An Experimental Comparison of Appearance and Geometric Model Based Recognition
1996
This paper describes an experimental investigation of the recognition performance of two approaches to the representation of objects for recognition. The first representation, generally known as appearance modelling, describes an object by a set of images. The image set is acquired for a range of views and illumination conditions which are expected to be encountered in subsequent recognition. This image database provides a description of the object. Recognition is carried out by constructing an eigenvector space to compute efficiently the distance between a new image and any image in the database. The second representation is a geometric description based on the projected boundary of an object. General object classes such as planar objects, surfaces of revolution and repeated structures support the construction of invariant descriptions and invariant index functions for recognition.
Keywords:
- Eigenvalues and eigenvectors
- 3D single-object recognition
- Computer vision
- Geometric data analysis
- Computer science
- Invariant (mathematics)
- Standard test image
- Active appearance model
- Cognitive neuroscience of visual object recognition
- Geometric modeling
- Pattern recognition
- Artificial intelligence
- Surface of revolution
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