Edge Detection using Stationary Wavelet Transform, HMM, and EM algorithm

23 Apr 2020  ·  S. Anand, K. Nagajothi, K. Nithya ·

Stationary Wavelet Transform (SWT) is an efficient tool for edge analysis. This paper a new edge detection technique using SWT based Hidden Markov Model (WHMM) along with the expectation-maximization (EM) algorithm is proposed. The SWT coefficients contain a hidden state and they indicate the SWT coefficient fits into an edge model or not. Laplacian and Gaussian model is used to check the information of the state is an edge or no edge. This model is trained by an EM algorithm and the Viterbi algorithm is employed to recover the state. This algorithm can be applied to noisy images efficiently.

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here