Robust regression and outlier detection
Robust regression and outlier detection
Robust methods for estimating pose and a sensitivity analysis
CVGIP: Image Understanding
Artificial Intelligence - Special volume on computer vision
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Efficient Linear Solution of Exterior Orientation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Image Warping with Scattered Data Interpolation
IEEE Computer Graphics and Applications
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Pose Estimation for Planar Structures
IEEE Computer Graphics and Applications
Estimation of Relative Camera Positions for Uncalibrated Cameras
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We address the problem of determining the viewpoint of an image without referencing to or estimating explicitly the 3-D structure pictured in the image. Used for reference are instead a number of sample snapshots of the scene, each supplied with the associated viewpoint. By viewing image and its associated viewpoint as the input and output of a function, and the reference snapshot-viewpoint pairs as input-output samples of that function, we have a natural formulation of the problem as an interpolation one. The interpolation formulation allows imaging details like camera intrinsic parameters to be unknown, and the specification of the desired viewpoint to be not necessarily in metric terms. We describe an interpolation-based mechanism that determines the viewpoint of any given input image, which has the property that it fits all the given input-output reference samples exactly. Experimental results on benchmarking image datasets show that the mechanism is effective in reaching quality viewpoint solution even with only a few reference snapshots.