A local image descriptor based on radial and angular gradient intensity histogram for blurred image matching

Sadeghi, Bahman; Jamshidi, Kamal; Vafaei, Abbas; Monadjemi, S. Amirhassan

doi:10.1007/s00371-018-01616-z

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…True-match-rate, False-match-rate and 1-precision factors are declared as follows: The threshold T is varied to obtain the curves. A perfect descriptor would give a recall equal to 1 for any precision [52], [53]. In another word, both the curve ((1-Precision) Truematch-rate) and the curve((False-match-rate)True-match-rate) are above and left, the efficiency of its algorithm is higher.…”

Section: B Baselinementioning

confidence: 99%

“…Then, we calculate the final registration results of SARoptics using a variety of data sets including Gaussian noise and salt-and-pepper noise. By calculating the matching results of Optical-Patch and SAR-Patch, the experiment uses a criterion [52], [53] calculated based on the number of true and false matches obtained per image pair. Assume two detected key points, A, and B, with their descriptors, DA, and DB, are selected from reference and target images, respectively.…”

Section: B Baselinementioning

confidence: 99%

See 1 more Smart Citation

Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Liao

Zhou³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: B Baselinementioning

confidence: 99%

Section: B Baselinementioning

confidence: 99%

Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Liao

Zhou³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Sadeghi proposed a histogram that combines the advantages of gradient and intensity features (RAGIH). Extensive experiments on the challenging Oxford data set show that this descriptor has good performance [3]. By introducing an efficient image retrieval method based on features, matching measures, and subspace selection, Mosbah selected relevant feedback information that relies on user injection.…”

Section: Related Workmentioning

confidence: 99%

Construction of Automatic Matching Recommendation System for Web Page Image Packaging Design Based on Constrained Clustering Algorithm

Kong

2022

Mobile Information Systems

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With the rapid development of artificial intelligence technology, computer vision science has also gained new opportunities. As the foundation of computer vision and numerous artificial intelligence applications, image matching technology has received extensive attention from researchers and companies around the world. However, in web design, the research on the image matching system is not mature enough, which results in a series of problems such that the web design is not beautiful enough, and the figures do not conform to the design theme. Therefore, it is the current trend to deeply study the structure of the automatic matching recommendation system for web page image packaging design. The purpose of this paper is to use the constrained clustering algorithm to study how to construct an automatic matching recommendation system for web page image packaging design. This paper first gives a general introduction to the classification of constrained clustering algorithms. Then, the operation mechanism and model establishment of SURF feature description operator, SIFT feature description operator, and ORB feature description operator are described in detail. Then, through experiments, the matching accuracy of the web page image matching system based on the constrained clustering algorithm and the influence of parameter changes are compared with other algorithms. Finally, a comparative experiment is carried out on the image matching effects of the three feature description operators. The matching speed, noise sensitivity, and rotation type experiments are introduced respectively. By constructing the web page image packaging design of the constrained clustering algorithm to automatically match the algorithm model of the recommender system and experimenting with the model, the advantages of the constrained clustering algorithm in the model construction are proved. The experimental results show that the constrained clustering algorithm has higher image matching efficiency and matching accuracy, and the accuracy of image feature extraction is better than other algorithms. However, when the network structure division attribution threshold is ϕ = 0.4 , the clustering performance of the constrained clustering algorithm is better. Compared with the parameter 100, when the parameter is 500 and 1000, the accuracy of the constrained clustering algorithm can be improved, and the calculation accuracy is increased by 0.317.

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Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

Karanwal

Diwakar

2022

Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)

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A local image descriptor based on radial and angular gradient intensity histogram for blurred image matching

Cited by 7 publications

References 31 publications

Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Construction of Automatic Matching Recommendation System for Web Page Image Packaging Design Based on Constrained Clustering Algorithm

Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

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