博碩士論文 106522087 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:10 、訪客IP:3.236.65.63
姓名 林翰廷(Han-Ting Lin)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 旅館場景影像自動分類系統
(Automatic Hotel scene image classification system)
相關論文
★ 影片指定對象臉部置換系統★ 以單一攝影機實現單指虛擬鍵盤之功能
★ 基於視覺的手寫軌跡注音符號組合辨識系統★ 利用動態貝氏網路在空照影像中進行車輛偵測
★ 以視訊為基礎之手寫簽名認證★ 使用膚色與陰影機率高斯混合模型之移動膚色區域偵測
★ 影像中賦予信任等級的群眾切割★ 航空監控影像之區域切割與分類
★ 在群體人數估計應用中使用不同特徵與回歸方法之分析比較★ 以視覺為基礎之強韌多指尖偵測與人機介面應用
★ 在夜間受雨滴汙染鏡頭所拍攝的影片下之車流量估計★ 影像特徵點匹配應用於景點影像檢索
★ 自動感興趣區域切割及遠距交通影像中的軌跡分析★ 基於回歸模型與利用全天空影像特徵和歷史資訊之短期日射量預測
★ Analysis of the Performance of Different Classifiers for Cloud Detection Application★ 全天空影像之雲追蹤與太陽遮蔽預測
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2024-7-26以後開放)
摘要(中) 場景識別是圖像語義分割中相當重要的一個環節,而如何正確且有效率地在場景中找到有效資訊的位置,是場景識別領域中十分困難的問題。在場景識別的任務上,場景是由物體、空間布局和背景之間的關聯關係等因素綜合而成的,而場景中的物體種類對分類結果影響甚深,透過辨識的場景物體分類出場景,例如浴室中的浴缸或馬桶、臥室中的床或書桌等。

本論文提出的方法是以辨識物體的特徵作為前處理的步驟,再根據結果分類出特定場景,透過Mask R-CNN算法針對輸入的圖片進行特定室內物件分割的處理,接著以分割完的物件作為場景的特徵,再與場景結合並進行分類。實驗結果證明,透過獲取場景中物件特徵的方法的前處理,能在場景識別中取得更好的場景分類準確度。
摘要(英) Scene Recognition is an important operation of Image Semantic Segmentation, in the wide range of scene recognition, it is a thorny issue to correctly and efficient find effective location information in specific scene. In the mission of scene recognition, a scene is mainly comprised of three elements, including object, spatial layout and the relationship between backgrounds, these object types in scene have huge impact on results of classification. Through this matter, scene could be recognized based on those identified objects of scene, for example, bathtub or toilet in the bathroom, bed or writing desk in the bedroom.
In this thesis, an effective architecture for scene recognition is proposed. The architecture includes a pre-process step to identify feature of each object, then classify specified scene based on the results of object feature. Moreover, those input pictures will be pre-processed through Mask R-CNN algorithm to identify specific indoor objects by results of segmentation, and those specified indoor objects become elements for scene recognition classification. The experimental results show that through pre-process of object identification, the proposed method has the advantages of accuracy in scene recognition.
關鍵字(中) ★ 室內場景
★ VGG16
★ Mask R-CNN
★ 特徵融合
關鍵字(英)
論文目次 摘要 I
Abstract II
目錄 III
圖目錄 IV
表目錄 V
第一章 緒論 1
1.1 研究動機 1
1.2 研究背景 2
1.3 系統架構 4
第二章 相關文獻 5
2.1 取得物體特徵 5
2.2.1 物體偵測 6
2.2.2 語義分割 6
2.2 神經網路架構 7
2.2.1 VGG-16 7
2.2.2 Inception 8
2.3優化器 12
2.4 防止過擬合 13
第三章 研究方法與系統程式 13
3.1 資料集 14
3.2 物件分割與預處理 16
3.2.1 Mask R-CNN與instance segmentation 16
3.2.2 Mask物件的顏色代表圖 17
3.3 建立特徵提取網路 20
3.2.1 參數設置 20
3.2.2 網路架構與特徵提取 20
3.4 特徵融合 21
3.5 系統程式 22
第四章 實驗結果 24
4.1 不同網路架構 24
4.2 提取特徵與融合的方式 26
第五章 結論與未來研究方向 27
參考文獻 28
參考文獻 [1] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. "Going deeper with convolutions", 2014.

