Probabilistic visual learning for object detection

We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for a unimodal distributions) and a multivariate Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition. This learning technique is tested in experiments with modeling and subsequent detection of human faces and non-rigid objects such as hands.<<ETX>>

• Publication year

  1995

• Venue

  Proceedings of IEEE International Conference on Computer Vision

• Publication date

  1995-06-20

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/ICCV.1995.466858
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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