New Application for the Edge Detection Algorithm. International Journal

New Application for the Edge Detection Algorithm. International Journal

journal article - other

en

The wavelet transform is a comparatively new and fast developing method for analysing signals. The main advantage of applying the wavelet transform to the detection of edges in an image is the possibility of choosing the size of the details that will be detected. The size of detected edges is set by the wavelet scale. In the case of the discrete wavelet transform the choice of the scale is performed by multiple signal passage through the wavelet filter. When processing a 2-D image, the wavelet analysis is performed separately for the horizontal and the vertical function. The vertical and the horizontal edges are thus detected separately. The wavelet transform will split the input signal into two components. One contains the low-frequency (LP) part of input signal, which corresponds to major changes in the function (individual objects in the image, etc.). The other part contains the high-frequency (HP) part of input signal, which corresponds to details in the function (noise, edges, etc.). This signal component is not processed on the next level of transformation.

The wavelet transform is a comparatively new and fast developing method for analysing signals. The main advantage of applying the wavelet transform to the detection of edges in an image is the possibility of choosing the size of the details that will be detected. The size of detected edges is set by the wavelet scale. In the case of the discrete wavelet transform the choice of the scale is performed by multiple signal passage through the wavelet filter. When processing a 2-D image, the wavelet analysis is performed separately for the horizontal and the vertical function. The vertical and the horizontal edges are thus detected separately. The wavelet transform will split the input signal into two components. One contains the low-frequency (LP) part of input signal, which corresponds to major changes in the function (individual objects in the image, etc.). The other part contains the high-frequency (HP) part of input signal, which corresponds to details in the function (noise, edges, etc.). This signal component is not processed on the next level of transformation.

The wavelet transform is a comparatively new and fast developing method for analysing signals. The main advantage of applying the wavelet transform to the detection of edges in an image is the possibility of choosing the size of the details that will be detected. The size of detected edges is set by the wavelet scale. In the case of the discrete wavelet transform the choice of the scale is performed by multiple signal passage through the wavelet filter. When processing a 2-D image, the wavelet analysis is performed separately for the horizontal and the vertical function. The vertical and the horizontal edges are thus detected separately. The wavelet transform will split the input signal into two components. One contains the low-frequency (LP) part of input signal, which corresponds to major changes in the function (individual objects in the image, etc.). The other part contains the high-frequency (HP) part of input signal, which corresponds to details in the function (noise, edges, etc.). This signal component is not processed on the next level of transformation.

Wavelet transform, Edge detection, Image, Signal, Analysis

2003

26.04.2003

1109-2750

WSEAS Transactions on Computers

2

2

5