关键词: 高帽—低帽变换, 不完全树形小波, 一致性判别, 双概率神经网络

Abstract: Low operation speed and being only fit for high quality images are the disadvantages of incomplete tree-structured wavelet. To deal with this problem, a new algorithm was proposed. Firstly, pre-processment using tophat-bothat was done to clear noise and to enhance contrast degrees; then the feature consistency was extracted. If its value was high, only one part of the image would be used in incomplete tree-structured wavelet. Otherwise, the whole image would be used. Lastly, Double Probabilistic Neural Network (DPNN) ws adopted here to identify images. Brodatz database was used for simulation, and the algae images pictured at scene was used as an application of this method. Result shows that this algorithm is fast in feature extraction and identification, with especially good performance at low quality images.

Key words: tophat-bothat transform, incomplete tree-structed wavelet, consistency judgment, Double Probabilistic Neural Network (DPNN)