Improved image matching algorithm based on LK optical flow and grid motion statistics

Liu, Qunpo; Xi, ulei; Zhang, Weicun; Yang, Lingxiao; Hanajima, Naohiko

doi:10.1504/ijcat.2022.123238

Cited by 7 publications

(4 citation statements)

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“…Table 3 demonstrates that in the outlier elimination algorithm comparison experiments, the dataset Odataset includes three motion pattern datasets (Normal amplitude motion scene dataset n_datas, small amplitude motion scene dataset s_datas, and large amplitude motion scene dataset l_datas), each of which consists of the inlier optical flow values (u gt , v gt ) and the outlier optical flow values (u ft , v ft ). [34] bamboo_2(frame_0001 ∼ 0010) [34] temple_2.1(frame_0001 ∼ 0010) [34] temple_2.2(frame_0011 ∼ 0021) [34] 15-50 pixels s_datas 30 group 100 alley_1(frame_0001 ∼ 0016) [34] FlyingChairs (6,7,11,12,16,33,40,78,82 104) [35] Middlebury(grov(2 ∼ 3)、urban(2 ∼ 3)、venus) [36] >15 pixels l_datas 40 group 100 ambush_2.1(frame_0001 ∼ 0010) [34] ambush_2.2(frame_0010 ∼ 0021) [34] market_5.1(frame_0001 ∼ 0010) [34] market_5.2(frame_0011 ∼ 0021) [34] <50 pixels…”

Section: Datasetsmentioning

confidence: 99%

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Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Wang,

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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During the tracking of moving targets in dynamic scenes, efficiently handling outliers in the optical flow and maintaining robustness across various motion amplitudes represents a critical challenge. So far, studies have used thresholding and local consistency based approaches to deal with optical outliers. However, there is subjectivity through expert-defined thresholds or delineated regions, and therefore these methods do not perform consistently enough under different target motion amplitudes. Other studies have focused on complex statistical-mathematical modelling which, although theoretically valid, requires significant computational resources. Aiming at the above problems this paper proposes a new method to calculate the optical outliers by using stochastic neighborhood graph combined with the Borda counting method, which reduces the computation amount on the basis of objectively eliminating the outliers. Sparse optical flow values are used as the overall population and the outlier and inlier sparse optical flow values are used as samples. Analyze the dissimilarity between sparse optical flow data points, obtaining the dissimilarity matrix, introducing the Gaussian function to smooth and reduce the dimensionality of the dissimilarity matrix, and then normalizing the smoothing matrix to generate the binding matrix, where the probability sum of each node to other nodes in the matrix is equal to 1. Stochastic neighborhood graphs are then generated based on a binding matrix to obtain the outlier probabilities of data points in different neighborhood graphs, and outlier samples are obtained based on the probability. To avoid the subjectivity of the expert thresholds, the outlier probabilities are weighted and ranked to calculate the data point Borda scores to obtain accurate optical outliers. The experimental results show that the method in this paper is robust to different amplitude motions and real scenarios, and the accuracy, precision and recall of outliers elimination are better than the current mainstream algorithms.

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Section: Datasetsmentioning

confidence: 99%

“…Sparse optical flow value (SOF) [6] is the calculation of a SOF field using the main features of the moving target (corner points, contours, texture features, etc.) and estimating the position of the main features in the next image frame by comparing the pixels of two adjacent frames.…”

Section: Introductionmentioning

confidence: 99%

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Wang,

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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“…Feature matching is a key content to implement technologies such as image recognition [1] [2] . It is widely used in different fields, such as target recognition and tracking, intelligent substation inspection and medical image processing [3]- [6] .…”

Section: Introductionmentioning

confidence: 99%

Improved AKAZE feature matching algorithm based on grid motion statistics

Wang,

Ji,

Lang

et al. 2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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Aiming at the problem that AKAZE algorithm has slow feature extraction speed and low accuracy in the feature matching process, this paper proposes to improve AKAZE's feature matching algorithm based on grid statistical motion. Firstly, in the feature extraction stage, the proposed algorithm uses the oFAST algorithm instead of constructing scale space to extract feature points. Then, the M-LDB descriptor is used for feature point description. Finally, the BF algorithm is used to perform coarse matching of features, and the grid motion statistics (GMS) algorithm is added to achieve the purification of matching point pairs and complete the matching. The performance of the proposed algorithm was compared with the AKAZE and ORB algorithms in the experimental fields and grayscale graph groups. The results show that the improved algorithm not only improves the matching speed, which is more than 2 times faster than the AKAZE algorithm, but also maintains a high matching accuracy, which is similar to the AKAZE algorithm.

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“…Although most of the scale can be preserved, the data information of certain details is still not accurate enough, and the final image matching effect is poor. Nonlinear scale space effectively solves this problem and can make the image matching results more accurate [3].…”

Section: Introductionmentioning

confidence: 99%

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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Improved image matching algorithm based on LK optical flow and grid motion statistics

Cited by 7 publications

References 0 publications

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

Improved AKAZE feature matching algorithm based on grid motion statistics

Application of Improved KAZE Algorithm in Image Feature Extraction and Matching

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