0000000000170814
AUTHOR
Ahmed Ben Said
Gravitational weighted fuzzy c-means with application on multispectral image segmentation
This paper presents a novel clustering approach based on the classic Fuzzy c-means algorithm. The approach is inspired from the concept of interaction between objects in physics. Each data point is regarded as a particle. A specific weight is associated with each data particle depending on its interaction with other particles. This interaction is induced by attraction forces between pairs of particles and the escape velocity from other particles. Classification experiments using two data sets from UCI repository demonstrate the outperformance of the proposed approach over other clustering algorithms. In addition, results demonstrate the effectiveness of the proposed scheme for segmentation …
Multispectral imaging and its use for face recognition : sensory data enhancement
In this thesis, we focus on multispectral image for face recognition. With such application,the quality of the image is an important factor that affects the accuracy of therecognition. However, the sensory data are in general corrupted by noise. Thus, wepropose several denoising algorithms that are able to ensure a good tradeoff betweennoise removal and details preservation. Furthermore, characterizing regions and detailsof the face can improve recognition. We focus also in this thesis on multispectral imagesegmentation particularly clustering techniques and cluster analysis. The effectiveness ofthe proposed algorithms is illustrated by comparing them with state-of-the-art methodsusing both…
Vector anisotropic filter for multispectral image denoising
In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.
Modified total variation regularization using fuzzy complement for image denoising
In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but …
Multispectral image denoising with optimized vector non-local mean filter
Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A …