Neurophysiology of spontaneous facial expressions: II. Motor control of the right and left face is partially independent in adults.

Facial expressions are described traditionally as monolithic or unitary entities. However, humans have the capacity to produce facial blends of emotion in which the upper and lower face simultaneously display different expressions. Recent neuroanatomical studies in monkeys have demonstrated that there are separate cortical motor areas for controlling the upper and lower face in each hemisphere that, presumably, also occur in humans.

Imaging of subclinical haemopoiesis after stem-cell transplantation in patients with haematological malignancies: a prospective pilot study.

Background: Haemopoietic stem-cell transplantation (HSCT) eradicates host haemopoiesis before venous infusion of haemopoietic stem cells (HSCs). The pathway to cellular recovery has been difficult to study in human beings because of risks associated with interventions during aplasia. We investigated whether F-fluorothymidine (F-FLT) imaging was safe during allogenic HSCT and allowed visualisation of early cellular proliferation and detection of patterns of cellular engraftment after HSCT.

AN AUTOMATIC 3D CT/PET SEGMENTATION FRAMEWORK FOR BONE MARROW PROLIFERATION ASSESSMENT.

Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous sites.

Neurophysiology of spontaneous facial expressions: I. Motor control of the upper and lower face is behaviorally independent in adults.

Facial expressions are described traditionally as monolithic entities. However, humans have the capacity to produce facial blends, in which the upper and lower face simultaneously display different emotional expressions. This, in turn, has led to the Component Theory of facial expressions.

Joint infrared target recognition and segmentation using a shape manifold-aware level set.

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM.

Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set.

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds.

Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online.

Properties of the Magnitude Terms of Orthogonal Scaling Functions.

The spectrum of the convolution of two continuous functions can be determined as the continuous Fourier transform of the cross-correlation function. The same can be said about the spectrum of the convolution of two infinite discrete sequences, which can be determined as the discrete time Fourier transform of the cross-correlation function of the two sequences. In current digital signal processing, the spectrum of the contiuous Fourier transform and the discrete time Fourier transform are approximately determined by numerical integration or by densely taking the discrete Fourier transform.