The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Probabilistic modeling of local appearance and spatial relationships for object recognition
Abstract
In this paper, we describe an algorithm for object recognition that explicitly models and estimated the posterior probability function, P(object/image). We have chosen a functional form of the posterior probability function that captures the joint statistics of local appearance and position on the object as well as the statistics of local appearance in the visual world at large. We use a discrete representation of local appearance consisting of approximately 10/sup 6/ patterns. We compute an estimate of P(object/image) in closed form by counting the frequency of occurrence of these patterns over various sets of training images. We have used this method for detecting human faces from frontal and profile views. The algorithm for frontal views has shown a detection rate of 93.0% with 88 false alarms on a set of 125 images containing 483 faces combining the MIT test set of Sung and Poggio with the CMU test sets of Rowley, Baluja, and Kanade. The algorithm for detection of profile views has also demonstrated promising results.
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Cites
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Cited by
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Face Expression Recognition and Analysis: The State of the Art
Bettadapura, V. (2012) · arXiv · doi:10.48550/arxiv.1203.6722 · Cited by 336
Automatic coding of facial expressions displayed during posed and genuine pain
Littlewort, G. C., Bartlett, M. S., & Lee, K. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.12.010 · Cited by 216
Face detection and tracking in video sequences using the modifiedcensus transformation
Küblbeck, C., & Ernst, A. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2005.08.005 · Cited by 178
Online domain adaptation of a pre-trained cascade of classifiers
Jain, V., & Learned-Miller, E. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995317 · Cited by 161
Metadata
- Authors
- Schneiderman, H., & Kanade, T.
- Venue
- Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Pages
- 45-51
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2166713160
- Cited by
- 358
- References
- 1
- FWCI
- 24.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Schneiderman, H., & Kanade, T. (2002). Probabilistic modeling of local appearance and spatial relationships for object recognition. Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231). https://doi.org/10.1109/cvpr.1998.698586
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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