近年來，隨著電腦運算速度以及顯示卡性能的提升，電腦圖學的技術被廣泛應用在各個領域之中，也促進了影像處理相關研究的發展，而紋理拼接便是其中之一，從多媒體產業(如:A/VR、電影、3D 動畫)，到紡織、印刷、建材相關產業都有使用到紋理影像的需求，其目標都希望將小尺寸紋理影像透過演算法產生出大尺寸的紋理影像。早期演算法從以像素為主的拼接到以區塊為主的拼接方法，改善了在拼接上的速度以及紋理結構之間的銜接流暢度，而後期演算法如卷積神經網路(Convolutional Neural Networks，CNN)、生成對抗網路(Generative Adversarial Nets，GAN)，在近年也大量應用於紋理拼接的研究，然而，神經網路的方法需要大量的影像作為訓練集，且所花費時間成本也較高。
因此，本研究提出了一個基於區塊的紋理拼接演算法，利用α合成(Alpha Blending)以及最小誤差路徑切割(Minimum Error Boundary Cut)來消除區塊與區塊之間拼接時的不連續感，並加入KD-Tree及最鄰近搜索技術(Approximate Nearest Neighbor，ANN)來加速相似區塊的搜索及拼接過程，最終達到產生出任意大小的紋理影像的目的，且適用於結構及隨機紋理影像，並製作成應用程式供相關產業之專業人員使用。而在最後本研究也與經典演算法進行比較，並利用人因實驗做為評估、探討本研究的優勢以及需要改善的地方。

In recent years, with the improvement of computer computing speed and graphics card performance, computer graphics technology has been widely used in various fields, and it has also promoted the development of image processing related research. Texture stitching is one of them. From the multimedia Industry (such as: A/VR, Movies, 3D Animation), Textile, Printing, and Building materials industries all have the need to use texture images. Their goals are to generate large-size texture images from small-size texture images through algorithms. The early algorithm changed from Pixel-based to Patch-based, which improved the speed of stitching and the smoothness of the smoothness of the texture structure, while the later algorithms such as CNN (Convolutional Neural Networks), GAN (Generative Adversarial Nets), have also been widely used in the research of texture stitching in recent years. However, the neural network method requires many images as a training set, and the time cost is also higher.
Therefore, this research proposes a block-based texture stitching algorithm, which uses Alpha Blending and Minimum Error Boundary Cut to eliminate the sense of discontinuity when stitching between blocks. KD-Tree and the nearest neighbor search technology (Approximate Nearest Neighbor, ANN) are added to accelerate the search and stitching process of similar blocks, and finally achieve the purpose of generating texture images of any size, and make them into applications for professionals in related industries use. In the end, this research also compares with classic algorithm, and uses human factors experiments as evaluations to discuss the advantages of this research and areas for improvement.

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