Research On The Classification Of Ancient Porcelain Shards Based On The Feature Of Digital Image

The research on cultural heritage protection with information technology, which belongs to intersectant research area of computer science and archeology, has become a hotspot in recent years. Chinese porcelain is one of important elements in archaeological discoveries, and porcelain has the characteristics of brittle, so many remains are discovered in the form of pottery fragments. During archaeological excavations, because a large number of porcelain shards usually mix up on the spot, there are a lot of difficulties in the artificial porcelain repair and classification management. In this paper, the extraction and application of the digital images characteristics has been researched penetratingly, and shards digital image mode is classified rightly according to the features such as color, texture and ornamentation shape in order to provide the assistant means contributing to shards automatic classification. This research is supported by national natural science foundation. The major advances are as following:(1) The paper proposes a simple adaptive smoothing and enhancement algorithm of digital image processing, which can realize the coinstantaneous processing of interior smoothing and edge enhancement of target area in image, strengthening the visual effect on the processed image.(2)This paper improved the algorithm of color pair wise clustering based on RGB so that the time complexity is promoted from O(n~2) to O(nlogn). Meantime, a new color un-equidistribution quantization method in HSI color space is defined to extract the color feature and apply to shards' classification. As a result, the method can achieve good effect.(3)In this paper, the extraction technology has been research on three aspects: structuring method, statistical method and frequency-domain transformation method. Methods based on primitive texture, gray level co-occurrence matrix, autocorrelation function, edge frequency, two-dimensional histogram and Gabor transform are realized. New color-texture model and method for the feature extraction are proposed to integrate color information and texture information effectively. Comparing with the existing methods, the new method can promote accuracy of shards' classification remarkably.(4) This paper puts forward a new segmentation algorithm for color image,which is called KFCM, that is based on image's color-texturetexton feature, kernel function and fuzzy clustering method. The algorithm can realize the effective segmentation for color image. Through the algorithm, shard ornamentation shape can be divided accurately so that the shape feature of ancient shards may be presented effectively..(5) Using support vector machine classification technology, the recognition performance about ancient porcelain shard's color, texture and ornamentation shape feature has been tested, analyzed and compared. Combing Matlab with Vc, an ancient shards classification prototype system based on the characteristics of digital image is developed as a assistant platform for automatic classification of ancient porcelain.