以影像處理方法分割相互接觸之近似橢圓

本論文發展一系列影像處理方法，使相互接觸之近似橢圓物件影像得以正確分割。主要方法包括：前置濾波器、分類、建立初始形變曲線與物件輪廓重建等。為了克服雜訊與被偵測物件具明顯紋理之干擾，提出結合Mean shift演算法與梯度向量場(gradient vector field, GVF)所設計之前置濾波器。經由此濾波器處理，可正確地擷取影像中物件之輪廓線。根據這些被偵測物件的輪廓線所產生之影像場，本研究發展出兩種不同的動態質點分類法，以正確獲得每一個物件之流場中心。此兩種分類法分別使用反向梯度向量場與距離轉換場配合Mean shift演算法發展出來。為了產生初始形變曲線，分別藉由蒙地卡羅觀念均勻放置動態質點法與使用Fitzgibbon最佳橢圓法，配合所提出兩種分類法。最後以主動輪廓模式(active contour model，ACM)重建近似橢圓物件之輪廓。實驗結果顯示，即使所偵測之物件輪廓線破碎不完整，但只要邊界輪廓線資訊比在50%以上，都能成功地重建出每一個物件的完整輪廓，其與人工分割米粒影像之相似度高達96%以上。當被偵測物件具明顯紋理或影像被雜訊干擾時，以Mean shift演算法與梯度向量場所設計之濾波器能有效抑制干擾，甚至當影像外加10%的點雜訊，所設計之濾波器仍能濾除此干擾，正確擷取物件輪廓線特徵，配合後續動態質點分類法(active points grouping approach)與主動輪廓模式，完成明顯紋理並相互接觸近似橢圓物件影像分割之目的。經由此方法所處理之物件影像，可依據其個別封閉輪廓線的取得，很容易求得影像中各物件之幾何、紋理或顏色之特徵，方便後續叢聚、分類與了解之目的。

關鍵字

主動輪廓模式；邊緣偵測；橢圓偵測；梯度向量場；影像處理；影像分割；機器視覺

並列摘要

In this study, we developed a synergistic approach for the segmentation of touching ellipse-like objects with obvious texture and noises in an image. The proposed approach modifies and integrates several major image processing methods including pre-filtering, grouping, creating initial contours, and reconstructing contours. For de-noising, mean shift algorithm and Gradient Vector Field (GVF) are employed as a pre-filter. Through the filtering and edge detection, the processed image only preserves the boundaries of objects and rejects noise. With the edges, we developed two kinds of active point grouping approaches for generating the field center of each touching ellipse-like object. Inverse GVF (IGVF) field and mean shift algorithm with distance transform (DT) weight map are employed in the two grouping approaches, respectively. For creating initial deformable contour of each object, we designed two generation methods, the equally-spacing active points method inspired by Monte Carlo’s concept as well as Fitzgibbon’s optimal ellipses method. Finally, the complete contour of each object could be correctly reconstructed by Active Contour Model (ACM). The result shows that the algorithm could successfully reconstruct the whole contour as long as more than 50% of piecewise edge information remained in an image. Compared with the original contours, the ones generated in this study achieved more than 96% similarity. When the obvious textures or noises are filtered out by the mean shift algorithm with GVF weight map, it could effectively remain the edges of the detected objects. Even for an image polluted by 10% salt and pepper noises, the approach still can effectively and robustly eliminate the added noises. Therefore we can successfully cluster objects and reconstruct their corresponding contours by applying active contour model approach. The complete contours of touching objects could facilitate the subsequent image processing to obtain the geometric, texture, and color characteristics of objects in an image. These features might then be used for further clustering, classification, or image understanding.

並列關鍵字

Active contour model (ACM) ； Edge detection ； Ellipse detection ； Gradient vector flow (GVF) ； Image processing ； Mean shift ； Segmentation

參考文獻

Wang, Y. C., and J. J. Chou. 2006. Segmentation of ellipse-like objects in an image with MFA approach. Journal of Agricultural Machinery 15(1): 15-24. (in Chinese)

Yun, H. S., W. O. Lee, H. Chung, H. D. Lee, J. R. Son, K. H. Cho, and W. K. Park. 2002. A Computer Vision System for Rice Kernel Quality Evaluation. 2002 ASAE Annual Meeting. Paper number 023130.

Bennett, N., R. Burridge, and N. Saito. 1999. A method to detect and characterize ellipses using the hough transform. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(7): 652-657.

Borgefors, G. 1984. Distance transform in arbitrary dimension. Comp. Vis. Graph. Imag. Proc. 27: 321-345.

Burt, P. J., T. H. Hong, and A. Rosenfeld. 1981. Segmentation and estimation of image region properties through cooperative hierarchical computation. IEEE Trans. on Systems, Man, and Cybernetics 11:802-809.

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以影像處理方法分割相互接觸之近似橢圓

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