[2] Espinace, P., Kollar, T., Roy, N., Soto, A., "Indoor Scene Recognition Through Object Detection Using Adaptive Objects Search ", 2010.

[3] Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba, "Places: A 10 million Image Database for Scene Recognition", 2017.

[4] Shuang Bai 1 ·Zhaohong Li 1 ·Jianjun Hou, "Learning two-pathway convolutional neural networks for categorizing scene images", 2016

[5] Szummer M, Picard RW, “Indoor-outdoor image classification.”, 1998

[6] Quattoni A, Torralba A, “Recognizing indoor scenes”, 2009.

[7] Li L, Su H, Xing EP, Fei-Fei L , “Object bank: a high-level image representation for scene classification and semantic feature sparsification.”, 2010.

[8] Pandey M, Lazebnik S, “Scene recognition and weakly supervised object localization with deformable part-based models”, 2011.

[9] Singh AAES, Gupta A, “Unsupervised discovery of mid-level discriminative patches”, 2012.

[10] Sadeghi F, Tappen MF, “Latent pyramidal regions for recognizing scenes.”, 2012.

[11] Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A, “Learning deep features for scene recognition using places database”, 2014.

[12] Ranzato M, Susskind J, Mnih V, Hinton G, “On deep generative models with applications to recognition.”, 2011.

[13] Lowe DG, “Distinctive image features from scale-invariant keypoints.”, 2004.

[14] Dalal N, Triggs B, “Histograms of oriented gradients for human detection”, 2005.

[15] Bay H, Tuytelaars T, Gool LV, “Surf: speeded up robust features”, 2006.

[16] Krizhevsky A, Sutskever I, Hinton G, “Imagenet classification with deep convolutional neural networks”, 2012.

[17] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”, 2014.

[18] R. B. Girshick, “Fast R-CNN”, 2015.
.
[19] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", 2016.

[20] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi., "You only look once: Unified, real-time object detection.", 2015.

[21] J. Redmon and A. Farhadi. "YOLO9000: Better, faster, stronger", InCVPR, 2017.

[22] J. Redmon and A. Farhadi., “Yolov3: An incremental improvement.”, 2018.

[23] Luis Herranz, Shuqiang Jiang, Xiangyang Li, “Scene recognition with CNNs: objects, scales and dataset bias”, 2016.

[24] Zhang L, Zhen X, Shao L, “Learning object-to-class kernels for scene classification.”, 2014.

[25] Long, J., Shelhamer, E., and Darrell, T., "Fully convolutional networks for semantic segmentation.", 2014.

[26] Kaiming He Georgia Gkioxari Piotr Doll ́ar Ross Girshick, "Mask R-CNN", 2018.

[27] Simonyan, K. & Zisserman, A., "Very deep convolutional networks for large-scale image recognition", 2014,

[28] S. Ioffe and C. Szegedy., “Batch normalization: Accelerating deep network training by reducing internal covariate shift”, 2015.

[29] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, “Rethinking the Inception Architecture for Computer Vision”, 2015.

[30] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi., “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, 2016.

[31] Ruder, S. "An overview of gradient descent optimization algorithms", 2016.

[32] Ning Qian., “On the momentum term in gradient descent learning algorithms. Neural networks : the official journal of the International Neural Network Society”, 1999.

[33] Diederik P. Kingma, Jimmy Ba, "Adam: A Method for Stochastic Optimization", 2017.

[34] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R., "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", 2014.

[35] waleedka, "Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow", 2018, from https://github.com/matterport/Mask_RCNN.
指導教授 鄭旭詠 審核日期 2019-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明