Optimizing templates for finding trees in aerial photographs
Pattern Recognition Letters
Modern Differential Geometry of Curves and Surfaces with Mathematica
Modern Differential Geometry of Curves and Surfaces with Mathematica
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
A Trainable Hierarchical Hidden Markov Tree Model for Color Image Annotation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model
Computer Vision and Image Understanding
Multiscale Bayesian segmentation using a trainable context model
IEEE Transactions on Image Processing
3D Scene interpretation by combining probability theory and logic: The tower of knowledge
Computer Vision and Image Understanding
Understanding leaves in natural images - A model-based approach for tree species identification
Computer Vision and Image Understanding
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In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.