Name
Abstract | ||
|---|---|---|
Recognition of object categories from their images is extremely challenging due to the large intra-class variations, and variations in pose, illumination and scale, in addition to lack of depth information of the object. Recovering the depth information from multiple images or from image cues such as variations in illumination or focus, is both computationally intensive and error prone. In contrast, the appearance based approaches are more robust and computationally efficient. However, they lack the potential accuracy of 3D feature based approaches due to the lack of shape information. We propose the use of structured lighting patterns projected on the object, which gets deformed according to the shape of the object for recognition. Since our goal is object classification and not shape recovery, we characterize the deformations using simple texture measures, thus avoiding depth recovery step. Moreover, the shape information present in the deformations is implicitly used for classification. We show that the information thus derived can significantly improve the accuracy of object category recognition from arbitrary-pose images. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICVGIP.2008.62 | Bhubaneswar |
Keywords | Field | DocType |
object category recognition,shape information present,depth information,shape information,image cue,object classification,object category,potential accuracy,projected texture,arbitrary-pose image,depth recovery step,image texture,image classification,image recognition,shape,robustness,structured light,lighting,classification algorithms,object recognition | Computer vision,3D single-object recognition,Pattern recognition,Image texture,Computer science,Appearance based,Robustness (computer science),Artificial intelligence,Contextual image classification,Statistical classification,Object category recognition,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
978-0-7695-3476-3 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avinash Sharma | 1 | 22 | 8.25 |
| Anoop M. Namboodiri | 2 | 255 | 26.36 |