Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA.
Neural Netw. 2011 Mar;24(2):208-16. doi: 10.1016/j.neunet.2010.10.007.
CONFIGR-STARS, a new methodology based on a model of the human visual system, is developed for registration of star images. The algorithm first applies CONFIGR, a neural model that connects sparse and noisy image components. CONFIGR produces a web of connections between stars in a reference starmap or in a test patch of unknown location. CONFIGR-STARS splits the resulting, typically highly connected, web into clusters, or "constellations". Cluster geometry is encoded as a signature vector that records edge lengths and angles relative to the cluster's baseline edge. The location of a test patch cluster is identified by comparing its signature to signatures in the codebook of a reference starmap, where cluster locations are known. Simulations demonstrate robust performance in spite of image perturbations and omissions, and across starmaps from different sources and seasons. Further studies would test CONFIGR-STARS and algorithm variations applied to very large starmaps and to other technologies that may employ geometric signatures. Open-source code, data, and demos are available from http://techlab.bu.edu/STARS/.
开发了一种新的基于人类视觉系统模型的星象图像配准方法 CONFIGR-STARS。该算法首先应用 CONFIGR，这是一种连接稀疏和嘈杂图像成分的神经模型。CONFIGR 在参考星图或未知位置的测试斑块中生成星星之间的连接网络。CONFIGR-STARS 将生成的网络（通常是高度连接的）分割成簇或“星座”。簇的几何形状被编码为签名向量，记录相对于簇基线边缘的边长和角度。通过将测试斑块簇的签名与参考星图代码本中的签名进行比较，可以识别测试斑块簇的位置，其中已知簇的位置。尽管存在图像干扰和遗漏，并且跨越来自不同来源和季节的星图，模拟仍表现出强大的性能。进一步的研究将测试 CONFIGR-STARS 和应用于非常大的星图以及可能采用几何签名的其他技术的算法变体。开源代码、数据和演示可从 http://techlab.bu.edu/STARS/ 获取。
