應用區域化的Zernike Moments於二維和三維物體辨識

Abstract

我們在此論文中提出了一種物體辨識的方法,此種方法並不會受到物體在 空間中的位置，大小，和旋轉的改變而影響。在此方法中有一個基本的假 設，就是物體是放在單純的背景中，因此可以很容易把物體從背景中分離 出來。同時我們改良Zernike moments 以獲得一種稱為區域化Zernike moments 的特徵抽取方法。這種特徵抽取方法除了保有Zernike moments 不受物體旋轉而改變的特性之外，又能抽取物體在幾何上的特徵，因此能 從同一張影像中獲取比Zernike moments 更多的資訊。另一方面，一種指 導式模糊適應性Hamming 網路{\rm (supervised fuzzy adaptive Hammingnet)} 在分類功能上等效於fuzzy ARTMAP，而且在學習與辨識時 不需花費時間去搜尋，所以比fuzzy ARTMAP更有效率, 因此我們選擇它來 當我們的分類器。在本篇論文中有兩個實驗，一個是二維空間的鑰匙辨識 ，另一個是三維空間的目標物辨識。經由實驗結果顯示提出的新方法對於 二維和三維空間的物體辨識均可獲得不錯的辨識效果。} We propose an object recognition approach that is invariant to translation, scale, and rotation. We assume objects are placed on a uniform background so that we can easily distinguish between the object and its background, and use localized Zernike moments to extract object invariant features. This method not only retains the rotation invariance of Zernike moments, but also extractes the geometrical features of objects. That is, localized Zernike moments can obtain more information from the same image than Zernike moments provide. A supervised fuzzy adaptive Hamming net is equivalent to a fuzzy ARTMAP when used as a classifier, and doesn't need to search when learning and recognition, i.e., it is more efficient than fuzzy ARTMAP. So, we use it as our classifier. Two experiments are reported, 2-D key-pattern recognition and 3-D target recognition. The experimental results show that the proposed method obtained good performance for both 2-D and 3-D object recognition.