PubMed · 8538738
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D Kersten, D C Knill, P Mamassian, I Bülthoff. 1996-01-04. Illusory motion from shadows.. https://doi.org/10.1038/379031a0
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XNBC is a software package for simulating biological neural networks. Two neuron models are available, a leaky integrator model and an ion-conductance model. Inputs to the simulated neurons can be provided by experimental data stored in files, allowing the creation of 'hybrid' networks. Graphic tools are used to describe the modeled neurons as well as the network. Neuron and network parameters can be modified during the simulation, to mimic electrical stimulations and drugs action. The temporal evolution of the network and of selected neurons can be visualized. A point process, frequency or dynamic analysis of the simulator output can be performed. The successive stages of the creation of a hybrid network are explained.
BACKGROUND: The present study investigated how human subjects capture and restore visual information within a line representation drawing the three-dimensional configuration of a space module. METHODS: Nine subjects were asked to perform a visual localization task within this geometric model. The task consisted of localizing a light point appearing during 3 s in 40 different spatial positions and for 8 tilt angles of the model in the following random order: 0 degree, 45 degrees, 180 degrees, 225 degrees, 90 degrees, 315 degrees, 135 degrees, and 270 degrees. RESULTS: Results showed symmetric variations of the point-localizing errors about 180 degrees of tilt angle (vertical orientation) and a decrease of the errors when the points occurred in the distal, then in the median and in the proximal planes (virtual depth). The performance also varied according to the virtual ceiling, floor, right, left and back planes. The results have been discussed using a three-dimensional space representation based on a conservative mode of spatial information up to 90 degrees of tilt (horizontal orientation), and on a transformation mode beyond 90 degrees of tilt. CONCLUSION: We conclude that the visual localization in single geometric planes is orientation-dependent.
The most important rendering methods applied in medical imaging are surface and volume rendering techniques. Each approach has its own advantages and limitations: Fast surface-oriented methods are able to support real-time interaction and manipulation. The underlying representation, however, is dependent on intensive image processing to extract the object surfaces. In contrast, volume visualization is not necessarily based on extensive image processing and interpretation. No data reduction to geometric primitives, such as polygons, is required. Therefore, the process of volume rendering is currently not operating in real time. In order to provide the radiological diagnosis with additional information as well as to enable simulation and preoperative treatment planning we developed a new hybrid rendering method which combines the advantages of surface and volume presentation, and minimizes the limitations of these approaches. We developed a common data representation method for both techniques. A preprocessing module enables the construction of a data volume by interpolation as well as the calculation of object surfaces by semiautomatic image interpretation and surface construction. The hybrid rendering system is based on transparency and texture mapping features. It is embedded in a user-friendly open system which enables the support of new application fields such as virtual reality and stereolithography. The efficiency of our new method is described for 3-D subtraction angiography and the visualization of morpho-functional relationships.