本論文將以小波轉換為基礎，提出一套快速偵測法於複雜影像中辨識多個目標物件。本研究所建構的影像辨識系統包含八個步驟：a.色彩模型轉換b.小波影像轉換c.雜訊濾波d.影像形態處理e.邊緣偵測f.特徵抽取和表示g.目標物框選h.目標物中心計算。本實驗使用全彩CCD攝影機來擷取影像，在處理色彩之前必須精確的表達色彩，用顏色偵測分割過濾複雜背景並定位出特定顏色範圍之目標物。進行小波轉換時，分別作高通和低通濾波，將得到的影像降階，高度與寬度變為原來的二分之一，使得影像處理更快速。根據一些環境影像圖片實驗結果顯示，我們提出的方法能夠準確且更快速的偵測出複雜影像中的多目標物件，更能準確有效計算出物件的形態資訊。

This paper proposes a set of methods for detecting and recognizing multiple target objectives in a complicated image based on wavelet transform. The constructed image recognition system consists of eight steps: a. color model transform, b. wavelet transform, c. noise filter, d. morphological image processing, e. edge detection, f. feature extraction and representation, g. target framing, h. computation of target object center.
This study, a full color camera CCD is adopted to capture images, to accurately represent color before processing and separate the complicated background via color detection and locate target objects within certain color range. During wavelet transform, it uses high- and low-pass filtering to reduce the image order, making the height and width half as much as the original size, by which the image process becomes quicker. From the experimental results on some environmental images, the proposed method is able to detect the multiple target objects in complex images quickly and precisely, as well as effectively calculate the pattern information of objects.

【1】 C. Faloutsos,“Signature Based Text Retrieval Methods: A Survery, “ IEEE Data Eng. Bull.,vol. 13, no. 1, pp. 25-32, Mar.1990.
【2】 Roghuveer M. Rao and Ajit S. Bopardikar, “Wavelet Transforms, Introduction to Theory and Application,” AddisonWesley Longman, Inc.,1998.
【3】 C. Sidney Burrus, Ramesh A. Copinath, and Haitao Guo,“Introduction to Wavelets and Wavelet Transforms,”Prentice Hall, Inc., 1998.
【4】 Birkhaüser, “A Friendly Guide to Wavelets,” Gerald Kaiser, 1994.
【5】 W. Skarbek, A. Koschan, “Color image segmentation”, Institute for Technical Informatics, Technical University of Berlin, October, 1994.
【6】 M.Antonono, M.Barlaud, P.Mathieu, and I. Daubechies, 1992 “Image Coding UsingWavelet Transform,” IEEE Trans.Image Processing, Vol.1, pp.205-220
【7】 R.A.Devore, B.Jawerth, and B.J.Lucier, 1992,“Image Compression Through Wavelet Transform Coding,” IEEE Trans.Informat.Theory, Vol.38, pp.719-146
【8】 J.Hadamard, “Resolution d’une Question Relative aux Determinants,” Bull.Sci.Math.,Ser.2, 17, Part 1,pp.240-246, 1893
【9】 N.Otsu, “A thresholding selection method from gray-level histograms,”IEEE Trans. On System Man Cybernetics, vol.9,pp.62-69, 1979.
【10】 Gonzalez, R. C. and R. E. Woods “Digital Image Processing,”2nd Ed., pp.556-588, Prentice Hall, New Jersey, 2003.
【11】 Stephane Mallat , ” A wavelet tour of signal processing ”,Academic Press,1998.pp76-87,153-156,241-262,319-335.
【12】 Larry L.Schumaker , Glenn Webb , ” Recent advances in wavelet analysis”,Academic Press,1994,pp309-334.
【13】 Gilbert G.Walter , Xiaoping Shen , ” Wavelets and other Orthogonal systems”Chapman&Hall,2001.pp91-107.
【14】 Brani Vidakovic , ” Statistical modeling by wavelets ” , Wiley, 1999.pp1-19,43-80, 133-140,167-94.
【15】 Boggess , Albert ,Narcowich,Francis J.”A first course in wavelets with Fourier analysis”.Prentice Hall,1998.pp 155-239.
【16】 賴岱佑，數位影像分析之智慧型監視系統，文魁資訊股份有限公司，2008
【17】 陳同孝、張真誠、黃國峰，數位影像處理技術，松崗電腦圖畫 資料股份有限公司，2001
【18】 張智星，MATLAB 程式設計與應用，清蔚科技股份有限公司出版事業部，2000
【19】 廖紹綱,數位影像處理活動 Matlab,全華科技,台北市,1999
【20】 長思科技產品研發中心，MATLAB6.5 輔助小波分析與應用，電子工業出版社，北京，2003。
【21】 繆紹綱譯，普林斯頓國際有限公司，“數位影像處理”，2004 年，pp.610-624。
【22】 莊富貿，“高精度巧智型太陽影像追蹤方法之設計與系統整合”，國立成功大學航空太空工程研究所碩士論文，2013年。