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DESAC: differential evolution sample consensus algorithm for image registration
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DOI：10.1007/s10489-022-03266-0.png)
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Abstract
En 中文
Image registration is the process of matching and superimposing multiple images from heterogeneous sources, and it is widely used in areas such as image processing and computer vision. In point feature-based image registration, the RANdom SAmple Consensus (RANSAC) algorithm is commonly used to eliminate mis-matches and solve transform models. Traditional RANSAC tends to sample in sample space. Its discrete estimation of model parameters can lead to an inability to solve for the largest consensus set at large input sizes and low inlier ratio. RANSAC with sampling in parameter space is a novel and effective method, but it is less studied and still has many shortcomings. In this paper, a Differential Evolution SAmple Consensus (DESAC) algorithm based on sampling in parameter space is proposed, which represents the model as individuals and optimizes the model parameters by Differential Evolution (DE). First, for an adequate search of the parameter space, DESAC uses priori information for the first initialization and the resident initialization performed in the iteration. Second, to achieve a balance between exploration and exploitation, a mutation operator combining neighborhood optimal individuals and random individuals is proposed. Third, a novel selection operator is proposed in order to obtain individuals with high number of inliers and low inlier error. Finally, to reduce the verification overhead, a simplified pre-test step is applied before the full verification. Comparing with advanced RANSAC variants in real image dataset, the proposed method can robustly return consensus sets with very high number of inliers. Its average inlier error is lower and the transformation model is more accurate.
Keywords:
Image registration
RANSAC
Differential evolution
Robust estimation