A new wavelet-based automatic multi-level thresholding technique is proposed. The new technique is a generalized version of the method proposed by Olivo . Olivo  proposed using a set of dilated wavelets to convolve with the histogram of an image. For each scale, a set of thresholds was determined automatically based on the rules he proposed. However, Olivo did not provide a systematic way to decide on an exact set of thresholds which corresponds to a specific scale that can lead to the best segmentation result. In this paper, we propose using a cost function as a guide to solve the above problem. Experimental results show that our approach can always automatically select the best scale for performance of multi-level thresholding.
|Number of pages||12|
|Journal||Image and Vision Computing|
|State||Published - 1 Jan 1997|
- Image segmentation
- Multi-level thresholding