Jet Based Feature Classification

In this paper, we investigate to which extent the "raw" mapping of Taylor series coefficients into jet-space can be used as a "language" for describing local image structure in terms of geometrical image features. Based on empirical data from the van Hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a Bayesian framework. We introduce the Brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. We give examples of simultaneous feature detection across scale.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Kim Steenstrup Pedersen, Martin Lillholm
• Keywords: Brownian Image Model, Computer Vision, Feature Posterior Maps, ICPR 2004, Simultaneous Feature Detection