2023年10月6日 · The proposed J-HMMSteg is a simple model-based, minimal-distortion JPEG image steganography technique that is secure and adaptive. It builds HMM image models …

The HMM parameters are estimated b y the EM algorithm. T o classify an image, the classes with maxim um a p osteriori probabilit y are searc hed join tly for all the blo c ks. Applications of the …

2008年3月20日 · HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and …

We propose a novel method for recognizing temporally or spatially varying patterns using MFCC (mel-frequency ceptral coefficient) and HMM (hidden Markov model). MFCC and HMM have …

Using the versatility of HMMs, let’s try applying them to another problem; Optical Character Recognition. Our goal is to recognize text in an image where the font and font size is known …

In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not …

HMM’s can not only be used to predict and classify the random input digit, but also be used to reconstruct the image when the certain parts of the input image are missing.

This study introduces J-HMMSteg, an adaptive and secure JPEG image steganography technique designed for data embedding with minimal distortion. J-HMMSteg employed a block-wise …

An HMM-HMT model is developed for each texture of interest, with which image segmentation is achieved. Several numerical examples are presented to demonstrate the model, with …

We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs …