三維影像自動物件分割與辨識演算法之研發

在這篇論文中，將對3-D圖像處理的物件分割和識別物體技術進行討論。隨著時代的進步，科技快速的發展，光學感測和測距裝置成為3-D影像與資訊的主要方式，3-D數據的可行性將更高。高解析的3-D物件的資訊，如人、汽車、樹木、建築物和道路的三維形貌資訊可以透過影像擷取獲得，而如何由取得的形貌資訊中進行正確的物件分割和識別將是機器視覺中具有挑戰性的研究課題。本研究的第一個貢獻是新型的物件深度切層分割處理技術，以即時運算將三維形貌資訊中進行物件分割。物件所在的深度位置可以初步的利用深度切層分割技術來分離。為準確地定義出物件的邊緣，使用區域增長法並通過遞歸搜索過程中來找出正確的邊緣位置，以清楚地將物件分割出來。第二個貢獻是對分割物件採用新型的區域增長演算法和表面特性的分類。為了解決背景識別的問題，提出以物件表面向量分佈作為參考依據。根據這個參考依據，能將所有的點資料進行一個初步的分類。將這個分類的結果納入該表面的區域增長法的成長過程中，就能有效的對複雜的物件進行數據分割演算。為了準確地找出物件的邊界，遞迴搜尋方式的區域增長算法的過程及開發在本研究中提出。本論文的第三個貢獻，旨在識別物體。識別物體的重點主要在於界定獨特的特徵點，藉此來區分不同的3-D物體之間的相似性。在這項研究中，物體識別方式以自動搜索範圍內物件的幾何形狀和曲率變化直方圖來識別目標。以往的目標識別的都是已一個感應裝置的單一視角影像來進行辨識，其辨識效果將受到約束。因此，本研究所提出的技術可以克服這一弱點，針對物件的重要特徵採用從多個視角的影像進行分析，而得到一個完整的3-D結構的特徵值方圖分佈，藉此得到精準的目標辨識結果。

關鍵字

影像分割；影像識別；圖像範圍；表面曲率；法線向量

並列摘要

In this dissertation we discuss a variety of 3-D image processing techniques that advance the state of the art in the fields of object segmentation and object recognition. The rapid development of technology has made powerful light detection and ranging devices become available in the market and the acquisition of 3-D range data has been more feasible and popular. High-resolution 3-D profile of objects, such as people, cars, trees, buildings and roads, can be captured, providing a great help for the tasks of object segmentation and recognition which are remaining as challenging research topics in computer vision. The first contribution of this research is the novel depth slicing technique for segmenting object with the real-time processing. The depth region containing objects can be initially segmented by employing depth slicing technique. For accurately marking object boundary, a region growing method is then applied through a recursive searching process. The second contribution is a new method for object segmentation employing region-growing algorithm and classification of surface characteristics. In order to solve the problem of digital background identification (DBI), the method proposes a novel criterion based on the distribution of normal surface vectors. According to this criterion, range data are classified into certain types of surface as an initial stage of evaluation for addressing all the points belonging to the background. By incorporating this criterion into the region-growing process, a robust range data segmentation algorithm capable of segmenting complex objects suffering huge amount of noises in outside condition is established. To detect accurately the object boundary, a recursive search process involving the region-growing algorithm for registering homogeneous surface regions is developed. The third contribution of this thesis aims at object recognition. The key breakthroughs for object recognition mainly lie in defining unique features that distinguish the similarity among various 3-D objects. In this research, the object recognition scheme is developed to identify targets underlining automated search in the range images using geometric constrains and curvature-based histogram. Since the accuracy of object recognition is generally limited by using a single viewpoint constraint of sensing device, the important feature of the proposed technique which can overcome this weakness is to employ a set of histograms from multiple views for representing the 3-D structure of objects.

並列關鍵字

object segmentation ； object recognition ； range image ； surface curvature ； normal vector

參考文獻

[1] L. C. Chen, X. L. Nguyen and Y. S. Shu, “High Speed 3-D Surface Profilometry using HSI Color Model and Trapezoidal Phase-shifting Method,” Technisches Messen, Vol. 76, No. 7-8, pp. 347-353, 2009.

[3] L. C. Chen, Y. S. Shu and X. L. Nguyen, “High Speed 3-D Surface Profilometry using HSI Color Model and Trapezoidal Phase-shifting Method,” International Conference on Precision Measurement, Technische Universität Ilmenau, German, September 08-12, 2008.

[4] L. C. Chen and X. L. Nguyen, “Dynamic 3-D surface profilometry using a novel colour pattern encoded with a multiple triangular model,” Special Issues, Measurement Science and Technology, Vol. 21, No. 5, pp. 054009, 2010.

[5] L. C. Chen and X. L. Nguyen, “Dynamic 3D surface profilometry using a novel color pattern encoded with a multiple triangular model,” The 9th International Symposium on Measurement Technology and Intelligent Instruments, Saint Petersburg, Russia, June 29 – July 2, 2009.

[7] S. Zhang and P. Huang, “High-Resolution, Real-time 3D Shape Acquisition,” Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04), Vol. 3, 2004.

國際替代計量

三維影像自動物件分割與辨識演算法之研發

全文下載

主題瀏覽