MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Mobile Networks and Applications
Detecting Independent Motion: The Statistics of Temporal Continuity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mosaic representations of video sequences based on slice image analysis
Pattern Recognition Letters
Guest Introduction: The Changing Shape of Computer Vision in the Twenty-First Century
International Journal of Computer Vision
Vision-Based Single-Stroke Character Recognition for Wearable Computing
IEEE Intelligent Systems
Presence: Teleoperators and Virtual Environments - Mixed reality
Image Registration for Foveated Omnidirectional Sensing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Double Hierarchical Algorithm for Video Mosaics
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Super-Resolution Image Restoration from Blurred Low-Resolution Images
Journal of Mathematical Imaging and Vision
A vision-based postproduction tool for footage logging, analysis, and annotation
Graphical Models - Special issue: Vision and computer graphics
CHI EA '97 CHI '97 Extended Abstracts on Human Factors in Computing Systems
Enhanced Biggs---Andrews Asymmetric Iterative Blind Deconvolution
Multidimensional Systems and Signal Processing
Journal of Mathematical Imaging and Vision
Lie algebra approach for tracking and 3D motion estimation using monocular vision
Image and Vision Computing
An efficient algorithm for superresolution in medium field imaging
Multidimensional Systems and Signal Processing
A fast algorithm for image super-resolution from blurred observations
EURASIP Journal on Applied Signal Processing
Binary Image Registration Using Covariant Gaussian Densities
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
An interactive interface for seizure focus localization using SPECT image analysis
Computers in Biology and Medicine
Environmental sound classification based on feature collaboration
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
One-dimensional mapping for estimating projective transformations
IEEE Transactions on Image Processing
Global motion estimation: feature-based, featureless, or both ?!
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
FlexCam: using thin-film flexible OLED color prints as a camera array
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Recovering projective transformations between binary shapes
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
International Journal of Computer Vision
Hi-index | 0.01 |
We present direct featureless methods for estimating the eight parameters of an “exact” projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image (or new image sequence) of greater resolution or spatial extent. The approach is “exact” for two cases of static scenes: (1) images taken from the same location of an arbitrary three-dimensional (3-D) scene, with a camera that is free to pan, tilt, rotate about its optical axis, and zoom, or (2) images of a flat scene taken from arbitrary locations. The featureless projective approach generalizes interframe camera motion estimation methods that have previously used a camera model (which lacks the degrees of freedom to “exactly” characterize such phenomena as camera pan and tilt) and/or which have relied upon finding points of correspondence between the image frames. The featureless projective approach, which operates directly on the image pixels, is shown to be superior in accuracy and the ability to enhance the resolution. The proposed methods work well on image data collected from both good-quality and poor-quality video under a wide variety of conditions (sunny, cloudy, day, night). These new fully automatic methods are also shown to be robust to deviations from the assumptions of static scene and no parallax