【摘要】：對古建筑圖像中對破損點的優(yōu)化提取,對古建筑后續(xù)原貌恢復(fù)具有重要意義。對圖像破損點的提取,需要獲得破損點分量特征信息,計算圖像的破損點超像素級視覺特征,完成圖像中破損點優(yōu)化提取。傳統(tǒng)方法保留破損圖像關(guān)鍵區(qū)域特征點,刪除古建筑圖像背景區(qū)域特征點,但忽略了計算圖像的破損點超像素級視覺特征,導(dǎo)致提取精度偏低。提出基于小波閾值自適應(yīng)修正的古建筑圖像破損點提取方法�；赗AC約束的兩步法來標(biāo)定系統(tǒng),對圖像進行灰度化轉(zhuǎn)換,進行圖像的邊緣檢測和小波降噪處理,對小波降噪提純后的古建筑圖像進行破損點深度超像素特征分割,獲得破損點分量特征信息,計算圖像的破損點向量量化區(qū)域的超像素級視覺特征,實現(xiàn)破損圖像破損點提取優(yōu)化。仿真證明,所提方法從視覺對比和量化分析的角度實現(xiàn)了破損區(qū)域的輪廓分割與檢測,破損點提取效果優(yōu)越。
[Abstract]:The optimum extraction of the damage points in the image of ancient buildings is of great significance to the restoration of the original features of the ancient buildings. In order to extract the broken points of images, we need to obtain the feature information of the components of the broken points, calculate the hyperpixel-level visual features of the broken points of the image, and complete the optimal extraction of the broken points in the image. The traditional method preserves the key region feature points of damaged images and deletes the feature points of the background region of ancient building images, but neglects the hyperpixel-level visual features of the damaged points of the calculated images, which leads to the low extraction accuracy. Based on wavelet threshold adaptive correction, a method for extracting damage points of ancient building image is proposed. A two-step method based on RAC constraint is used to calibrate the system. The image is grayscale transformed, the image edge detection and wavelet denoising processing are carried out, and the wavelet de-noising purified ancient building image is segmented by the ultra-pixel feature of the depth of damage point, which is based on the wavelet de-noising and purification. The feature information of the broken point component is obtained, and the hyperpixel level visual feature of the vector quantization region of the broken point is calculated to realize the optimization of the broken point extraction of the damaged image. Simulation results show that the proposed method achieves the contour segmentation and detection of damaged areas from the visual comparison and quantitative analysis, and the extraction effect of damage points is superior.
【作者單位】： 長春大學(xué)旅游學(xué)院;
【基金】：吉林省教育廳“十三五”社會科學(xué)研究規(guī)劃項目(吉教科文合字JJKH20171024SK)
【分類號】：TP391.41;TU-87

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