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研究生:余佩珊
研究生(外文):Pei-Shan Yu
論文名稱:基於深度學習之半自動化顯著物偵測及切割
論文名稱(外文):Semi-Automated Salient Object Detection and Segmentationusing Deep Learning
指導教授:陳敦裕陳敦裕引用關係
指導教授(外文):Duan-Yu Chen
口試委員:康立威魏志達
口試委員(外文):Li-Wei KangJyh-Da We
口試日期:107-06-22
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:24
中文關鍵詞:超像素
外文關鍵詞:superpixels
相關次數:
  • 被引用被引用:0
  • 點閱點閱:181
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  • 收藏至我的研究室書目清單書目收藏:0
隨著科技的發展,人工智慧在各領域都有著不同的進展,舉凡智慧交通、智能家居、智能機器人等處處都能看到人工智慧的蹤跡。在人工智慧中,訓練數據的收集一直是不可或缺的一部分,在處理訓練數據時,都必須要有正確的數據標記,所以在物件的切割部分一直以來也被視為重要的研究主題之一。其中,在多數切割方法中,要求全部的training data 都必須要標記過,但在未標記過的data 則無法被切割出來。
本文利用卷積神經網路對超像素(super-pixel)提取目標物的特徵,以模擬圖中目標物的顯著性。接著採用region growing的方式,將目標物的切割結果手動的微調,以達到更符合目標物的切割,最後將目標物的切割結果及標籤存取起來,如此一來可以方便且有效的切割任意想要切割的物品。
With the development of science and technology, artificial intelligence has made different progress in various fields, such as smart transportation, smart home, intelligent robots and so on. In artificial intelligence, the collection of training data has always been an important part. When training data pre-processing, it is necessary to have the correct data mark, so the segmentation part of the object has always been regarded as one of the important research topics. Among them, in most segmentation methods, all training data must be marked, but unmarked data cannot be segment.
In this paper, the convolutional neural network is used to extract the features of the object from super-pixel to obtain salient object. Then using the region growing method to manually fine-tune the segment result of the target to achieve a more consistent segmentation of the target. Finally, the segmentation result and the label of the target are saved, so that any item can be more conveniently and efficiently to segment.
摘 要 .. iii
ABSTRACT .. iv
Table of Contents .. vi
List of Figures .. vii
List of Tables .. viii
Chapter 1. Introduction .. 1
Chapter 2. 研究方法 .. 3
2.1 Overview .. 3
2.2 Superpixels[21] .. 4
2.3 Convolution Neural Network for Saliency Object Detection .. 7
2.4 Region Growing .. 9
Chapter 3. Experimental Results .. 12
3.1 Overview .. 12
3.2 成效評估 .. 12
3.2 使用介面介紹 .. 19
Chapter 4. Conclusion .. 22
Reference .. 23
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[ 11]S. Goferman, L. Zelnik-Manor and A. Tal, "Context-Aware Saliency Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1915-1926, Oct. 2012.
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[ 16] K. He, G. Gkioxari, P. Doll#westeur034#r and R. Girshick, "Mask R-CNN," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2980-2988. [ 17] Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham
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