John Chiverton

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This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was(More)
An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with dynamic pho-tometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the(More)
A new fully automatic object tracking and segmentation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker.(More)
We are interested in segmentation and tracking using high-level shape information, particularly for objects that undergo arbitrary and smooth deformations , but without the a priori learning of shape constraints. We introduce a shape based level set active contour framework that learns shape information on-line, simultaneously combining the newly learnt(More)
We present segmentation and tracking of deformable objects using non-linear on-line learning of high-level shape information in the form of a level set function. The emphasis is for successful tracking of objects that undergo smooth arbitrary deformations, but without the a priori learning of shape constraints. The high-level shape information is learnt(More)
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the distribution of currently estimated foreground pixels; (b) the ratio of the background distribution to the model distribution for all estimated background pixels. This approach(More)
—Accurate classification of signals composed of two or more classification classes (e.g., biomedical imaging data with pathological structures) might utilize a density that takes account of the signal acquisition process. A new density based on a Gaussian point spread function (PSF) and another utilizing a phenomenological observation known as Benford's Law(More)
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