3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A methodology for quantitative performance evaluation of detection algorithms
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
A major concern in practical vision systems is how to retrieve the best matched models without exploring all possible object matches. This research presents probabilistic hypothesis generation based on indexing approach for the rapid recognition of three dimensional objects. We have defined the discriminatory power of a feature for a model object is defined in terms of a posteriori probability. This measure displays belief that a model appears in the scene after a feature is observed. We compute off-line the discriminatory power of features for model objects from CAD model data using computer graphic techniques. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for features detected in a scene in the order of the discriminatory power of these features for model objects. Experimental results on synthetic and real range images show the effectiveness of our probabilistic method for hypothesis generation.