In this paper, we present a robust and accurate method for the segmentation of cross-sectional boundaries of vessels found in contrast-enhanced (CE) CTA and MRA images. The proposed algorithm first detects the edges along 1D rays in multiple scales by using mean-shift analysis. Second, edges from different scales are accurately and efficiently combined by using the properties of mean-shift clus...

This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavel...

In this paper, a target tracking algorithm is proposed by combining the improved Mean Shift algorithm with the adaptive Kalman filter. For a selected moving object, frame difference and region growing methods are used to segment target and extract the dominant color. In the tracking process, the initial iterative position is obtained by adaptive Kalman filter in each frame. The tracking result ...

This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visua...

Visual object tracking is the active area of research in the computer vision. Various techniques have being developed for the object tracking .Particle filter deals with the tracking of the objects having non linear motion .Particle filter has been used in for various fields and application efficiently. Proposed Technique is based on combination of kalman filter, mean shift and particle filter....

This paper develops an approach to minimize the number of process tooling adjustments and deliver an acceptable fraction of non-conforming products based on given product quality specification limits in assembly processes. A linear model is developed to describe the relationships between product quality and process tooling locating positions. Based on the model, the process mean shifts of tooli...

In an earlier work, joint with R. Kenyon, we computed the Hausdorff dimension of the “multiplicative golden mean shift” defined as the set of all reals in [0, 1] whose binary expansion (xk) satisfies xkx2k = 0 for all k ≥ 1. Here we show that this set has infinite Hausdorff measure in its dimension. A more precise result in terms of gauges in which the Hausdorff measure is infinite is also obta...

In this paper, we address the probleni of tmcking multiple people in a cluttered environment had on thorough usage of color infomintion. We introduce color invariants to the proposed conihineci color spxtial model, which combines the low-level arid nlid<ilelevel color spatial infomiation togetlier. This formalization gives ils a way to coiiibine low-level ~ n d middle-level color spatial distri...

Ring theory is one of the branches of the abstract algebra that has been broadly used in images. However, ring theory has not been very related with image segmentation. In this paper, we propose a new index of similarity among images using Zn rings and the entropy function. This new index was applied as a new stopping criterion to the Mean Shift Iterative Algorithm with the goal to reach a bett...

We present a scalable method for brain cell identification in multiview confocal light sheet microscopy images. Our algorithmic pipeline includes a hierarchical registration approach and a novel multiview version of semantic deconvolution that simultaneously enhance visibility of fluorescent cell bodies, equalize their contrast, and fuses adjacent views into a single 3D images on which cell ide...