Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact and Approximate Solutions of the Perspective-Three-Point Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Object Pose in 25 Lines of Code
ECCV '92 Proceedings of the Second European Conference on Computer Vision
An Efficient Iterative Pose Estimation Algorithm
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
An Efficient Correspondence Based Algorithm for 2D and 3D Model Based Recognition
An Efficient Correspondence Based Algorithm for 2D and 3D Model Based Recognition
Conversational informatics where web intelligence meets brain informatics
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Artifact-mediated society and social intelligence design
Artificial intelligence
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In this paper, we describe a method for finding the pose of an object from a single image. We assume that we can detect and match in the image three feature points of the object, and that we know their relative geometry on the object. At first we present the exact pose calculation with an existing method and emphasize the limitation. Then we introduce a new method which consists of adding to the camera an inclinometer so that we reduce the number of unknown parameters and thus are able to compute the pose efficiently by using a classical iterative optimization method.