The proposed RINCCAS method, an abbreviation of the paper title, was originally developed to participate in DAFNE (Digital Anastylosis of Frescos challeNgE) race, June-July 2019. The method consists of two phases. Phase 1 extends the classic Normalized Cross Correlation (NCC) for template matching of arbitrary curvilinear 2D shapes of fragmentsthat are assumed belonging to a fresco as perceived in a photograph. For this purpose, each fragment is approximated by one (up to several, but not overlapping) Maximal & Axes-Collinear Inner Rectangles (MACIRs). The extension also includes rotation invariance and vector compatibility of NCC in respect to the three (RGB) color channels. The high positioning accuracy makes it possible to identify eventual/existing spurious fragments in Phase 2 of RINCCAS, as follows − by HSV scheme for color differences, and by accurate recognition of overlaps among the fragments. The first phase is ‘log-cubically’ complex in speed, estimated on the average size of the MACIRs of the fragments. For some DAFNE tasks, the 1st phase of RINCCAS requires high computational resources (HPC), while a conventional PC is sufficient for its 2nd phase, even in the case of multiple interactive optimization of the ratio between true and spurious fragments.

拟议的RINCCAS方法是论文标题的缩写，最初是为了参加2019年6月至7月的DAFNE（壁画challeNgE的数字吻合术）竞赛而开发的。该方法分为两个阶段。阶段1扩展了经典的归一化互相关（NCC），用于模板的任意曲线2D形状的片段模板匹配，这些片段被假定属于照片中的壁画。为此，每个片段用一个（最多几个，但不重叠）Maximum＆Axes-Collinear-Innerrectangle（MACIRs）近似。该扩展还包括相对于三个（RGB）颜色通道的NCC旋转不变性和矢量兼容性。较高的定位精度使得可以在RINCCAS的第2阶段中识别出最终的/存在的虚假碎片，如下所示-通过HSV方案检测色差，并准确识别片段之间的重叠。第一阶段的速度“对数立方”复杂，根据片段的MACIR的平均大小进行估算。对于某些DAFNE任务，RINCCAS的第一阶段需要大量的计算资源（HPC），而常规PC足以满足其第二阶段的需要，即使在对真假片段比率进行多次交互优化的情况下也是如此。