Effect of noise on model selection criteria in visual applications

In this paper, the effect of noise level on the performance of a large number of existing model selection criteria for two important computer vision applications (motion and range segmentation) has been investigated. The results of our experiments show that although the performance of all model selection criteria deteriorates by increasing the level of noise, the Surface Selection Criterion (SSC) remains the criterion of choice for different noise levels.