Optimal edge-based shape detection

9 years 6 months ago
Optimal edge-based shape detection
Abstract--We propose an approach to accurately detecting twodimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao [5]. We define an operator for shape detection by extending the DODE filter along the shape's boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propag...
Hankyu Moon, Rama Chellappa, Azriel Rosenfeld
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where TIP
Authors Hankyu Moon, Rama Chellappa, Azriel Rosenfeld
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