Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use

Abstract

Abstract. This paper presents a vectorisation system based on the use of strategic knowledge. This one is composed of two parts: a processing library and a graphic user interface. Our processing library is composed of image preprocessing and vectorisation tools. Our graphic user interface is used for the strategic knowledge acquisition and operationalisation. It allows to construct and to execute scenarios, exploiting any processing of our library, according to documents' contexts and users' adopted strategies. A XML data representation is used, allowing an easy data manipulation. A scenario example is presented for graphics recognition on utility maps.

Local Structural Analysis: A Primer

“…The second one analyses skeleton images in order to extract pixel graphs. It uses different methods, based on the connectivity analysis [54] [44], or the 3-connected pixel destruction [14] [27]. The Figure 2 (a) gives an example of an extracted pixel graph [14].…”

“…The first methods use contour images like intermediate representations. They are based on mathematical morphology [20] or on neighbouring tests [14]. Similar to skeletonisation based methods, they involve a second step to extract the contours' pixel chains.…”

“…The second methods directly extract the contours' pixel chains without any intermediate image representation. They use line following methods [1], or blob coloring methods [14]. Their advantages rely on the fact that they provide the inclusion relations between chains, and they permit the selection of internal or external contours [14].…”

“…They use line following methods [1], or blob coloring methods [14]. Their advantages rely on the fact that they provide the inclusion relations between chains, and they permit the selection of internal or external contours [14]. The Figure 2 (b) gives an example of external contours' pixel chains of the Figure 1 [14].…”

“…Then, they search the majority directions for each pixel, and construct like this the line, extremity, and junction regions (Figure 2 (e)). In [14], we propose region decomposition method based on a wave aggregation. The wave breaking and stopping cases define the regions' boundaries.…”