We propose the segmentation of noisy datasets into Multiple Inlier Structures with a new Robust Estimator (MISRE). The scale of each individual structure is estimated adaptively from the input data and refined by mean shift, without tuning any parameter in the process, or manually specifying thresholds for different estimation problems. Once all the data points were classified into separate str...

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart (individuals chart) is often used as an alternative charting scheme in practice. In thi...

The traditional color-based mean-shift tracking algorithm is popular among tracking methods due to its simple and efficient procedure, however, the lack of dynamism in its target model makes it unsuitable for tracking objects which have changes in their sizes and shapes. In this paper, we propose a fast novel threephase colored object tracker algorithm based on mean shift idea while utilizing a...

Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, ...

We discuss a novel statistical framework for image segmentation based on nonparametric clustering. By employing the mean shift procedure for analysis, image regions are identified as clusters in the joint color-spatial domain. To measure the significance of each cluster we use a test statistics that compares the estimated density of the cluster mode with the estimated density on the cluster bou...

We consider applications of clustering techniques, Mean Shift and Self-Organizing Maps, to surface reconstruction (meshing) from scattered point data and review a novel kernel-based clustering method.

Painting style transformation aims to produce a painting in a particular artist’s style from a picture or other paintings. In the previous implementations of example-based painting style transfer, users had to manually select the patches from example patches. It is time-consuming and subjective for a user to select proper patches from a large number of patches. This paper develops a patch-based...

An automatic object tracking and video summarization method for multi-camera systems with a large number of non-overlapping field-of-view cameras is explained. In this framework, video sequences are stored for each object as opposed to storing a sequence for each camera. Objectbased representation enables annotation of video segments, and extraction of content semantics for further analysis. We...

BACKGROUND Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier dete...

 In this paper an applied problem, where the response of interest is the number of success in a specific experiment, is considered and by various visions is studied. The effects of outlier values of response on results of a regression analysis are so important to be studied. For this reason, using diagnostic methods, outlier response values are recognized. It is shown that use of arc-sine ...