Interpreting Line Drawing Images: A Knowledge Level Perspective

Abstract

Abstract. Image understanding systems rely heavily on a priori knowledge of their application domain, often exploiting techniques developed in the wider field of knowledge-based systems (KBSs). Despite attempts, typified by the KADS/CommonKADS projects, to develop structured knowledge engineering approaches to KBS development, those working in image understanding continue to employ unstructured 1 st generation KBS methods. We analyse some existing image understanding systems, concerned with the interpretation of images of line drawings, from a knowledge engineering perspective. Attention focuses on the relationship between the structure of the systems considered and the KADS/CommonKADS models of expertise, sometimes called generic task models. Mappings are identified between each system and an appropriate task model, identifying common inference structures and use of knowledge. This is the first step in the acquisition of models of the expertise underpinning drawing interpretation. Such models would bring significant benefits to the design, maintenance and understanding of line drawing interpretation systems.

Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

“…So, the participants should describe their method precisely (which algorithms for preprocessing have been used and which a priori knowledge has been taken into account). A previous methodology has been proposed to describe graphics recognition systems at a knowledge level [39]. It could be a way to approach this problem.…”

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

“…Existing engineering interpretation methods may be roughly classified into five categories, depending on the basic technique they rely on: pixel-level knowledge-independent [6], [7], [9], [10], [11], [22], [23], pixel-level knowledge-dependent [13], [16], [17], [18], [20], [24], [25], [26], [27], [28], [29], vectorial-level knowledge-independent [30], [31], [32], [33], vectorial-level knowledge-dependent [34], [35], [36], [37], [38], [39], and hybrid systems [8], [40], [41].…”

“…Zhi et al [35] develop a graph-based approach AUG to capture architectural information originally produced by designers in CAD plans and rebuild the topological relationships. However, since knowledge is hard-coded, these systems are not easily extensible to other domains [41], [46].…”

“…Several reasons may explain this situation. First, how to represent contextual knowledge that describes drawing conventions efficiently and robustly is admittedly a hard problem [3], [4], [41]. Some knowledge-based systems use the "rules + inference" representations to obtain low-level vectors [8], [16], [17], [18], [19], [20], [21]; however, such unstructured rules are not amenable to content-oriented analysis due to their limited ability to describe complex relations of various types of high-level entities in real-life engineering drawings.…”

A Platform to Extract Knowledge from Graphic Documents. Application to an Architectural Sketch Understanding Scenario

“…The development of general graphics recognition platforms remains still a challenge. Some interesting approaches exist that are focused on that goal [1][2][3]. In the existing applications the methodological basis is mainly the same.…”