Correlation and relaxation labelling: an experimental investigation on fast algorithms
This paper compares experimental results between three popular matching functions: the cross-correlation coefficient (CCC); the sum of squared difference (SSD); and sum of the absolute value of difference (SAVD), within our newly developed correlation-relaxation (C-R) framework (Wu 1995). The C-R framework is a general method for determining optical flow and has been applied to determining cloud motion from satellite images. SSD and SAVD are simpler and faster functions to calculate, when compared with CCC, and their uses can lead to significant savings in computer time in the initial selection of displacement candidates. Given that the image distortion is Gaussian noise, and the motion is translational, the study shows that while computationally more expensive, the performance of the CCC function is better, or at least no worse, than using SSD and SAVD in the selection of initial displacement candidates. Similarly, the performance of SSD is better, or no worse, than using SAVD. Computationally, SSD is the fastest among the three functions. In the presence of high level distortion, however, the poor quality of initial candidates selected using SSD and SAVD usually means a large number of iterations of the subsequent relaxation labelling process. In contrast, the CCC function gives high quality initial candidates, and only a small number of iterations are needed. The CCC function also usually leads to better final quality in motion estimations than that produced using the SSD or the SAVD function in the C-R algorithm. In the presence of moderate and low level distortion, however, the performance of SSD can be adequate, and its use can lead to faster processing without much sacrifice to the overall motion estimation quality.
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