A multiresolution spline with application to image mosaics
ACM Transactions on Graphics (TOG)
International Journal of Computer Vision
IEEE Computer Graphics and Applications
Using geometric corners to build a 2D mosaic from a set of image
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mosaics of Scenes with Moving Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ACM SIGGRAPH 2003 Papers
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Vignette and Exposure Calibration and Compensation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Color Matching in Colour Remote Sensing Image
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
Image stitching with dynamic elements
Image and Vision Computing
Joint spatial and tonal mosaic alignment for motion detection with PTZ camera
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
| Hi-index | 0.00 |
This paper presents a tone compensation method between images to make a seamless panoramic image. Different camera settings of input images, including white-balance, exposure time, and f-stops, affect the overall color tone of a resultant panoramic image. Although numerous methods have been proposed to deal with such color variations for seamless image stitching, most of them do not properly consider the dynamic scene in which different scene contents exist in input images. In this paper, we propose an efficient method that takes dynamic scene contents into account for compensating color tone difference. The proposed approach consists of three steps. First, we compensate the color tone difference by using the linear color transform with robust local features. Second, we filter out dynamic objects (i.e., dynamic scene contents) by measuring similarity between the linear transformed image and the reference image. Finally, we precisely correct the color variation with detected consistent regions only. The qualitative evaluation shows superior or competitive results compared to commercially available products.