部分重疊物件辨識：輪廓點特徵比對

Recognizing and Locating Partially Occluded Objects by Boundary

廖博仁(Bor-Ren Liaw)

指導教授 ： 蔡篤銘

元智大學/工程學院/工業工程與管理學系/碩士(1993年)

摘要

本研究主要在於利用機器視覺技術來辨識部分重疊物件與所在之位置。在
本研究中是以平面物件當成辨識對象，其物件的外型輪廓可為任意曲線(
Smooth curve) ，而物件本身不需要有任何特徵(如內孔、頂角等) 來做
為辨識的依據，所以可使辨識對象更具彈性，且應用的範圍更加廣泛。本
研究方法主要可分成兩大部分，第一部分主要是以圓來描述每一個邊緣
點 (Boundary Point) 的特徵值，包括了半徑值、圓心至輪廓點的各法線
向量(Normal)和圓弧的凹凸向，並利用此特徵值建立本研究中所使用的標
準物件參照表。第二部分為輪廓點的比對，主要是利用廣義霍氏轉換法(
General Hough Transform) 的概念找出待測物件部分重疊影像中的參考
點(Reference point) ，在此部分所使用的方法為二階段式的霍氏轉換
法(Two-Stage Hough Transform) ，首先在第一階段中，利用各輪廓點的
半徑值和凹凸向等限制式估算出部份重疊物件與標準物件的旋轉角度(△
θ)，然後在第二階段中使用輪廓點的半徑值、凹凸向和旋轉角度△θ等
限制式估算出部份重疊物件的參考點，辨識出部份重疊物件。由於本研究
方法所使用的標準物件參照表有三個指標值，使得辨識的正確性比廣義霍
氏轉換法為佳且對 高遮蓋的重疊物件亦有良好的辨識效果。本研究成果
可運用在自動化製程之中，利用機器視覺系統來對物件的生產過程進行監
視與控制，不須藉由人工處理，便可將物件所在的位置或損壞的情形傳送
至下一個製程中心，以利機器對待處理物件做準確的判斷與處理，增加自
動化之彈性及製程的效率。

關鍵字

機器視覺 ； 重疊物件 ； 輪廓表示 ； 霍氏轉換

並列摘要

The problem of recognizing partially occluded parts is of
considerable interest and challenge in the field of industrial
automation. While it is possible to employ carriers or pallets
to separate or prearrange the parts for easy recognition, a
vision system which can recognize the parts even though they
may be partially overlapped and in random positions in much
flexible. An occluded object can be due to the occurrence of
touching and overlapping objects, or the occurrence of
defects. The polygonal approximation of contours is the most
frequently used technique to represent the shape of objects,
and the line segments of the observed shapes are matched with
the segments of model objects. However, this method is
sensitive to noise and gives satisfactory results only for
polygonal objects. In this research, we consider the problem of
identifying and locating partially occluded objects lying on a
flat surface, and the contours of objects can be arbitrary
piecewise smooth curves. A novel shape representation is
developed to describe the boundaries of objects. Given a
boundary point on the curve, we use the least-square circle
fitting method to estimate the center of circle for a small
curve segment in the neighbor of Pi. A vector Ni connecting the
estimated center and point Pi is formed to represent the
geometric features of the boundary point. The magnitude of the
vector (or, the radius of the estimated circle) represents the
curvature of the curve at point Pi, and the vector direction
represents the normal to the curve at point Pi. Then, a two-
stage Hough transform is used for the recognition of occluded
objects. The vector N is used as the shape signature that gives
geometric constraint information in the voting stage of the
Hough transform. It eliminates noise and false matching
boundary points. The matching correctness and reliability and
the accuracy of the object pose (rotation angle and
translation) are improved accordingly.

並列關鍵字

Machine vision ； Occluded parts ； Shape matching ； Hough transform