Elements of information theory
Elements of information theory
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value Gradients
International Journal of Computer Vision
Model-Based Localisation and Recognition of Road Vehicles
International Journal of Computer Vision
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Mutual Information Based Evaluation of 3D Building Models
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Alignment by maximization of mutual information
Alignment by maximization of mutual information
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We present an alignment framework for object detection using a hierarchy of 3D polygonal models. One difficulty with alignment methods is that the high-dimensional transformation space makes finding potential candidate states a time-consuming task. This is an important consideration in our approach, as an exhaustive search is applied on a densely-sampled state space in order to avoid local minima and to extract all possible candidates. In our framework, a level-of-detail (LOD) 3D geometric model hierarchy is generated for the target object. Each of this model acts as a classifier to determine which of the discrete states are potential candidates. The classification is done through the estimation of pixel and edge-based mutual information between the 3D model and the image, where the classification speed significantly depends on the LOD and resolution of the image. By combining these models of various LOD into a cascade, we show that search time can be reduced significantly while accuracy is maintained.