Name
Abstract | ||
|---|---|---|
To recognize an object in an image an internal model is required to indicate how that object may appear. The authors show how to learn such a model from a series of training images depicting a class of objects, producing a model that represents a probability distribution over the variation in object appearance. Features identified in an image through perceptual organization are represented by a graph whose nodes include feature labels and numeric measurements. A learning procedure generalizes multiple image graphs to form a model graph in which the numeric measurements are characterized by probability distributions. A matching procedure, using a similarity metric based on a non-parametric probability density estimator, compares model and image graphs to identify an instance of a modeled object in an image. Experimental results are presented from a system constructed to test this approach. The system learns to recognize partially occluded 2-D objects in 2-D images using shape cues. It can recognize objects as similar in general appearance while distinguishing them by their detailed features.<
<ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX> |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/ICCV.1993.378202 | ICCV |
Keywords | Field | DocType |
computer vision,feature extraction,image recognition,learning (artificial intelligence),object recognition,2-D images,feature labels,matching procedure,model graph,multiple image graphs,numeric measurements,object appearance,object recognition models learning,partially occluded 2-D objects,perceptual organization,probability density estimator,probability distribution,probability distributions,shape cues,similarity metric,training images | Computer vision,3D single-object recognition,Pattern recognition,Computer science,Object model,Learning object,Feature extraction,Active appearance model,Probability distribution,Feature (machine learning),Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | Citations |
1993 | 1 | 24 |
PageRank | References | Authors |
3.17 | 25 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arthur R. Pope | 1 | 121 | 31.89 |
| D. G. Lowe | 2 | 15718 | 1413.60 |