Name
Abstract | ||
|---|---|---|
Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. Using physical models for image formation, we derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces including many textured surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. We have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. We present several examples demonstrating the system's ability to recognize model objects in cluttered scenes independent of object configuration and scene illumination. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/34.481544 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
color image region,dimensional object geometry,object surface,object configuration,dimensional object recognition,illumination-invariant recognition,local invariants,scene illumination,local color invariants,model object,invariants capture information,object recognition system,color vision,indexation,physical model,color,pixel,computer vision,geometry,reflectivity,layout,spectral reflectance,surface texture,color image,color constancy,object recognition,machine vision,image segmentation,three dimensional,image formation,lighting,pose estimation | Color constancy,Computer vision,Pattern recognition,Computer science,Pose,Image formation,Image segmentation,Artificial intelligence,Invariant (mathematics),Pixel,Color image,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
18 | 2 | 0162-8828 |
Citations | PageRank | References |
67 | 23.66 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Slater | 1 | 67 | 23.66 |
| Glenn Healey | 2 | 67 | 23.66 |