• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.201-209
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• 2011

This paper suggests a panorama image stitching system which consists of an image matching algorithm: modified SURF (Speeded Up Robust Feature) and an image blending algorithm: multi-band blending. In this paper, first, Modified SURF is described and SURF is compared with SIFT (Scale Invariant Feature Transform), which also gives the reason why modified SURF is chosen instead of SIFT. Then, multi-band blending is described, Lastly, the structure of a panorama image stitching system is suggested and evaluated by experiments, which includes stitching quality test and time cost experiment. According to the experiments, the proposed system can make the stitching seam invisible and get a perfect panorama for large image data, In addition, it is faster than the sift based stitching system.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.569-576
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• 2012

This paper presents an x-ray medical image panorama system which can overcome the smallness of the images that exist on a source computer during remote medical processing. In the system, after the standard medical image format DICOM is converted to the PC standard image format, a MSR algorithm is used to enhance X-ray images of low quality. Then SURF and Multi-band blending are applied to generate a panoramic image. Also, this paper evaluates the proposed SURF based system through the average gray value error and image quality criterion with X-ray image data by comparing with a SIFT based system. The results show that the proposed system is superior to SIFT based system in image quality.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1537-1549
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• 2020

The ground sample distance of KOMPSAT-3 is 0.7 m for panchromatic band, 2.8 m for multi-spectral band, and the swath width of KOMPSAT-3 is 16 km. Therefore, an image of an area wider than the swath width (16 km) cannot be acquired with a single scanning. Thus, after scanning multiple areas in units of swath width, the acquired images should be made into one image. At this time, the necessary algorithm is called image mosaicking or image stitching, and is used for cartography. Mosaic algorithm generally consists of the following 4 steps: (1) Feature extraction and matching, (2) Radiometric balancing, (3) Seamline estimation, and (4) Image blending. In this paper, we have studied an effective seamline estimation method for satellite images. As a result, we can estimate the seamline more accurately than the existing method, and the heterogeneity of the mosaiced images was minimized.