首页if band > 1 and band < bands and np.sum((band_data == 2) & (prev_band == 0) & (next_band == 0)) > 0: selected_bands.append(band)解释这段的代码

if band > 1 and band < bands and np.sum((band_data == 2) & (prev_band == 0) & (next_band == 0)) > 0: selected_bands.append(band)解释这段的代码

时间: 2024-05-26 18:12:24 浏览: 39

colourhistogram.rar_CBIR.fig_DWT.fig_cbir_cbir dwt_feature band

Colourhistogram II. TEXTURE FEATURE EXTRACTION IN CBIR An overview of the proposed CBIR system is illustrated in Fig. 1. The proposed algorithm, Label Wavelet Transform (LWT), is based on color image segmentation [1], and it is an extension of DWT-based texture feature extraction method. The 2-D DWT is computed by applying separable filter banks to the gray level images. The detail images Dn,1, Dn,2, and Dn,3 are obtained by band-pass filtering in a specific direction, and they can be categorized into three frequency bands: HL, LH, HH band, respectively. Each band contains different directional information at scale n. The texture feature is extracted from the variance (ó2 n,i) of the coefficients cn,i of the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To represent the texture feature of an image q, the texture feature vector of DWT is defined as [2]: TDWT (q) = [ó2 1,1, ó2 1,2, ó2 1,3, ..., ó2N max,3], (1) where Nmax denotes the largest scale. In this work, Nmax

I'm sorry, but your question seems to be incomplete. It ends abruptly with an incomplete expression. Could you please provide more context or complete the expression so that I can better understand your question and provide an appropriate response?

阅读全文