imagematching is a basic problem in image processing and pattern recognition. It is used to calculate the visual similarity between images taken in the same scene with different sensors, different perspectives or at ...
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imagematching is a basic problem in image processing and pattern recognition. It is used to calculate the visual similarity between images taken in the same scene with different sensors, different perspectives or at different times. In addition to image adjustment, it is an indispensable step in image analysis and digital photogrammetry. It is also important for applications such as automatic navigation, image processing, medical image analysis, and motion estimation. The current image adjustment technology can be divided into three categories: domain-based image conversion technology, gray-scale-based technology, and performance-based technology. Among them, the feature-based matchingalgorithm directly matches the features of the image, so it greatly improves the calculation efficiency and is easy to adapt to complex image transformations, such as geometric distortion, different resolutions, and image transformations at different angles. imagematching refers to the process of using effective matchingalgorithms to find the same or similar cue points for two or more image data. In applications such as medical image processing and analysis, remote sensing monitoring, weapon movement and image processing, imagematching technology is an important step. images have strong structural features, such as corners, edges, statistics, and textures. These functions play an important role in imagematching and scanning technology. The key to many imagematching problems depends on selection, detection and expression. For different imagematching problems, different functions are selected, and the matching results may be very different.
In this article interactive visualization techniques for creating virtual objects are considered. We describe the most common methods of photometric transformations between images and a variety of geometric objects co...
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ISBN:
(纸本)9781509048151
In this article interactive visualization techniques for creating virtual objects are considered. We describe the most common methods of photometric transformations between images and a variety of geometric objects contextualized against the backdrop of increased adoption of 360 videos and virtual reality systems. Two techniques, the Harris-Laplacian and the Scale Invariant Feature Transform (SIFT) have been described. The algorithm estimation of virtual objects interactive visualization is given. The image-matchingalgorithm using key points is described.
A fast matchingalgorithm is proposed aiming at the fault of large calculation quantity in the correlation matchingalgorithm. A coarse-to-fine strategy is adopted in the new algorithm, i.e., a circular template is us...
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A fast matchingalgorithm is proposed aiming at the fault of large calculation quantity in the correlation matchingalgorithm. A coarse-to-fine strategy is adopted in the new algorithm, i.e., a circular template is used in matching area to complete the coarse matching such that the matching windows can be determined firstly, and then the whole template is used to decide the final correct window. This algorithm is applied to remote sensing imagematching. The simulation results show that the algorithm can carry out correct matching and its calculation quantity is only 10% of that of the correlation matchingalgorithm.
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