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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65722

    Title: 模糊車牌字元預測使用超解析度影像重建技術;Blur License Plate Character Prediction Using Super-Resolution Based Image Reconstruction Technique
    Authors: 鄧名杉;Deng,Ming-Sang
    Contributors: 資訊工程學系
    Keywords: 車牌切割;車牌辨識;超解析度;影像還原;Vehicle license plate segmentation;vehicle license plate recognition;super-resolution;image reconstruction
    Date: 2014-07-29
    Issue Date: 2014-10-15 17:08:54 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 車牌影像的分析與應用是長久以來電腦視覺領域中一個重要的課題,在近年來,以此技術為基礎的應用正在蓬勃發展當中。舉例來說:道路監控、贓車追蹤、出入口監測等等,都是典型的應用。本篇論文除了一般狀況的車牌影像之外,更特別專注於透過超解析度技術來處理模糊車牌的案例。
    ;The research on vehicle license plate is an important issue in computer vision. In recent years, applications based on this technology are getting popular. For example, entrance monitoring system, road surveillance, suspicious vehicle investigation…etc. In our work, we are not only dealing with the normal case but also focusing on the blurred plate images using super-resolution technique. The purpose of this thesis is to design a vehicle license plate analysis and image reconstruction system that can overcome the following cases: blurred images, images with variation illumination intensity, and tiny target images.
    In this thesis, we adopt LBP as image feature and RBF neural network to perform feature translation in the first stage. After that, an algorithm for analyzing the image and performing the image reconstruction is proposed in the second stage. In image reconstruction stage, two-layer architecture is applied. In the first step, image reconstruction is performed recursively to obtain better reconstruction result. A new image will be generated each time and each new image will go through analysis and voting procedure recursively. In the second step, image reconstruction is further manipulated according to the voting result. The purpose of image reconstruction is to intensify the weak clue in the blurred image recursively.
    Three different experiments were conducted to verify the validity of our proposed method. The images in the first experiment are blurred but can be identified by human. We can apply the proposed method to reconstruct the original blurred images into clear and correct license plate images. The images of the second and third experiments are very blurred and cannot be identified by human. Here, we utilize the candidate ranking list for analyzing the accuracy. This list can help police to reduce the search space when performing the blurred license plate image matching for criminal investigation.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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