Name
Abstract | ||
|---|---|---|
. We describe how to model the appearance of an object usingmultiple views, learn such a model from training images, and recognizeobjects with it. The model uses probability distributions to characterizethe significance, position, and intrinsic measurements of various discretefeatures of appearance; it also describes topological relations amongfeatures. The features and their distributions are learned from trainingimages depicting the modeled object. A matching procedure, combining... |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/3-540-61750-7_30 | Object Representation in Computer Vision |
Keywords | Field | DocType |
learning appearance models,object recognition,probability distribution | Computer vision,Graph,3D single-object recognition,Pattern recognition,Computer science,Object model,Probability distribution,Artificial intelligence,Instrumental and intrinsic value,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
3-540-61750-7 | 13 | 2.17 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arthur R. Pope | 1 | 121 | 31.89 |
| D. G. Lowe | 2 | 15718 | 1413.60 |