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SCALESPACE
2001
Springer

A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses

10 years 5 months ago
A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses
This paper develops and investigates a new approach for evaluating feature based object hypotheses in a direct way. The idea is to compute a feature likelihood map (FLM), which is a function normalized to the interval [0, 1], and which approximates the likelihood of image features at all points in scale-space. In our case, the FLM is defined from Gaussian derivative operators and in such a way that it assumes its strongest responses near the centers of symmetric blob-like or elongated ridge-like structures and at scales that reflect the size of these structures in the image domain. While the FLM inherits several advantages of feature based image representations, it also (i) avoids the need for explicit search when matching features in object models to image data, and (ii) eliminates the need for thresholds present in most traditional feature based approaches. In an application presented in this paper, the FLM is applied to simultaneous tracking and recognition of hand models based on...
Ivan Laptev, Tony Lindeberg
Added 30 Jul 2010
Updated 30 Jul 2010
Type Conference
Year 2001
Where SCALESPACE
Authors Ivan Laptev, Tony Lindeberg
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