臺灣博碩士論文加值系統

影像分割技術是電腦視覺最重要的基礎之一，舉凡圖像檢索、圖形辨識、機器視覺等領域，要先有好的分割技術才能有效進行後續的檢索與辨識工作。傳統的影像分割方法主要根據影像中的彩色資訊為基礎，但隨著平價RGB-D攝影機日益普及，讓我們有了新的影像分割方式。本文採用Kinect攝影機所得到的彩色與深度資訊搭配來進行影像分割，首先對彩色影像進行初步分割，接著使用彩色搭配深度資訊來作鄰近區塊的合併以得到最終的分割成果。藉由深度資訊來彌補以往只單靠顏色作分割的不足，並得到效果合宜的成果。

Image segmentation is one of the most important foundations of computer vision. In many applications such as image retrieval, pattern recognition, machine vision and related fields, it is necessary to have a good segmentation technology to facilitate the follow-up retrieval and recognition work. Traditional image segmentation methods are mainly based on the color information in images. With the growing popularity of cheap RGB-D cameras, let us have a new method to do image segmentation. This study uses Kinect camera to get color and depth information for image segmentation. First, image is initially segmented according to color information, followed by the use of color and depth information for the merge of adjacent blocks to get the final segmented results. Use the depth information to make up for the past only color for the lack of segmentation to get the effect of appropriate results.

目 錄 I
圖目錄 III
表目錄 IV
誌謝 V
摘要 VI
Abstract VII
Chapter 1 簡介 1
1.1 研究背景 1
1.2 研究動機與目的 3
Chapter 2 文獻探討 4
2.1 Kinect感測原理 4
2.2 RGB影像分割方法 6
2.2.1分水嶺分割法 6
2.2.2區域生長法 8
2.2.3分離與合併區域分割法 9
2.2.4顏色重心和彩色圖像聚類分割法 10
2.2.5 K-means和人工魚群算法應用於圖像分割法 11
2.2.6 超像素分割方法 11
2.3 RGB-D影像分割方法 14
2.3.1 圖像標記分水嶺分割法 14
2.3.2多層聚類圖像分割法 15
Chapter 3 研究方法與步驟 16
3.1 色彩空間轉換 17
3.2 K-means分群 18
3.3 區塊合併 20
Chapter 4 實驗結果 22
Chapter 5 結論與未來展望 29
參考文獻 30

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