RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY
RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY
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DOI：
10.1145/358669.358692
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发表时间：
1981-01-01
影响因子：
22.7
通讯作者：
BOLLES, RC
中科院分区：
文献类型：
--
作者：
FISCHLER, MA;BOLLES, RC
一个新的范例，随机抽样共识（RANSAC），用于拟合模型的实验数据。RANSAC能够解释/平滑包含显著百分比的粗差的数据，因此非常适合于自动图像分析中的应用，其中解释是基于易出错特征检测器提供的数据。本文的一个主要部分描述了RANSAC的位置确定问题（LDP）的应用：给定一个图像描绘了一组地标与已知的位置，确定该点在空间中的图像是从哪里获得的。响应于RANSAC的要求，新的结果得出的最小数量的地标需要获得一个解决方案，并提出算法计算这些最小地标解决方案的封闭形式。这些结果提供了一个自动系统，可以解决在困难的观看LDP的基础
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing