4 April 2001 Fourier-Hankel transform used in image raw data preprocessing
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Abstract
For a raw picture data set in either binary or gray-scaled digital form, we can first apply a pixel-quantization method to condense the picture to a much smaller file. Then we can use a math-graphic program such as Microsoft Visual Basic to compute its center of mass (CM). From this CM, we can then construct a polar coordinate with M sectors and N rings. If we apply a normalized Magnitude Fourier Transform to these M sectors and a normalized Hankel transform to these N rings, we will obtain two numerical series truncated at P and Q terms (e.g., P equals Q equals 16). We can then construct a P+Q (or 32) dimension ANALOG vectors. This vector may be used as the pre-processed image vectors for feeding to any neural network (including the noniterative neural networks we presented in the last 8 years) for training and learning.
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Chia-Lun John Hu, "Fourier-Hankel transform used in image raw data preprocessing", Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001); doi: 10.1117/12.420943; https://doi.org/10.1117/12.420943
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