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JBE, vol. 22, no. 6, pp.713-723, November, 2017

DOI: https://doi.org/10.5909/JBE.2017.22.6.713

Images Grouping Technology based on Camera Sensors for Efficient Stitching of Multiple Images

Jiheon Im, Euisang Lee, Hoejung Kim and Kyuheon Kim

C.A E-mail: kyuheonkim@khu.ac.kr

Abstract: Since the panoramic image can overcome the limitation of the viewing angle of the camera and have a wide field of view, it has been studied effectively in the fields of computer vision and stereo camera. In order to generate a panoramic image, stitching images taken by a plurality of general cameras instead of using a wide-angle camera, which is distorted, is widely used because it can reduce image distortion. The image stitching technique creates descriptors of feature points extracted from multiple images, compares the similarities of feature points, and links them together into one image. Each feature point has several hundreds of dimensions of information, and data processing time increases as more images are stitched. In particular, when a panorama is generated on the basis of an image photographed by a plurality of unspecified cameras with respect to an object, the extraction processing time of the overlapping feature points for similar images becomes longer. In this paper, we propose a preprocessing process to efficiently process stitching based on an image obtained from a number of unspecified cameras for one object or environment. In this way, the data processing time can be reduced by pre-grouping images based on camera sensor information and reducing the number of images to be stitched at one time. Later, stitching is done hierarchically to create one large panorama. Through the grouping preprocessing proposed in this paper, we confirmed that the stitching time for a large number of images is greatly reduced by experimental results.

Keyword: Panorama, Stitching, K-mean clustering, GPS, Azimuth, FOV, SURF

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