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Development of Image Stitching Using Feature Detection and Feature Matching Techniques

机译:使用特征检测和特征匹配技术的图像拼接的开发

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Image stitching is a process of merging two or more images of the scene into single image of high resolution which is also termed as panoramic image. Image stitching is used to assimilate information from several images by overlapping view fields to create a panoramic view without loss of any information. Stitching of images can be performed by two types of common approaches such as direct and indirect techniques. Direct techniques involve direct comparison of image pixel intensities that are combining. Indirect techniques are dependent on image features. These techniques incorporate feature detection and feature matching of the images to be stitched. In this proposed work, efficient feature detection and feature matching techniques such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST), Euclidean distance and Random sample consensus (RANSAC) are used for the process of image stitching. Images of a scene are captured with fifteen degree difference. These images are pre-processed if needed and then fed to feature detection and matching process. Using the matched feature points, images are stitched to get a 360 degree scene in a single image as outcome. The FAST technique gives good matching points of images using RANSAC which help in obtaining a better stitched image with integrating all the data of different images involved in stitching. The experiment is carried out for two datasets and average detected feature points and matched points for three algorithms are computed. The SIFT algorithm gives 36 matched points among 319 detected points, SURF gives 55 matched points among 334 detected feature points and FAST gives 102 matched points among 774 detected feature points. Image stitching finds application in various fields such as medical imaging, satellite imagery, automobile industries, image stabilization, document mosaicing and further image stitching can be extended to video stitching.
机译:图像拼接是将场景的两个或多个图像合并成高分辨率的单个图像的过程,该图像也被称为全景图像。通过重叠视图字段使用图像拼接来通过重叠视图来吸收来自多个图像的信息,以创建全景视图而不会丢失任何信息。图像的缝合可以通过两种类型的常见方法进行,例如直接和间接技术。直接技术涉及直接比较是组合的图像像素强度。间接技术取决于图像特征。这些技术包括要缝合的图像的特征检测和特征匹配。在该提出的工作中,有效的特征检测和特征匹配技术,如尺度不变的功能变换(SIFT),加速稳健的功能(SURF)和来自加速段测试(快速),欧几里德距离和随机样本共识(RANSAC)的特征用于图像缝合的过程。场景的图像被捕获,有十五度差异。如果需要预处理这些图像,然后馈送到特征检测和匹配过程。使用匹配的特征点,缝合图像以在单个图像中获得360度的场景作为结果。快速技术使用Ransac提供了良好的图像匹配点,这有助于获得更好的缝合图像,其中与拼接中涉及的不同图像的所有数据集成。实验是针对两个数据集进行的,并且计算了三个算法的平均检测特征点和匹配点。 SIFT算法在319个检测点之间提供36个匹配点,SHUT在334个检测的特征点之间提供55个匹配点,并且在774个检测到的特征点之间快速给出102个匹配点。图像拼接在各种领域发现应用,如医学成像,卫星图像,汽车行业,图像稳定,文件拼接和其他图像缝合,可以扩展到录像。

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