Enhanced Reality Visualization in a Surgical Environment

Enhanced reality visualization is the process of enhancing an image by adding to it information which is not present in the original image. A wide variety of information can be added to an image ranging from hidden lines or surfaces to textual or iconic data about a particular part of the image. Enhanced reality visualization is particularly well suited to neurosurgery. By rendering brain structures which are not visible, at the correct location in an image of a patient's head, the surgeon is essentially provided with X-ray vision. He can visualize the spatial relationship between brain structures before he performs a craniotomy and during the surgery he can see what's under the next layer before he cuts through. Given a video image of the patient and a three dimensional model of the patient's brain the problem enhanced reality visualization faces is to render the model from the correct viewpoint and overlay it on the original image. The relationship between the coordinate frames of the patient, the patient's internal anatomy scans and the image plane of the camera observing the patient must be established. This problem is closely related to the camera calibration problem. This report presents a new approach to finding this relationship and develops a system for performing enhanced reality visualization in a surgical environment. Immediately prior to surgery a few circular fiducials are placed near the surgical site. An initial registration of video and internal data is performed using a laser scanner. Following this, our method is fully automatic, runs in nearly real-time, is accurate to within a pixel, allows both patient and camera motion, automatically corrects for changes to the internal camera parameters (focal length, focus, aperture, etc.) and requires only a single image.

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.

Study on Granular Dynamics in Vertically Vibrated Beds using Tracking Technique

The study of granular material is of great interest to many researchers in both engineering and science communities. The importance of such a study derives from its complex rheological character and also its significant role in a wide range of industrial applications, such as coal, food, plastics, pharmaceutical, powder metallurgy and mineral processing. A number of recent reports have been focused on the physics of non-cohesive granular material submitted to vertical vibration in either experimental or theoretical approaches. Such a kind of system can be used to separate, mix and dry granular materials in industries. It exhibits different instability behaviour on its surface when under vertical vibration, for example, avalanching, surface fluidization and surface wave, and these phenomena have attracted particular interest of many researchers. However, its fundamental understanding of the instability mechanism is not yet well-understood. This paper is therefore to study the dynamics of granular motion in such a kind of system using Positron Emission Particle Tracking (PEPT), which allows the motion of a single tracer particle to be followed in a non-invasive way. Features of the solids motion such as cycle frequency and dispersion index were investigated via means of authors’ specially-written programmes. Regardless of the surface behaviour, particles are found to travel in rotational movement in horizontal plane. Particle cycle frequency is found to increase strongly with increasing vibration amplitude. Particle dispersion also increased strongly with vibration amplitude. Horizontal dispersion is observed to always exceed vertical dispersion.

Peeling, healing and bursting in a lubricated elastic sheet

We consider the dynamics of an elastic sheet lubricated by the flow of a thin layer of fluid that separates it from a rigid wall. By considering long wavelength deformations of the sheet, we derive an evolution equation for its motion, accounting for the effects of elastic bending, viscous lubrication and body forces. We then analyze various steady and unsteady problems for the sheet such as peeling, healing, levitating and bursting using a combination of numerical simulation and dimensional analysis. On the macro-scale, we corroborate our theory with a simple experiment, and on the micro-scale, we analyze an oscillatory valve that can transform a continuous stream of fluid into a series of discrete pulses.