A depth-colour image registration method based on local feature point extraction

Liu, Juan; Xiao, Ke; Gao, Guandong

doi:10.1016/j.biosystemseng.2022.05.008

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…J Liang et al applied the local feature point extraction algorithm to the matching of deep color images, achieved the matching by local feature point extraction of two types of images and optimized the affine transform matrix on this basis, and the experimental results show that the accuracy of their algorithm is 15 times higher than that of the rest of the three algorithms on average, and it has a high degree of accuracy and effectiveness [8]. Ji S et al conducted a comparative analysis of traditional image matching methods and deep learning based image matching methods by following three steps of feature extraction, feature description, and similarity computation, and the experimental results show that the effect of dataset on the model is significant, while the efficiency of the deep learning method is to be improved [9].…”

Section: Related Workmentioning

confidence: 99%

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Application of Improved KAZE Algorithm in Image Feature Extraction and Matching

Zhang,

Yan

2023

IEEE Access

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Intelligent navigation and recognition technology have continuously improved the field of image matching, so how to achieve more efficient and accurate feature matching is the key to image processing. This study provides a detailed introduction to the calculation methods, advantages and disadvantages of various algorithms such as SIFT, ORB and KAZE. Finally, the KAZE algorithm was selected because it can better handle edge information. However, this algorithm still has drawbacks such as high computational cost. Therefore, starting from the feature description and similarity calculation, the study introduced the concepts of second-order degree values, circular rotation characteristics, and expected distance and intercept distance to improve it, which is called the algorithm. Theoretically, this algorithm can greatly reduce the computational burden of the algorithm. To further validate the effectiveness of the improved algorithm, the study selected five types of image groups from the Mikolajczyk standard image database, including changes in image scale, brightness, and perspective. Simulation experiments were conducted on the improved algorithm and the remaining five algorithms. The experimental results show that compared to the original algorithm, Li-KAZE reduces the overall running time by 27.9% and improves the average matching rate by 0.12%. Compared to other SIFT and other algorithms, Li-KAZE also has the best performance among all algorithms. When faced with changes in compression values and extensive changes in graph groups, its highest repeatability reaches 99%. In summary, the comprehensive performance of the Li-KAZE algorithm has achieved the goal of improvement.

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Section: Related Workmentioning

confidence: 99%

“…In equation (8), o represents the group number; s presentation layer number; 0 σ represents the benchmark scale parameter; Number of images NOS =× . i σ is different from the dimension of evolution time i t , so the conversion process shown in formula ( 9) is required:…”

Section: B Optimization and Feature Matching Design Of Kaze Algorithmmentioning

confidence: 99%

Application of Improved KAZE Algorithm in Image Feature Extraction and Matching

Zhang,

Yan

2023

IEEE Access

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“…The research outcomes provided reference for feature extraction and retrieval in other image processing fields. Liang et al (2022) proposed a local feature point extraction (LFPE) algorithm for depth color image registration. To evidence algorithm's accuracy and efficacy, comparisons were made with FAST, SURF, and BRISK algorithms.…”

Section: Introductionmentioning

confidence: 99%

Crop Disease and Pest Identification Technology Based on Acpso-SVM Algorithm Optimization

Dong,

Wang

2023

Eng. Agríc.

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Research on the classification and identification of crop diseases and pests can help farmers quickly prevent crop diseases and pests. A crop disease and pest identification model based on adaptive chaotic particle swarm optimization algorithm is raised. The model introduces swarm intelligence algorithm to optimize the features of image extraction. Then the adaptive inertia weight is used to improve the optimization performance of PSO, and the support vector is used to accurately classify crop pests and diseases. Finally, the model is trained by simulation experiment to evaluate the performance of the model and analyze the performance. The model has a good performance in the experiment, the model has a clear recognition effect in the color feature extraction of pests and diseases, and the recognition accuracy is 95.08% after combining the texture feature. Moreover, in the visual transformation of 20°-40°, the recognition accuracy of the model is above 90%. In practical application, the average accuracy of the model is 91.78%, which is 3.71% higher than that of the comparison algorithm. In comparison experiments, the classification accuracy of the proposed models is above 90%. The experimental outcomes denote that the proposed algorithm has good effectiveness in identifying crop diseases and pests.

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