Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

Zheng, Zhaohui; Zheng, Hong; Ma, Yong; Fan, Fan; Ju, Jianping; Xu, Bichao; Li, Mingyu; Cheng, Shijie

doi:10.3390/rs11121418

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…design orthopedic disease aided diagnosis with its source code. Based on these factors, this paper independently designed a computer-aided diagnosis system for orthopedic diseases [25] and realized the auxiliary diagnosis functions of gout and cartilage defect with the help of this system.…”

Section: Figure 3: Relationship Diagram Of Mutual Information Entropy H(f(i) R(j)) H(r(j)) H(r(j)|f(i))mentioning

confidence: 99%

“…At present, the computer-aided diagnosis system, which is widely used in the field of orthopedic diagnosis, generally has the characteristics of broad functions and mature design, which can realize routine orthopedic disease diagnosis, but it is not effective for complex or specific orthopedic diseases, and it is basically not open source, so it is impossible to design orthopedic disease aided diagnosis with its source code. Based on these factors, this paper independently designed a computer-aided diagnosis system for orthopedic diseases [ 25 ] and realized the auxiliary diagnosis functions of gout and cartilage defect with the help of this system.…”

Section: Design Of Computer-aided Diagnosis System For Orthopedic Diseasesmentioning

confidence: 99%

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An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

Guo

Chen

2021

Journal of Healthcare Engineering

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There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis. The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. Through the linear search strategy of golden section method and Powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.

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Section: Figure 3: Relationship Diagram Of Mutual Information Entropy H(f(i) R(j)) H(r(j)) H(r(j)|f(i))mentioning

confidence: 99%

Section: Design Of Computer-aided Diagnosis System For Orthopedic Diseasesmentioning

confidence: 99%

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

Guo

Chen

2021

Journal of Healthcare Engineering

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“…In this paper, the accuracy [34] is taken as the evaluation index of algorithm accuracy. Set the slope of the matching line segment [39] between two horizontal matching feature points on the matching image as the standard slope, and then compare the slope of other matching line segments with it. If the slopes are equal, it is a valid match, and the correct matching logarithm is obtained; otherwise, it is a wrong match.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Study on Optimization Method of Visual Odometry Based on Feature Matching

Song

Zhao

Ren

et al. 2022

Mathematical Problems in Engineering

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The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided.

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“…Then a feature point matching algorithm is used to match each region. First, the OBR feature points [23] of the sample image and the image to be registered are extracted ; then the feature point matching algorithm [24], [25] is used to quickly match and screen the area to be measured. Finally, the rotation angle and displacement are calculated.…”

Section: A Initial Target Matchingmentioning

confidence: 99%

“…After extracting orb feature points, we use the FUGC algorithm [25] for feature point matching. First, we use brute force to construct a set of hypothetical matches for orb feature points.…”

Section: A Initial Target Matchingmentioning

confidence: 99%

Robot Target Location Based on the Difference in Monocular Vision Projection

Zheng

Deng

et al. 2023

IEEE Access

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Visual guidance is widely used in industrial robots. At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance. However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced. A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image. First, the contour region of the template image is divided according to the position of the grasping points. Then the weight is assigned according to the distance from each contour region to the grasping points. Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured. Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately. A large number of experiments show that the method has the design requirements of high stability and high accuracy.

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Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

Cited by 7 publications

References 27 publications

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

Study on Optimization Method of Visual Odometry Based on Feature Matching

Robot Target Location Based on the Difference in Monocular Vision Projection

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