A Statistical Framework for Detection of Connected Features (original) (raw)

2006

Abstract

ABSTRACT This document provides a general idea of what edge-detection is and how it works e.g. for computer vision etc., edge detectors are often operated with arbitrary parameters such as thresholds. Determining the significant values for these parameters on a trial and error basis may be a problem. It is therefore beneficial to try to understand edge-detection in terms of established quantitative methods. Here we show how the idea of an Hypothesis test can be used for significance testing and that provided there is the same null hypothesis distribution everywhere in an image, applying Hypothesis testing is the same as thresholding. We show how the method of error propagation can be used to find out if we have uniform noise on a feature enhancement. We apply this analysis to the Canny algorithm for detection of step edges. We explain that for other than step edges this algorithm needs modification and how this can be done while staying within the overall framework for the detection of connected features via non-maximal suppression and hysteresis thresholding. The DoG is a linear filter which has the required properties for algorithmic stability, and can be used for the detection of ridge structures. The orientation of the ridge is defined for this process as the direction of maximum second derivative. This ridge detector is then evaluated for the task of fly wing analysis, by looking at the specific characteristics of noise and scale stability.

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