Sensitivity of ALIAS to small variations in the dimension of fractal images

Based on collective learning systems theory, a transputer-based parallel processing image processing engine, known as ALIAS (adaptive learning image analysis system) has been applied to a difficult image processing problem: the detection of anomalies in otherwise normal images. To test its ability to detect small differences in the complexity of similar images, ALIAS was trained on a set of nondeterministic self-affine fractal images of dimension 2.10, and then tested with five unique sets of fractal images of dimension 2.12, 2.14, 2.16, 2.18, and 2.20. Formal experimental results revealed that ALIAS easily detected the difference between control images of fractal dimension 2.10 and test images of fractal dimension greater than 2.16. Informal observations suggest that this difference cannot be easily detected by the human eye