Optimized size-adaptive feature extraction based on content-matched rational wavelet filters

One of the challenges of feature extraction in image processing is caused by the fact that objects originating from a feature class don't always appear in a unique size, and the feature sizes are diverse. Hence, a multiresolution analysis using wavelets should be suitable. Because of their integer scaling factors classical dyadic or M-channel wavelet filter banks often don't match very well the corresponding feature sizes occurring within the image. This paper presents a new method to optimally extract features in different sizes by designing a rational biorthogonal wavelet filter bank, which matches both the features' characteristics and the significant sizes of the most dominant features' sizes. This is achieved by matching the rational downsampling factor to the different feature sizes and matching the filter coefficients to the feature characteristics. The presented method is evaluated with the detection of defects on specular surfaces and of contaminations on manufactured metal surfaces.