Cluster segmentation method for ancient architecture wall inscription contaminated writing brush character image

Abstract

The invention discloses a cluster segmentation method for ancient architecture wall inscription contaminated writing brush character images, which belongs to the field of ancient architecture digital repair. The cluster segmentation method comprises the steps of: constructing a partial differential model for denoising an acquired image, and carrying out block-based enhancement according to illumination characteristics of the inscription image; segmenting the enhanced image by utilizing a maximum between-class variance method, and carrying out morphological processing on the image; carrying out regional positioning on the processed image to obtain minimum enclosing rectangles of character regions, and marking the corresponding character regions in the enhanced image; and finally, carrying out first FCM clustering on the character regions to determine a clustering central matrix, restraining a membership degree by utilizing an average grey degree similarity and a distance punishment function, and carrying out NKFCM clustering and deblurring processing to obtain a final cluster segmentation image. The cluster segmentation method can effectively eliminate influence of noise on clustering, can maintain the segmentation integrity, and can extract inscription characters with high quality. The cluster segmentation method is mainly used for clustering segmentation of ancient architecture wall inscription contaminated writing brush characters.

Description

technical field [0001] The invention belongs to the field of digital restoration of ancient buildings, and specifically relates to a clustering and segmentation method for image clustering and segmentation of dirty brush characters on ancient wall inscriptions, which can not only filter out the noise caused by pollution, but also ensure the complete segmentation of the main body of characters. Background technique [0002] The brush writing on the wall inscriptions of ancient buildings carries a large amount of historical information and is of great value in calligraphy and historical research. However, due to natural weathering and man-made damage, and some inks are decomposed and processed from animal and plant remains, they are prone to mildew, and the inscriptions have been polluted to varying degrees, and some are even difficult to identify. Traditional physical decontamination and restoration are easy to remove while removing pollution. Causes damage to the text body a...

AI Technical Summary

Problems solved by technology

The threshold-based clustering algorithm has a strong dependence on the selection of the threshold, and the selection of the threshold is greatly affected by noise, and the robustness of the algorithm is poor; the model-based clustering algorithm can make full use of the characteristic information of the text, but the training model is limited The amount of sample data required is large, and the prior sample requirements are high, which is not suitable for small sample ancient text clustering; partition-based clustering algorithms, such as the k-means algorithm, can better eliminate differential noise for text segmentation However, the noise filtering effect of blocky pollution with similar gray levels is not good, and the segmented text body is incomplete.

The above method has a better clustering effect on inscription images with less pollution in the text area, but when the pollution seriously affects the main body of the text, the clustered text may have defects or contain more noise

Images