Computer
Capturing and viewing gigapixel images
ACM SIGGRAPH 2007 papers
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Mosaicing of Fibered Fluorescence Microscopy Video
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Natural and seamless image composition with color control
IEEE Transactions on Image Processing
Simulating classic mosaics with graph cuts
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Color and luminance compensation for mobile panorama construction
Proceedings of the international conference on Multimedia
Color and luminance compensation for mobile panorama construction
Proceedings of the international conference on Multimedia
Reference-guided exposure fusion in dynamic scenes
Journal of Visual Communication and Image Representation
Fast high dynamic range image deghosting for arbitrary scene motion
Proceedings of Graphics Interface 2012
Robust patch-based hdr reconstruction of dynamic scenes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging
A Fast and reliable image mosaicing technique with application to wide area motion detection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Exposure stacks of live scenes with hand-held cameras
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Auto-calibration for image mosaicing and stereo vision
Transactions on Computational Science XIX
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This paper presents a technique to automatically stitch multiple images at varying orientations and exposures to create a composite panorama that preserves the angular extent and dynamic range of the inputs. The main contribution of our method is that it allows for large exposure differences, large scene motion or other misregistrations between frames and requires no extra camera hardware. To do this, we introduce a two-step graph cut approach. The purpose of the first step is to fix the positions of moving objects in the scene. In the second step, we fill in the entire available dynamic range. We introduce data costs that encourage consistency and higher signal-to-noise ratios, and seam costs that encourage smooth transitions. Our method is simple to implement and effective. We demonstrate the effectiveness of our approach on several input sets with varying exposures and camera orientations.