We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdf). We show that the UK-means algorithm, which generalises t...
Ben Kao, Sau Dan Lee, David W. Cheung, Wai-Shing H...
This paper presents a method for the segmentation of skin lesions in dermoscopy images. The proposed technique uses region based level sets and adopts a mixture of Gaussian densit...
We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images proc...
Abstract. A new 3D segmentation method based on the level set technique is proposed. The main contribution is a robust evolutionary model which requires no fine tuning of parameter...
This paper presents a new diffusion scheme on statistical manifolds for the detection of texture boundaries. The technique derives from our previous work, in which 2-dimensional R...
Sang-Mook Lee, A. Lynn Abbott, Neil A. Clark, Phil...
Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method...
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
A technique is presented for representing linear features as probability density functions in two or three dimensions. Three chief advantages of this approach are (1) a unified re...