Shape Matching Based on Multi-Scale Invariant Features

Zhang, Kun; Ma, Xiao; Xin-guo, LI

doi:10.1109/access.2019.2935879

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…The effect of contour matching is closely related to the quality of the contour template [ 15 ]. This section describes the algorithm flow and the key points of contour template creation, including image preprocessing, calculating the number of adaptive pyramid layers, calculating the adaptive search angle step of the image pyramid, establishing the template image pyramid, and extracting the contour feature information of the template image.…”

Section: Design Of Contour Template Building Algorithmmentioning

confidence: 99%

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Wu,

Luo,

Sun

et al. 2024

Sensors

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Aiming at the problems of the poor robustness and universality of traditional contour matching algorithms in engineering applications, a method for improving the surface defect detection of industrial products based on contour matching algorithms is detailed in this paper. Based on the image pyramid optimization method, a three-level matching method is designed, which can quickly obtain the candidate pose of the target contour at the top of the image pyramid, combining the integral graph and the integration graph acceleration strategy based on weak classification. It can quickly obtain the rough positioning and rough angle of the target contour, which greatly improves the performance of the algorithm. In addition, to solve the problem that a large number of duplicate candidate points will be generated when the target candidate points are expanded, a method to obtain the optimal candidate points in the neighborhood of the target candidate points is designed, which can guarantee the matching accuracy and greatly reduce the calculation amount. In order to verify the effectiveness of the algorithm, functional test experiments were designed for template building function and contour matching function, including uniform illumination condition, nonlinear condition and contour matching detection under different conditions. The results show that: (1) Under uniform illumination conditions, the detection accuracy can be maintained at about 93%. (2) Under nonlinear illumination conditions, the detection accuracy can be maintained at about 91.84%. (3) When there is an external interference source, there will be a false detection or no detection, and the overall defect detection rate remains above 94%. It is verified that the proposed method can meet the application requirements of common defect detection, and has good robustness and meets the expected functional requirements of the algorithm, providing a strong technical guarantee and data support for the design of embedded image sensors in the later stage.

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Section: Design Of Contour Template Building Algorithmmentioning

confidence: 99%

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Wu,

Luo,

Sun

et al. 2024

Sensors

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“…However, corner detection methods (e.g., FAST, SUSAN, MSFD and curvature scale-space (CSS) 11 – 13 ) can generate rich and accurate corners, but are susceptible to inaccuracies for images with homogenous backgrounds and are therefore unsuitable for low-contrast images. Spot detection methods such as Hessian’s determinant or maximum stable extreme region 14 – 16 and the feature detection algorithms SIFT 17 , 18 and SURF 19 rely on extreme points in a region to detect feature points. Aldana-Iuit et al introduced a feature detector named Sadder 20 .…”

Section: Introductionmentioning

confidence: 99%

Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions

Wang

Lee

Lai

et al. 2022

Sci Rep

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In the registration of medical images, nonrigid registration targets, images with large displacement caused by different postures of the human body, and frequent variations in image intensity due to physiological phenomena are substantial problems that make medical images less suitable for intensity-based image registration modes. These problems also greatly increase the difficulty and complexity of feature detection and matching for feature-based image registration modes. This research introduces an automatic image registration algorithm for infrared medical images that offers the following benefits: effective detection of feature points in flat regions (cold patterns) that appear due to changes in the human body’s thermal patterns, improved mismatch removal through coherent spatial mapping for improved feature point matching, and large-displacement optical flow for optimal transformation. This method was compared with various classical gold standard image registration methods to evaluate its performance. The models were compared for the three key steps of the registration process—feature detection, feature point matching, and image transformation—and the results are presented visually and quantitatively. The results demonstrate that the proposed method outperforms existing methods in all tasks, including in terms of the features detected, uniformity of feature points, matching accuracy, and control point sparsity, and achieves optimal image transformation. The performance of the proposed method with four common image types was also evaluated, and the results verify that the proposed method has a high degree of stability and can effectively register medical images under a variety of conditions.

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“…The problem of registering point sets arises in a variety of applications, such as workpiece localization [1], digital elevation model generation [2], shape recognition [3], [4], and image registration [5], [6], thus laying a foundation for the implementation of many high-technology platforms. In general, the task of point set registration is to assign potential point-to-point correspondences and recover the underlying transformation that warps one point set to the other.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Nonrigid Point Set Registration via Coregularized Least Squares

Yang

Liu

et al. 2020

IEEE Access

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This paper describes a method for performing nonrigid point set registration on data with different kinds of degradation (deformation, occlusion, noise, and outliers). We formulate the registration problem as a mixture model estimation problem by employing two topologically complementary constraints in a Gaussian mixture model (GMM)-based learning framework. The first constraint is Tikhonov-based regularization, which maintains the overall spatial connectivity by moving the point set collectively and coherently. The second constraint is graph-Laplacian-based regularization embedding, which preserves the intrinsic topological characteristics of the marginal space during registration. Hence, the proposed method is named coregularized least squares (Co-RLS). Our method iteratively estimates the correspondences and nonrigid transformation between two given sets of points. First, the correspondences are established subject to the ordering information of the contour points using feature descriptors, i.e., the shape context. Then, the transformation is recovered by minimizing the Co-RLS function, and the expectation maximization method is used to update the transformation parameters and outlier ratios. Experimental results on various types of synthetic and real data show the superior effectiveness and robustness of the proposed method compared with other state-of-the-art methods.

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Shape Matching Based on Multi-Scale Invariant Features

Cited by 11 publications

References 39 publications

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions

A Unified Framework for Nonrigid Point Set Registration via Coregularized Least Squares

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