3D objects recognition by optimal matching search of multinary relations graphs
Computer Vision, Graphics, and Image Processing
Probabilistic models of observed features and aspects with application to weighted aspect graphs
Pattern Recognition Letters
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On View Likelihood and Stability
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Viewpoint Planning Strategy for Determining True Angles on Polyhedral Objects by Camera Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
PERFORM: A Fast Object Recognition Method Using Intersection of Projection Error Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computational Model of View Degeneracy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing without Invariants in 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic 3D Object Recognition
International Journal of Computer Vision
View Variation of Point-Set and Line-Segment Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Indexing for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Angle Densities and Recognition of 3D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Visibility-Based Test Scene Understanding by Real Plane Search
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Visibility-based modelling and control for network-based robotics
Pattern Recognition Letters
Object recognition using point uncertainty regions as pose uncertainty regions
Image and Vision Computing
Spatial Modelling for Mobile Robot's Vision-based Navigation
Journal of Intelligent and Robotic Systems
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Two novel probabilistic models for viewed angles and distances are derived using an observability sphere method. The method, which is based on the assumption that the prior probability density is isotropic for all viewing orientations, can be used for the computation of observation probabilities for object's aspects, features, and probability densities of their quantitative attributes. Using the sphere, it is discovered that the probability densities of viewed angles, distances, and even projected curvature have sharp peaks at their original values. From this peaking effect, it is concluded that in most cases, the values of angles and distances are being altered only slightly by the imaging process, and they can still serve as a strong cue for model-based recognition. The probabilistic models for 3-D object recognition from monocular images are used. To form the angular elements that are needed, the objects are represented by their linear features and specific points primitives. Using the joint density model of angles and distances, the probabilities of initial matching hypotheses and mutual information coefficients are estimated. These results are then used for object recognition by optimal matching search and stochastic labeling schemes. Various synthetic and real objects are recognized by this approach.