摘要
In the field of underwater image processing, the line and rounded objects, like mines and torpedoes, are the most common targets for rec, ognition. Before further analysis, these two image patterns need to be detected and extracted from the underwater images in real-time. Using the subpixel position, direction and curvature information of an edge provided by Zernike Orthogonal Moment （ZOM） edge detection operators, an enhanced Randomized Hough Transform （RHT） to extract straight-lines is developed. This line extraction method consists of two steps ： the rough parameters of a line are obtained robustly at first using RHT with large quantization in the Hough space and then the parameters are refined with line fitting techniques. Therefore both the robustness and high precision can be achieved simultaneously. Particularly, the problem of ellipse extraction is often computationally demanding using traditional Hough Transform, since an ellipse is characterized by five parameters. Based on the generalized K-RASAC algorithm, we develop a new ellipse extraction algorithm using the concept of quadratic curve cluster and random sampling technique. We first develop a new representation of quadratic curves, which facilitates quantization and voting for the parameter A that represents a candidate ellipse among the quadratic curves. Then, after selecting two tangent points and calculating the quadratic parameter equation, we vote for the parameter A to determine an ellipse. Thus the problem of ellipse extraction is reduced into finding the local minimum in the A accumulator array. The methods presented have been applied successfully to the extraction of lines and ellipses from synthetic and real underwater images, serving as a basic computer vision module of the underwater objects recognition system. Compared to the standard RHT line extraction method and K-RANSAC ellipse extraction method, our methods have the attractive advantages of obtaining robustness and high precision simultaneously while preserving the merits of high computation speed and small storage requirement.
In the field of underwater image processing, the line and rounded objects, like mines and torpedoes, are the most common targets for recognition. Before further analysis, these two image patterns need to be detected and extracted from the underwater images in real-time. Using the subpixel position, direction and curvature information of an edge provided by Zernike Orthogonal Moment (ZOM) edge detection operators, an enhanced Randomized Hough Transform (RHT) to extract straight-lines is developed. This line extraction method consists of two steps: the rough parameters of a line are obtained robustly at first using RHT with large quantization in the Hough space and then the parameters are refined with line fitting techniques. Therefore both the robustness and high precision can be achieved simultaneously. Particularly, the problem of ellipse extraction is often computationally demanding using traditional Hough Transform, since an ellipse is characterized by five parameters. Based on the generalized K-RASAC algorithm, we develop a new ellipse extraction algorithm using the concept of quadratic curve cluster and random sampling technique. We first develop a new representation of quadratic curves, which facilitates quantization and voting for the parameter λ that represents a candidate ellipse among the quadratic curves. Then, after selecting two tangent points and calculating the quadratic parameter equation, we vote for the parameter λ to determine an ellipse. Thus the problem of ellipse extraction is reduced into finding the local minimum in the λ accumulator array. The methods presented have been applied successfully to the extraction of lines and ellipses from synthetic and real underwater images, serving as a basic computer vision module of the underwater objects recognition system. Compared to the standard RHT line extraction method and K-RANSAC ellipse extraction method, our methods have the attractive advantages of obtaining robustness and high precision simultaneously while preserving the merits of high computation speed and small storage requirement.
