(3.236.214.19) 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  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:黃晨哲
研究生(外文):Chen-Jhe Huang
論文名稱:智慧型工作犬成長影像履歷資訊系統
論文名稱(外文):An Intelligent System of Growth Traceability for Working Dogs Based on Images
指導教授:蔡玉娟蔡玉娟引用關係
指導教授(外文):Yuh-Jiuan Tsay
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:61
中文關鍵詞:影像辨識特徵點成長履歷即時性
外文關鍵詞:Image recognitionFeature Pointgrowth traceabilityreal-time
相關次數:
  • 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
工作犬可協助人類所指派之各項工作。訓練工作犬所需耗費的成本相當龐大,例如:導盲犬,若購買國外訓練好的導盲犬,價格相當昂貴,因此台灣近幾年來極力推廣工作犬培訓中心,以國內自行培訓的之方式,降低所需之昂貴金額。本研究基於利用影像辨識、追蹤技術,建置出一套工作犬成長影像履歷資訊系統來觀察、輔助訓練人員了解犬隻的行為、狀況等等,有助於培訓中心對於工作犬之培訓,降低訓過程中所耗費之成本。本研究分為四個部分:(1)犬隻辨識追蹤功能,主要用來追蹤、辨識目標物件;(2)自動化樣版功能,自動化建立圖片樣板,儲存犬隻行為、表情之樣板,並儲存於資料庫中,用來匹配犬隻行為模式;(3)生活履歷功能,將匹配的行為,以自動化方式紀錄於本系統中,提供訓練人員後續分析、參考之作用,有效提升工作效率;(4)警示輔助功能,犬隻若有異常狀況發生,能夠即時性的發出警示告知訓練人員,降低犬隻意外、疾病之發生。
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

中文
[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。
英文
[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.

連結至畢業學校之論文網頁點我開啟連結
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!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
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