A Robust System for Visual Pattern Recognition in Engineering Drawing Documents

Abstract

In AEC (Architectural, Engineering and Constructional) domain, the architects use several types of drawing documents. These documents consist of various category of graphical objects and such objects can be classified based on their shape and curvature properties. Here, we have proposed a query enabled image searching system which can be used to locate objects in a set of documents. To achieve our goal, at-first we have identified a key point search window based on the inflection points of the contour of the object. Then, for each window we try to classify the curve segment into positive or negative class based on the curvature values. Then depending on the predicted class, we get the maxima or minima point on the contour. Now, we use these points as seed values (initial key points) to our feature detection mechanism. To extract the shape and/or curvature properties of the objects at different levels of scale and orientation, we have employed two feature extraction mechanisms. To get the local feature descriptor, we have introduced a neighborhood-based curvature extraction methodology and to consider the global shape of the object, Hu moments based shape descriptor of the object contour is computed. Finally, these two features are normalized, and their combined feature vector is used to compare and measure the similarity between the key points of two objects. We have also introduced a mechanism to identify the query object as a whole, when it consists of multiple overlapping contours for a single object. We have tested our system on a variety of AEC class of drawing documents and the results are quite encouraging. The proposed system will enable the users to locate similar objects inside a project consisting of hundreds of documents. Also, the method can be used for object hyperlinking with some alterations.