We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Mean...

Clustering is an important data exploration task. Its use in data mining is growing very fast. Traditional clustering algorithms which no longer cater to the data mining requirements are mod#ed increasingly. Clustering algorithms are numerous which can be divided in several categories. Two prominent categories are distance-based and density-based (e.g. K-means and DBSCAN, respectively). While K...

We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Mean...

Image fusion and denoising have been widely researched as separate techniques for the past few decades. Most of the fusion techniques fuse the images with the assumption that images are nonnoisy. But in many practical applications, especially, in the case of satellite images this assumption fails. In this paper, a novel technique based on nonlocal means filter in conjunction with multiresolutio...

This paper considers a multiple-circle detection problem on the basis of given data. The problem is solved by application of the center-based clustering method. For the purpose of searching for a locally optimal partition modeled on the well-known k-means algorithm, the k-closest circles algorithm has been constructed. The method has been illustrated by several numerical examples.

We show that the popular Non-Local Means method for image denoising can be implemented exactly, easily and with lower computation time using convolutions. Also, our algorithm allows the use of infinite-size non-binary patches, which improves the denoising quality.

Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed ...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract K-means Clustering Algorithm, among the various clustering algorithms proposed till date, has proved its superiority by its simplicity and usability. However, it is prone to a number of limitations, one being lack of balance in clusters. Clusters o...

1. Local Optimum Problem in K-Means Now that you have learned the k-means clustering algorithm, let’s take a look at how the initial choice of cluster locations can affect the result of the algorithm. The k-means algorithm is an optimization problem that iteratively makes steps towards the “best” placement for the clusters. However, the algorithm can get stuck in a placement for the clusters th...

The classical non-local means image denoising approach, the value of a pixel is determined based on the weighted average of other pixels, where the weights are determined based on a fixed isotropic ally weighted similarity function between the local neighbourhoods. It is demonstrate that noticeably improved perceptual quality can be achieved through the use of adaptive anisotropic ally weighted...