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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94533


    Title: 特徵點穩定與匹配校正應用於自由視角即時監視系統
    Other Titles: Free view point real-time monitoring system base on feature point stabilizaion and matching correction
    Authors: 余方翊;Yu, Fang-Yi
    Contributors: 淡江大學電機工程學系碩士班
    李維聰;Lee, Wei-Tsong
    Keywords: 自由視角;即時成像;特徵點截取;特徵點匹配;Free-View point system;real-time image;feature point detection;feature point matching
    Date: 2013
    Issue Date: 2014-01-23 14:44:46 (UTC+8)
    Abstract:   隨著科技的進步,人們對於行動網路影像的需求也越來越高,如何能在行動終端上利用有限的硬體及網路資源來達到流暢的影像監控是許多研究人員的議題。本論文探討如何即時並準確的建立自由視角監視系統,並以行動終端展示的方式將實驗成果呈現。
      此外,本論文在即時成像系統中進行兩大部分的改善:第一部分為特徵點擷取於即時系統中進行穩定,於特徵點中加入強度值的概念,將出現頻率高的特徵點保存,剔除出現頻率低的特徵點,降低自然界雜訊引起特徵點晃動以及閃爍的程度,提高系統的穩定性;第二部分為特徵點匹配的演算法進行改良,計算特徵點的相對位置,並使用此資訊加入固有的特徵點匹配演算法,提高特徵點匹配的準確度。
      本論文最後將上述兩改良演算法與二維自由視角立體影像監視系統結合,並將系統細分成三大區塊,分別處理特徵點擷取、匹配運算、影像合成及壓縮傳輸三大部分。分割後的系統達到分散式運算的效果,大幅降低了運算時間,實作出自由視角即時監視系統。
    Due to Internet of things increasingly mature, future monitoring has become not only simply display 3D images, but also be able to interact with users. In this paper, we present a fast free view point monitor system without rebuild 3D module tardily.
    Images lost their depth information after they are captured by cameras, recalculating their coordinates in real world are inefficient and usually easily been distorted. In order to achieve the goal of free view point real-time processing, parallax of images become an very important information to us.
    Computing time plays an important role in free view point real-time monitor system. Tradition 3D modeling algorithms usually have high accuracy but low performance, speeding up the system is the first problem we face. Instead of reconstructing 3D models, we put our focus on simulating users’ point of view in our new algorithm. Our experimental environment requires multiple cameras focus on one object in different angle. After images are captured by cameras, we’ll find feature points on each image with Harris corner detector. The second step is matching these corner points, finding relations between different images. After matching feature points, the third step is triangle meshing. By using feature points as vertex, the images are segmented into several triangles. Meshed triangle images transformed into the user’s view point and recover texture on simulation image in the last step. System repeats step one to three until user has new view point commands.
    SURF is very good at handling scale changing and image twisting, but feature points found by SURF are no corners, without corner information, it is hard to simulate users’ view point. Harris corner detector is well known of its good performance and stabilization, that is why we combined these two algorithms in our research
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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