432–439 (2003)
Tian, Q., Sebe, N., Lew, M.S., Loupias, E., Huang, T.S.: Image Retrieval Using Wavelet-Based Salient Points. J. of Electronic Imaging 10(4), 835–849 (2001)
Backer, G., Mertsching, B., Bollmann, M.: Data- and Model-Driven Gaze Control for an Active-Vision System. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(12), 1415–1429 (2001)
Heidemann, G.: Focus-of-Attention from Local Color Symmetries. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(7), 817–830 (2004)
Privitera, C.M., Stark, L.W.: Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(9), 970–982 (2000)
Moravec, H.P.: Towards Automatic Visual Obstacle Avoidance. In: Proc. 5th Int’l Joint Conf. on Artificial Intelligence, Cambridge, Massachusetts, USA, pp. 584–587 (1977)
Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)
Smith, S., Brady, J.: SUSAN – A New Approach to Low Level Image Processing. Int. J. of Computer Vision 23(1), 45–78 (1997)
Zheng, Z., Wang, H., Teoh, W.: Analysis of Gray Level Corner Detection. Pattern Recognition Letters 20, 149–162 (1999)
Zitová, B., Kautsky, J., Peters, G., Flusser, J.: Robust detection of significant points in multiframe images. Pattern Recognition Letters 20(2), 199–206 (1999)
Heidemann, G., Kaiser, B., Bax, I., Bekel, H., Ritter, H.: Spatiotemporal Events and Action Sequences. Technical report, Bielefeld Univ., Neuroinformatics Group (2005)
Heidemann, G.: Unsupervised image categorization. Image and Vision Computing 23, 861–876 (2005)
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Kaiser, B., Heidemann, G. (2007). Context-Free Detection of Events. In: Ersbøll, B.K., Pedersen, K.S. (eds) Image Analysis. SCIA 2007. Lecture Notes in Computer Science, vol 4522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73040-8_23
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DOI: https://doi.org/10.1007/978-3-540-73040-8_23
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