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

Source: [Edge Detection using Stationary Wavelet Transform, HMM, and EM algorithm](<https://www.sota2.com/research/paper/68fa67721e209131cea16534>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:02:46.151Z

Authors: S.Anand, K.Nagajothi, K.Nithya

Published: 2020-04-23T16:27:26.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.11296>)

[PDF](<https://arxiv.org/pdf/2004.11296.pdf>)