A Phase Correlation Approach to Active Vision
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 3691)
In this paper, dealing with the case of large movements in active vision applications, we first develop an algorithm to estimate the motion of an object and its background. Furthermore, with the assumption of small translations between successive frames, we develop an active tracking algorithm. Its main advantage is that an area-based projection method is presented which resorts to an area integral. Thus, it becomes more robust to the translation distortion of the Log-polar image. In addition, the rotation and scaling estimates can be fulfilled in the spatial domain and not in the frequency domain. Thus, the intrinsic drawbacks of the discrete Fourier transform, such as rotationally dependent aliasing and spectral leakages, can be avoided in our case. Our novelty consists in the introduction of the normalized phase correlation approach in our two algorithms. Because this approach does not rely on the smoothness or differentiability of the flow field in a sequence, it makes the large movement estimation possible. The experimental results show that the motions of object and background can be effectively estimated and a moving object can be tracked using our proposed algorithm in an image sequence.
KeywordsMotion Estimation Image Watermark Linear Phase Active Vision Successive Frame
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer-Verlag Berlin Heidelberg 2005