Evaluation of Image Feature Detection and Matching Algorithms

Ou, Yiwen; Cai, Zhiming; Lü, Jian; Dong, Jian; Ling, Yufeng

doi:10.1109/icccs49078.2020.9118480

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Brute-force matches the descriptors with hamming metrics with 2 descriptors obtained in the previous step using the match_descriptors function provided by scikit-image package. A pair of feature points is considered matched when the distance between their descriptors is below a certain threshold [9].…”

Section: Feature Points Matchingmentioning

confidence: 99%

A comparison of feature extraction methods in image stitching

Zheng

2023

ACE

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The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.

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Section: Feature Points Matchingmentioning

confidence: 99%

A comparison of feature extraction methods in image stitching

Zheng

2023

ACE

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“…6. Several keypoint evaluations have been proposed in the literature [5,28,29]. However, there is no consensus on a universally optimal detector for all possible image geometrical and photometric variations [23].…”

Section: Tablementioning

confidence: 99%

Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint燚etectors

Rube¹

2022

Computers, Materials &Amp; Continua

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The repeatability rate is an important measure for evaluating and comparing the performance of keypoint detectors. Several repeatability rate measurements were used in the literature to assess the effectiveness of keypoint detectors. While these repeatability rates are calculated for pairs of images, the general assumption is that the reference image is often known and unchanging compared to other images in the same dataset. So, these rates are asymmetrical as they require calculations in only one direction. In addition, the image domain in which these computations take place substantially affects their values. The presented scatter diagram plots illustrate how these directional repeatability rates vary in relation to the size of the neighboring region in each pair of images. Therefore, both directional repeatability rates for the same image pair must be included when comparing different keypoint detectors. This paper, firstly, examines several commonly utilized repeatability rate measures for keypoint detector evaluations. The researcher then suggests computing a two-fold repeatability rate to assess keypoint detector performance on similar scene images. Next, the symmetric mean repeatability rate metric is computed using the given two-fold repeatability rates. Finally, these measurements are validated using well-known keypoint detectors on different image groups with various geometric and photometric attributes.

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Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control

Shen,

Zhan,

Tang

et al. 2024

ACS Appl. Mater. Interfaces

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Substrate oxidation is inevitable when exposed to ambient atmosphere during semiconductor manufacturing, which is detrimental to the fabrication of state-of-the-art devices. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for random substrates poses a multidimensional challenge and is sometimes controversial. Due to variations in substrates and growth processes, the determination of the deoxidation condition heavily relies on the individual's expertise, yielding inconsistent results. This study employs a machine learning model that integrates interpolation and vision transformer (Interpolation-ViT) techniques. The model utilizes reflection high-energy electron diffraction videos as input to predict the status of the substrate, enabling automated deoxidation within a controlled architecture for various substrates. Furthermore, we highlight the potential of models trained on data from specific MBE equipment to achieve high-accuracy deployment on different pieces of equipment. In contrast to traditional methods, our approach holds exceptional value, as it standardizes deoxidation temperatures across diverse equipment and substrates. This significantly advances the standardization of the semiconductor process. The concepts and methods presented are expected to revolutionize semiconductor manufacturing processes in the optoelectronic and microelectronic industries.

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Evaluation of Image Feature Detection and Matching Algorithms

Cited by 6 publications

References 12 publications

A comparison of feature extraction methods in image stitching

A comparison of feature extraction methods in image stitching

Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint燚etectors

Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control

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