.png){kind=icon}
.png){kind=icon}
Return
Video-based descriptors for object recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2011.08.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We describe a visual recognition system operating on a hand-held device, based on a video-based feature descriptor, and characterize its invariance and discriminative properties. Feature selection and tracking are performed in real-time, and used to train a template-based classifier during a capture phase prompted by the user. During normal operation, the system recognizes objects in the field of view based on their ranking. Severe resource constraints have prompted a re-evaluation of existing algorithms improving their performance (accuracy and robustness) as well as computational efficiency. We motivate the design choices in the implementation with a characterization of the stability properties of local invariant detectors, and of the conditions under which a template-based descriptor is optimal. The analysis also highlights the role of time as weak supervisor during training, which we exploit in our implementation. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Feature tracking
Video-based descriptors
Object recognition
Multi-view recognition
Mobile devices
Visual recognition
Active vision
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
4.0K
Citations:
6.7K
