Algorithm for Sequence Image Automatic Mosaic Based on SIFT Feature

Li, Lin-na; Geng, Nan

doi:10.1109/icie.2010.55

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…The specific process is as follows:Feature extraction: Feature extraction refers to extracting the common image features in the two images separately. In this paper, the FAST corner detection method is used to extract features 36 . Before FAST corner detection method, SIFT and SURF extracted excellent features, but the time consumption was still very high, and FAST solved this problem to some extent.…”

Section: Proposed Methodsmentioning

confidence: 99%

Vehicle video surveillance system based on image fusion and parallel computing

Liu

Lyu

Gong

2020

Circuit Theory & Apps

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Summary Autonomous driving has gradually moved towards practical applications in recent years. It is particularly critical to provide reliable real‐time environmental information for autonomous driving systems. At present, vehicle video surveillance systems based on multi‐source video and target detection algorithms can effectively solve these problems. However, the previous vehicle video surveillance systems are often unable to balance the surveillance effect and the surveillance frame rate. Therefore, we will introduce a vehicle video surveillance system based on parallel computing and computer vision in this article. First, multiple fisheye cameras are used to collect surround‐view environmental information. Second, we will use a low‐light camera, infrared thermal imager, and millimeter‐wave radar to provide forward‐view environmental information at night. Correspondingly, we designed the surround‐view image fusion algorithm and the forward‐view image fusion algorithm based on parallel computing. At last, a monocular camera and detection algorithms are used to provide forward‐view detection results. In a word, this vehicle video surveillance system will benefit the practical application of autonomous driving.

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Section: Proposed Methodsmentioning

confidence: 99%

Vehicle video surveillance system based on image fusion and parallel computing

Liu

Lyu

Gong

2020

Circuit Theory & Apps

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“…In order to realize the mosaic of incomplete defect images, firstly, the feature of the target image is extracted based on SIFT algorithm, and the feature points of the defect image are obtained. Through the description of the feature points, the feature point set is established, and the transformation matrix is constructed by feature matching, and finally, the defect image fusion is realized [30]. Firstly, the multi-scale features of simulated image data in scale space are constructed using Equations ( 10) and (11).…”

Section: Pretreatment Of Borehole Wall Damage Image 421 Defect Image ...mentioning

confidence: 99%

Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft

Tang

Gao

et al. 2023

Applied Sciences

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The coal mine wind shaft is an important ventilation channel in coal mines, and it is of great significance to ensure its long-term safety. At present, the inspection of wind shafts still depends on manual work, which has low reliability and high risk. There are two main problems in the shaft wall detection of ventilation shafts: (1) The humidity and dust concentration in ventilation shafts are high, which makes imaging difficult; (2) the cracks on the shaft wall are long and irregular, so it is impossible to acquire the information of the whole crack from a single photo. Firstly, the mapping analysis between the concentration of water vapor and dust in the wind shaft and the image definition is determined by experiments. Then, the inspection robot is designed to move along the axial and circumferential directions to get close to the shaft wall, and the rack-and-rail drive design is adopted to ensure the real-time position feedback of the robot. Then, through the crack parameter detection method based on depth learning, the movement direction of the robot is controlled according to the crack direction so as to ensure that the complete crack parameters are obtained. Finally, the crack detection algorithm is verified by experiments.

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“…The limitation of this algorithm is, it works with the optimal case because this method is not excellent when the distorted transformations occurred or the gray values difference between the two images is found. [7] Presents an approach for the panoramic view generation. First, salient features are robustly detected from the input images by a robust algorithm called Scale Invariant Feature Transform (SIFT).…”

Section: Related Workmentioning

confidence: 99%