Affine Iterative Closest Point Algorithm Research Articles

Affine Iterative Closest Point Algorithm Based on Color Information and Correntropy for Precise Point Set Registration.

In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate the problem of low registration accuracy for data with weak geometric structures, we consider introducing color features into traditional affine algorithms to establish more accurate and reliable correspondences. Secondly, we introduce the correntropy measurement to overcome the influence of a large amount of noise and outliers in the RGB-D datasets, thereby further improving the registration accuracy. Experimental results demonstrate that the proposed registration algorithm has higher registration accuracy, with error reduction of almost 10 times, and achieves more stable robustness than other advanced algorithms.

A Method for Wafer Defect Detection Using Spatial Feature Points Guided Affine Iterative Closest Point Algorithm

In integrated circuit manufacturing industry, in order to meet the high demand of electronic products, wafers are designed to be smaller and smaller, which makes automatic wafer defect detection a great challenge. The existing wafer defect detection methods are mainly based on the precise segmentation of one single wafer, which relies on high-cost and complicated hardware instruments. The segmentation performance obtained is unstable because there are too many limitations brought by hardware implementations such as the camera location, the light source location, and the product location. To address this problem, in this paper, we propose a method for wafer defect detection. This novel method includes two phases, namely wafer segmentation and defect detection. In wafer segmentation phase, the target wafer image is segmented based on the affine iterative closest algorithm with spatial feature points guided (AICP-FP). In wafer defect detection phase, with the inherent characteristics of wafers, a simple and effective algorithm based on machine vision is proposed. The simulations demonstrate that, with these two phases, the higher accuracy and higher speed of wafer defect detection can be achieved at the same time. For real industrial system, this novel method can satisfy the real-time detection requirements of automatic production line.

A correntropy-based affine iterative closest point algorithm for robust point set registration

The iterative closest point (ICP) algorithm has the advantages of high accuracy and fast speed for point set registration, but it performs poorly when the point set has a large number of noisy outliers. To solve this problem, we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers. Firstly, we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model, which can avoid the influence of outliers. To maximize the objective function, we then propose a robust affine ICP algorithm. At each iteration of this new algorithm, we set up the index mapping of two point sets according to the known transformation, and then compute the closed-form solution of the new transformation according to the known index mapping. Similar to the traditional ICP algorithm, our algorithm converges to a local maximum monotonously for any given initial value. Finally, the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.

Robust Non-rigid Registration Based on Affine ICP Algorithm and Part-Based Method

The classical affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into a local minimum. As an extension of the classical affine registration algorithm, this paper first proposes an affine ICP algorithm based on control point guided, and then applies this new method to establish a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided, meanwhile, the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.

Improvement of affine iterative closest point algorithm for partial registration

In this study, partial registration problem with outliers and missing data in the affine case is discussed. To solve this problem, a novel objective function is proposed based on bidirectional distance and trimmed strategy, and then a new affine trimmed iterative closest point algorithm is given. First, when bidirectional distance measurement is applied, the ill‐posed partial registration problem in the affine case is prevented. Second, the overlapping percentage is solved by using trimmed strategy which uses as many correct overlapping points as possible. The authors’ method computes the affine transformation, correspondence and overlapping percentage automatically at each iterative step. In this way, it handles partially overlapping registration with outliers and missing data in the affine case well. Experimental results demonstrate that their method is more robust and precise than the state‐of‐the‐art algorithms. It also has good convergence and similar running time with traditional algorithms.

Robust affine iterative closest point algorithm with bidirectional distance

This study proposes a robust affine iterative closest point (ICP) algorithm based on bidirectional distance for the registration of m -dimensional ( m -D) point sets. Since the affine registration problem can be formulated as a least square (LS) problem by incorporating an affine transformation, this study first analyses the ill-posed problem of the affine registration and turn it into well-posed one by introducing the bidirectional distance into the LS formulation. Then, the corresponding affine ICP algorithm is proposed to solve the well-posed problem. By using the bidirectional distance, the proposed algorithm can directly estimate the affine transformation and converge monotonically to a local minimum from any given initial parameters. To obtain the desired global minimum, good initial parameters can be estimated by independent component analysis (ICA) technique. The proposed approach makes no geometric assumptions on point sets, so it is a general framework for affine registration of m -D point sets. Experimental results demonstrate its robustness and accuracy compared with the current state-of-the-art approaches.

Affine iterative closest point algorithm for point set registration

The traditional iterative closest point (ICP) algorithm is accurate and fast for rigid point set registration but it is unable to handle affine case. This paper instead introduces a novel generalized ICP algorithm based on lie group for affine registration of m- D point sets. First, with singular value decomposition technique applied, this paper decomposes affine transformation into three special matrices which are then constrained. Then, these matrices are expressed by exponential mappings of lie group and their Taylor approximations at each iterative step of affine ICP algorithm. In this way, affine registration problem is ultimately simplified to a quadratic programming problem. By solving this quadratic problem, the new algorithm converges monotonically to a local minimum from any given initial parameters. Hence, to reach desired minimum, good initial parameters and constraints are required which are successfully estimated by independent component analysis. This new algorithm is independent of shape representation and feature extraction, and thereby it is a general framework for affine registration of m- D point sets. Experimental results demonstrate its robustness and efficiency compared with the traditional ICP algorithm and the state-of-the-art methods.