Method of marking diseases in ancient murals on basis of global dictionary features

Abstract

The invention discloses a method of marking diseases in ancient murals on the basis of global dictionary features. The method includes the steps of by applying a dictionary training method to an original mural image, according to a pre-marked true value image, subjecting a disease area and a no-disease image to online dictionary learning to train a disease dictionary and a non-disease dictionary; super-pixelating the target image by means of a super-pixel method; creating Bayesian models to mark each super-pixel block. Images can be more quickly and effectively segmented by means of super-pixelating and sparse coding; the identical Bayesian models are used to segment diseases in the ancient mural images, and the sparse coding based on super-pixels helps increase algorithm operating speed by 103 times, approximating real-time segmenting.

Description

technical field [0001] The invention relates to the fields of computer image processing and pattern recognition, in particular to a method for identifying diseases of ancient murals based on global dictionary features. Background technique [0002] As one of the earliest forms of painting in human history, mural painting is the witness of human history and civilization and the carrier of ancient and modern cultural inheritance. With the passage of time, large-scale murals have been damaged due to various natural and human factors. Therefore, the protection of murals has always been an area of ​​exploration. Finding and taking targeted measures against different diseases is the key . The traditional manual drawing of diseases is obviously inefficient and difficult to store and update. How to better and more efficiently find the diseases of murals is of great significance to the protection of murals. At the same time, the continuous monitoring of each mural image can obtain ...

AI Technical Summary

Problems solved by technology

Its disadvantage is that it cannot describe the local distribution of colors in the image and the spatial position of each color, that is, it cannot describe a specific object or object in the image.

At this time, existing technologies such as Deformable Parts Model (DPM), Saliency Analysis (Saliency) [6] , Region Merge and other general detection techniques are not suitable for this field

At the same time, in the existing detection-segmentation problem, well-performing detection algorithms such as sparse coding have the problem of extremely slow speed. Therefore, the present invention proposes an improved sparse coding method (Sparse Coding) [3,4] , and applied to the specific problem of ancient mural disease detection and segmentation, greatly improving the detection results

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