Ali Mohammad Nickfarjam

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Thresholding is an important pre-processing in many computer vision applications. Finding optimal value in image thresholding is a challenge for many researchers. In this paper, a novel method for image thresholding using Otsu and based on Particle Swarm Optimization (PSO) is proposed. The main idea of the proposed method is combination between Otsu ability(More)
We propose a novel approach for image steganog-raphy by taking the advantages of Particle Swarm Optimization (PSO) and Least Significant Bits (LSBs) replacement. This technique is based on hiding the Most Significant Bits (MSBs) of secret image pixels in LSBs of a host image. The proposed method finds the best pixel in order to embed. We define four feature(More)
Multi-level thresholding is a basic pre-processing technique in computer vision and pattern recognition tasks. Optimal threshold values classify pixel values into multiple categories. The proposed method performs multi-level grayscale image thresholding based on local variance of pixels and Particle Swarm Optimization (PSO). Local variance contains(More)
In this paper, a new method for image hiding is presented which takes advantages of Particle Swarm Optimization (PSO) and neighborhood similarity features in order to embed pixels of secret image in best positions of host image. Most Significant Bits (MSBs) of secret image pixels are utilized to hide in Least Significant Bits (LSBs) of host image pixels.(More)
This paper presents a multi-resolution method for gray-level image enhancement using Particle Swarm Optimization (PSO). The enhancement optimization procedure is a non-linear problem with various constraints. The proposed image enhancement algorithm (MGE-PSO) generates a whole pyramid of differently sized image in order to utilize more information for(More)
We propose a novel approach for image segmentation by taking the advantages of a 5-layer Deep Belief Network (DBN). DBN composed of multiple layers of latent variables (“hidden units”) which used to extract abstract and robust features for image segmentation. However, it processes images with intricate background, hardly. In order to overcome(More)
This paper develops an efficient approach of object detection called Histogram of Oriented Gradients (HOG) by taking the power of Self-adaptive Particle Swarm Optimization (SPSO). The HOG indicates locally normalized histogram of gradient orientations features in a dense overlapping grid gives very good results for object detection. The effects of the(More)
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