( 您好!臺灣時間:2021/05/07 12:20
字體大小: 字級放大   字級縮小   預設字形  


研究生(外文):Chen-Jhe Huang
論文名稱(外文):An Intelligent System of Growth Traceability for Working Dogs Based on Images
指導教授(外文):Yuh-Jiuan Tsay
外文關鍵詞:Image recognitionFeature Pointgrowth traceabilityreal-time
  • 被引用被引用:0
  • 點閱點閱:192
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
Working dogs are excellent dog professionally trained, and an mainly used to assist the work of human to complete assignments. Training working dogs cost a substantial expense for example the training of guide dogs in Taiwan early needs to purchase foreign trained guide dogs, but the price is quite expensive. Therefore in recent years working dog training center find the way toward to the local training to reduce the huge expense of training cost required. This study is based on the use of image recognition, tracking, monitoring technology, to build a set of working dogs the growth image biographical information systems to assist in training staff to understand dog behavior, status, and so contribute to the training center for the training of working dogs reduce the cost of the training of dogs consuming, this study by the dog's facial expressions, actions, behavior, habits, etc., as a feature recognition. The system is divided into four parts:(1) identification tracking dogs, mainly used to track the target and the background material marked objects separated and marquee dog to achieve recognition, tracking effect;(2) automated sample Edition functionality, automation model to create a picture, save the dogs behavior, expression of templates and stored centrally in the database used mode than the behavior of dogs;(3) life history functions, such comparisons, identify behavior subsequent analysis, automated way of records in the system, training staff, reference, and effectively improve work efficiency;(4) warning auxiliary functions, the dog, if an exception condition occurs the instant alert to inform training staff, reduce the incidence of dog accidents, disease.
摘要 I
Abstract II
誌謝 IV
圖目錄 VIII
表目錄 X
1.緒論 1
1.1研究背景與動機 1
1.2研究目的 3
1.3研究流程 5
1.4論文架構 6
2.文獻探討 7
2.3 辨識方法相關文獻 9
2.3.1 背景偵測 9
2.3.2 影像處理 12
2.3.3 影像特徵擷取和辨識方法 16
2.4 辨識流程與方法 18
2.4.1 前置處理 18
2.4.2 臉部偵測和特徵擷取 21
2.4.3 臉部辨識處理 23
3.研究方法 27
3.1系統架構 27
3.2工作犬追蹤模組 28
3.2.1樣板匹配(cvMatch Template) 31
3.3自動化樣板模組 32
3.3.1影像灰階化 33
3.3.2加速強健特徵(Speed-up Robust Features, SURF)演算法 34
3.4成長履歷模組 38
3.5警示輔助模組 44
4.系統開發與實作 46
4.1實作平台 46
4.2系統展示 46
4.2.1追蹤辨識功能 48
4.2.2影像成長履歷功能 49
4.2.3警示輔助功能 50
4.3實驗步驟與設計 51
4.4實驗結果分析 53
5.結論與未來發展 55
5.1結論 55
5.2未來發展 56
5.3研究限制 56
6.參考文獻 57

[1] 吳明衛,「自動化臉部表情分析系統」,國立成功大學資訊工程研究所碩士論文,2003年。
[2] 簡為哲,「主成份分析與因素分析應用於影像辨識和影像壓縮之比較」,國立台北大學統計研究所碩士論文,2005年。
[3] 李奇明,「利用臉部表情診斷學習困難度之研究」,國立臺灣師範大學工業教育研究所碩士論文,2007年。
[4] 黃薰瑩,「一個自動化葉片辨識系統」,國立交通大學多媒體工程研究所碩士論文,2007年。
[5] 楊煒達,「簡易方法之少量人臉辨識系統」,國立中央大學資訊工程研究所碩士論文,2007年。
[6] 鐘仁厚,「基於模糊邏輯之臉部表情辨識」,國立成功大學電機工程研究所碩士論文,2008年。
[7] 郭鴻肇,「影像監視防盜保全系統之研製」,元智大學電機工程研究所碩士論文,2008年。
[8] Y. Adini, Y. Moses and S. Ullman, “Face recognition: the problem of compensating for changes in illumination direction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 721-732.
[9] J. S. Beis and D. G. Lowe, “Shape Indexing Using Approximate Nearest-Neighbour Search In High-Dimension Spaces,” IEEE Conference on Computer Vision and Pattern Recognition, 1997.
[10] K. Choong Yowand R. Cipolla, “Feature-Based Human FaceDetection,”Department of Engineering, Cambridge, 1997.
[11] D. Chai and A. Bouzerdoum, “A Bayesian Approach to Skin ColorClassification in YCbCr Color Space,”IEEE Region Ten Conference,Kuala Lumpur, Malaysia, Vol. 2, 2000, pp. 421-424.
[12] L. L. Chen, S. H. Liu, S. H. Li and C. W. Chang, “Fast Video Object Segmentation for an Economical Network Surveillance System,” Proceedings of the WCE2002, Xin Zhu, Taiwan, 2002, pp. 101-106.
[13] C. C. Chiang, W. K. Tai, M. T. Yang, Y. T. Huang and C. J. Huang, “Anovel method for detecting lips, eyes and faces in real time,” Real–TimeImaging, Vol. 9, No. 4, August. 2003, pp. 277-287.
[14] R. Cucchiara, C. Grana, M. Piccardi and A. Prati, “Detecting moving objects, ghosts, and shadows in video streams,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 10, 2003, pp. 1337-1342.
[15] A. Abadpou and S. Kasaei, “New PCA-based Compression Method for Natural Color Images,” IPM Workshop on Computer Vision, 2004.
[16] L. Chiunhsiun and S. Ching-Hung, “Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network,” Pattern Recognition Letters, Vol. 28, Issue 16, 1 December. 2007, pp. 2190-2200.
[17] H. Fu and Z. Chi, “Combined thresholding and neural network approachfor vein pattern extraction from leaf images,” IEEE Proceedings-Vision,Image and Signal Processing, Vol. 153, No. 6, December. 2006.
[18] B. Heisele, P. Ho, J. Wu and T. Poggio, “Face recognition: component-based versus global approaches,” International Journal of Computer Vision and Image Understanding,2003, pp. 6-21.
[19] Li. Hongliang and King N. Ngan, “Saliency model-based face segmentation and tracking in head-and-shoulder video sequences,”Journal of Visual Communication and Image Representation, Vol. 19, Issue 5, July. 2008, pp. 320-333.
[20] A. Z. Kouzani, F. He and K. Sammut, “Towards invariant facerecognition,”Inf. Sci., Vol. 123, 2000, pp. 75-101.
[21] P. Kakumanu, S. Makrogiannis and N. Bourbakis, “A survey of skin-color modeling and detection methods,” Pattern Recognition, Vol. 40, Issue 3, March. 2007, pp. 1106-1122.
[22] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, 2004, pp 91-110.
[23] R. Lienhart, A. Kuranov and V. Pisarevsky, “Empirical Analysis of Detection Cascades Boosted Classifiers for Rapid Object Detection,” MRL Technical Report, May. 2002.
[24] J. Lu., X. Yuan and T. Yahaqi, “A Method of Face Recognition Based onFuzzy c–Means Clustering and AssociatedSub–NNs,” IEEE Transactions OnNeural Networks, Vol. 18, No. 1, January. 2007, pp. 150-160.
[25] S. J. McKenna, Y. Raja and S. Gong, “Tracking colour objects using adapative mixture models,” Image and Vision Computing,” Vol. 17, Issue 3-4, March. 1999, pp. 225-231.
[26] A. Pentland, B. Moghadam and T. Starner, “View-based and modular eigenspaces for facerecognition,” in: Proceeding of the IEEE Conf. on Computer Vision and Pattern Recognition,June. 1994, pp. 84-91.
[27] M. Soriano, B. Martinkauppi, S. Huovinen and M. Laaksonen, “Adaptiveskin skin color modeling using the skin locus for selecting training pixels,”Pattern Recognition, Vol. 36, No. 3, March. 2003, pp. 681-690.
[28] B. Shoushtarian and H. E. Bez, “A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking,” Pattern Recognition Letters, Vol. 26, No. 1, 2005, pp. 5-26.
[29] P. Spagnolo, T. D'Orazio, M. Leo and A. Distante, “Moving object segmentation by background subtraction and temporal analysis,” Image and Vision Computing, Vol. 24, No. 5, May. 2006, pp. 411-423.
[30] R. Verschae, A. Soria-Frisch and A. Olano, “Fuzzy fusion for skin detection,” Fuzzy Sets and Systems, Vol. 158, Issue 3, 1 February 2007, pp. 325-336.
[31] C. C. Wang, S. S. Huang, L. C. Fu and P. Y. Hsiao, “Driver Assistance System for Lane Detection and Vehicle Recognition with Night Vision,” IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 3530-3535.
[32] P. Kakumanu, S. Makrogiannis and N. Bourbakis, “A survey of skin-color modeling and detection methods,” Pattern Recognition, Vol. 40, Issue 3, March. 2007, pp. 1106-1122.
[33] L. Zhiming, and L. Chengjun, “Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition,” Computer Vision and Image Understanding, Vol. 111, Issue 3, September 2008, pp. 249-262.

註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