新影像邊緣偵測法與基於影像邊緣特徵之浮水印技術

A New Edge Detection Method and Watermarking Technique Based on Image Edge Features

任佳珉(Jia-Min Zen)

國立虎尾科技大學/管理學院/資訊管理研究所/碩士(2007年)

https://doi.org/10.6827/NFU.2007.00010

摘要

邊緣是影像中常見的特徵之一，只要能夠取得邊緣完整資訊就可以製造出鮮明的影像輪廓圖；而浮水印則是一種應用在扮演保護影像著作權資訊的技術。本論文提出一基於向量量化索引表上的邊緣特徵以及資料探勘中關聯法則概念之強韌型浮水印技術。此外，本論文也提出植基於全新滑動視窗技術之二種不同的影像邊緣偵測方法。第一個方法是藉由定義在向量量化索引表上的4項目集之關聯法則，分別對原始影像及浮水印來探勘出邊緣特徵資訊。爾後，再將從浮水印所探勘出之關聯法則嵌入至原始影像的關聯法則後，也就達到保護影像智慧財產權之目的。
第二個方法是基於滑動視窗技術來偵測出影像像素值變化之邊緣偵測方法，其精神為利用影像邊緣明顯劇變之處來將像素值納入到大小不一的子視窗裡，並將此概念擴展至從影像的四個不同方向來找出邊緣特徵，便可產生四個具方向性之邊緣地圖。爾後，利用八個邊緣篩選器從中選取出可能的邊緣，並經由投票法，影像邊緣輪廓便油然而生。
第三個方法則是以相同的概念：浮動式大小之滑動視窗技術，來提出一影像邊緣偵測方法並改善上述所提及之偵測器。沿著不同的滑動方向，浮動視窗的大小會隨著像素值及其同樣滑動方向之周圍像素值來做調整。基於此概念並從影像八個不同的滑動視窗方向偵測出各方向的影像地圖。接下來，不同方向生之影像地圖中的某些邊緣特徵會被視為可能的影像邊緣，而較長的影像邊緣則會組成具方向性之影像邊緣概略輪廓。在將某些影像邊緣概略輪廓圖執行基本邏輯運算-或（OR）之後，便可以產生影像輪廓圖。
根據相關實結果證實，本論文所提出的第一個技術可有效抵抗一般幾何攻擊且任何比原圖還大且複雜的浮水印，本方法也可將之藏匿在原圖中。此外，本論文所提出之二種影像邊緣偵測技術，也可以減少雙邊緣及斑點的情況發生。

並列摘要

Edge is one of common features in images. By taking the complete information of edges in an image is to generate the sharp profile of the object. Watermarking is a technique that plays a role as the protector for the image’s copyright information. In this thesis, we propose a robust watermarking technique based on edge features in vector quantization (VQ) index table and the concept of association rules in data mining. Besides, we also present two edge detection approaches using a novel sliding window technique. The first scheme is to define 4-itemset association rules in VQ index table to explore some edge characteristics such that association rules of a watermark and an original image can be obtained, respectively. Subsequently, by embedding association rules of a watermark into association rules of an original image, the purpose for protecting intellectual property rights is achieved.
The second scheme is to apply sliding window technique to explore the variations of image pixels for edge detection. All pixels in an image will be included into different sub-windows with different size according to the heterogeneities of edges. After performing this concept on image pixels to explore edge features from four variant directions, four different directional edge maps are generated. Afterwards, eight edge selectors are utilized to sieve out possible edges, and the final edge image is determined by voting.
The third scheme also applies the same concept, sliding window technique with dynamic size, to propose an edge detection approach and improve the second scheme. Along different sliding direction, the window size will be modified according to the variation of a pixel and its neighboring pixels in the same sliding direction. This concept is utilized to generate eight directional edge maps from eight different sliding directions. Subsequently, directional edge candidates can be obtained by regarding some pixels in those edge maps as edges, and from each directional edge candidate, some connective edge candidates will be considered to be edges so as to generate a directional edge rough. The final edge image is created by performing the basic logical operation, OR, to some directional edge roughs.
Relevant experimental results demonstrate that our proposed robust watermarking technique can survive geometric operation attacks, and any a complex watermark with size larger than an original image also can be implemented. Besides, the other two schemes applied to detect edges also can achieve better results with less double edges and speckles occurring.

參考文獻

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