English  |  正體中文  |  简体中文  |  Items with full text/Total items : 78111/78111 (100%)
Visitors : 30608340      Online Users : 401
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77591

    Title: 基於樣本自動化修補與形變之組合式卡通圖像創作系統;Automatic Cartoon Image Creation With Inpainting And Deformation
    Authors: 郭宇航;Kuo, Yu-Hang
    Contributors: 資訊工程學系
    Keywords: 圖像創作;形變;影像組合;影像修補;影像處理
    Date: 2018-07-20
    Issue Date: 2018-08-31 14:49:12 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 人工智慧是電腦科學領域近年廣泛討論的議題之一,透過的對訓練資料擷取特徵學習,使用機器學習自主創作的技術也蓬勃發展,其中發展最佳的領域莫過於圖像生成。
    本篇論文著重於自動創作生成卡通圖像,已將同種類的訓練資料透過分割、分群並取得區域部位之間的鄰近相連關係(Region Relationship Graph)為前提,後續將各個區域部位(Region)實作修補、形變、組合等等,創作出全新的圖像,相較於生成對抗網路(Generative Adversarial Network,GAN)使用深度學習的神經網路,本篇採用影像處理的方法實作,較能在運算時間、資料集數量與硬體資源上取得優勢。
    ;Artificial intelligence is one of the topics that have been widely discussed in the field of computer science in recent years. Through the acquisition of feature learning from training data and using machine learning to create, the technology has also flourished. Among them, the best field for development is image generation.
    This paper focuses on the automatic creation of cartoon images. It has premised that the same kind of training data is divided, grouped, and acquired the region relationship graph. Subsequent implementation of each region. Patching, deformation, assembling, and so on, create new images. Compared to the neural network that uses deep learning in Generative Adversarial Network (GAN), this paper adopts image processing method to implement it. Take advantage of computing time, data sets, and hardware resources.
    The system proposed in this paper is divided into three stages. Considering that Region and Region in the original input image have covering effects, the segmented region will have shadowed depressions. The first stage of the system is to inpaint each region. For the diversity of creation, the second stage is deforming the regions. Finally, a template is randomly selected, and the modified Region is assembled and adjusted. The user can also adjust the parameters. The random parameters of the system and the user′s parameters will cross the countless combinations, creating a new image.
    It can be seen in the experiment that if using a few training data, they can have new creations, and more training materials can create more diversity. The results can also identify objects of the same kind as the training data.
    Appears in Collections:[資訊工程研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明