This chapter contains operators that can be used to either query information related to iconic objects or to manipulate iconic objects.
HALCON provides different kinds of iconic objects that are suitable for different needs:
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An image consists of one to multiple channels, i.e., matrices of similar size that contain gray values of various pixel types. Images are the major source for most machine vision tasks.
A region is defined as a set of pixels. The pixels of a region do not need to be connected. This means that even an arbitrary collection of pixels can be handled as a single region. Region processing is suitable, e.g., to apply a blob analysis within images or to define a region of interest (ROI) for following subpixel-precise operations.
XLD is the abbreviation for eXtended Line Description and comprises all contour and polygon based data. Note that XLD contours are returned or used by many HALCON operators whereas XLD polygons and XLD parallels are needed only in special cases. A contour is a sequence of subpixel-accurate 2D control points that are connected by lines. Typically, the distance between control points is about one pixel. XLD objects contain, besides the control points, so-called local and global attributes. Typical examples for these are, e.g., the edge amplitude of a control point or the regression parameters of a contour segment. Contour processing is suitable, e.g., for subpixel-precise measurements. Subpixel-precise operations can be faster if they are applied within an ROI.
See the “Quick Guide” for further details about the
available data structures.