Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Xi, Wenfei; Shi, Zhenguo; Li, Dongsheng

doi:10.1515/phys-2017-0053

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…It depends on the characteristics of feature on the image themselves. Images containing 'round feature shape' like vegetation, shrubbery area and etc were suited with 'blob' detectors (SIFT and SURF) since it more efficient on those conditions [54] [55] and [56]. SURF detectors performed efficiently in multispectral face recognitions where the combinations of conventional SURF detectors and FREAK descriptions extracted highest number of feature matching compare others algorithms [57].…”

Section: Multispectral Image Matchingmentioning

confidence: 99%

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

2023

IJG

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SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.

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Section: Multispectral Image Matchingmentioning

confidence: 99%

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

2023

IJG

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“…As a result, SIFT is more suitable for obtaining images in forestry, shrubbery, and grassland. Another drawback is SIFT requires large complex computations, resulting in lower imagematching performance [10].…”

Section: Sift Image Matchingmentioning

confidence: 99%

Multispectral’s Three-Dimensional Model Based on SIFT Feature Extraction

2023

IJG

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Recently, multispectral images can be captured not only from satellite sensors but also from cameras. Hence, using the photogrammetric approach, multispectral images can be manipulated to generate a three-dimensional model. The main issues regarding multispectral images were the low visibilities of the image features. Moreover, the tie point extractions on multispectral images were still in doubt. Hence, this paper examines the capabilities of the SIFT algorithm to extract feature points from multispectral images and generate the point cloud from the extracted feature points. This study chose a pothole as the subject of this research. The red, red edge, green, and near-infrared bands from the Parrot Sequoia camera were used to generate the pothole model. All captured images were processed using structure-from-motion (SfM) with Multi-View Stereo (MVS) technique. This study records the feature points extraction result and analysis of the pothole model and discuss it in this paper.

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“…Hough transform can detect curves and straight lines in images. In 1977, Moravec proposed Moravec operator, which uses pixel gray autocorrelation function to detect feature points [4] . In 2004, D G Lowe proposed an algorithm based on scale transformation invariance (SIFT), The algorithm is used to achieve accurate image matching [5] .…”

Section: Introductionmentioning

confidence: 99%

Research on the processing algorithm of drilling image

Yuan

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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In geological exploration, the texture and context information generated by different rocks on the inner wall of the drilling hole is of great significance to understand the geological condition. Drilling camera technology can obtain digital drilling video and hole wall images. For a long time, the method of manual interpretation is the main method of studying hole wall images. This method has problems such as poor real-time performance and low work efficiency .In this paper, the processing algorithm is designed based on the imaging characteristics of hole wall image and the method of digital image processing, including image preprocessing, image expansion and image stitching. First, the radius of the area is determined according to the image features, and then the concentric circle expansion method is used to obtain the rectangular expansion image. Finally, the SIFT feature point is used to complete the accurate matching with the Ransac algorithm, and use weighted average method to complete image fusion. The entire algorithm can obtain a panoramic expansion image of the hole wall area, laying the foundation for the quantitative analysis of drilling data.

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Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Cited by 10 publications

References 17 publications

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

Multispectral’s Three-Dimensional Model Based on SIFT Feature Extraction

Research on the processing algorithm of drilling image

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