Abstract In this paper, we present a novel approach that is able to cope with fragmented lines by post processing the 3D lines in euclidean 3D space based on the mean shift algorithm. Starting with mapped line segments from a stereo camera setup, the lines are grouped by their direction and their proximity, so small parts of a big line structure merge into only one. This step provides not only ...

Automatic skin lesion detection is a key step in computeraided diagnosis (CAD) of Skin cancers, since the accuracy of the subsequent steps in CAD crucially depends on it. In this paper, a novel method of automatic skin lesion segmentation based on texture analysis and supervised learning is proposed. It firstly involve the clustering of training image into homogeneous regions using Mean-shift; ...

Segmentation, Unsupervised clustering, Mean shift, Cardiac images, Human heart, Left ventricle. In this paper an image segmentation scheme that is based on combinations of a nonparametric technique and a seed based clustering algorithm is reported. The method has been applied to clinical unsubtracted angiograms of the human heart. The first step of the method consists in applying a mean shiftba...

Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistenby of the estimates. The results are generalized to obtain a simple mean-shift estimate that can be extended in a k-nearestneighbor approach. Applications of gradient estimation to pa...

This paper describes an approach to tracking the global locations of multiple dancers using marker-based motion capture system from only unlabeled 3D marker data for the application of interactive dance. This algorithm is based on the fact that in the presence of multiple people, marker labeling is poor or even infeasible, while reconstruction of the marker 3D coordinates is still reasonable. T...

Image Segmentation is dividing the image into the segments. There are various algorithms available for segmentation but clustering approach is considered to be best. The study shows that there are various gaps which are not covered by the researchers so an improved approach towards image segmentation using mean shift and FELICM has been proposed. HSV color space is considered to be better appro...

In many applications, denoising is necessary since point-sampled models obtained by laser scanners with insufficient precision. An algorithm for point-sampled surface is presented, which combines fuzzy c-means clustering with mean shift filtering algorithm. By using fuzzy c-means clustering, the largescale noise is deleted and a part of small-scale noise also is smooth. The cluster centers are ...

In this paper we contend that color based image segmentation can be performed in two stages: an edge preserving filtering stage followed by pixel grouping. Furthermore, for the first step, we introduce a framework under which many current filtering approaches (and some novel ones) can be classified. We present experiments where a new method, called Color Mean Shift, outperforms all the other me...

We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated “weight i...

A variable search space (VSS) approach according to the color feature combined with point feature for object tracking is presented. Mean shift is a wellestablished and fundamental algorithm that works on the basis of color probability distributions, and is robust to given color targets. As it solely depends upon back projected probabilities, it may miss the targets because of illumination and n...