This paper establishes the foundational principles and practice for a unified theory of arbitrary information management by disclosing systems, devices and methods for the management of substrates or biological substrates. In this context, a substrate is any aspect of any entity that is capable of responding to or emitting stimuli irrespective of whether the stimuli actually emanate from any aspect of the entity or not. Management of substrates could be achieved through the management of stimuli that modulate or moderate or influence any aspect of the substrate as well as through the management of any stimuli emanating from the substrate. The results enable a wide range of novel applications in a variety of fields with far-reaching implications. For example, the functional organization of many regions of the brain including the superior temporal cortex which is believed to play a critical role in the hierarchical processing of human visual and auditory stimuli is poorly understood. It is not known precisely which layer within which region of the brain is responsible for which aspect of visual or auditory processing. Simultaneous non-invasive acquisition of bio-signals representing contributions from multiple layers of neuronal populations within the brain could provide new insights leading to the resolution of many of these outstanding issues and provide a deeper understanding of the underlying physiological processes.

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This paper presents a novel approach to the correction of panoramic (wide-angle) image distortions. Unlike traditional methods that separate the distortion of the panoramic image into radial and tangential components and then concentrate on the correction of one type of distortion at a time, the method presented in this paper uses an integrated approach that simultaneously corrects all non-linear distortions of the panoramic image. The system uses data obtained from carefully constructed calibration patterns to automatically build and train a constructive neural network of suitable complexity to approximate the characteristic distortion of the panoramic image. The trained neural network is then used to correct the distortions represented by the sample data. It is demonstrated that by applying the distortion correction method presented in this paper to panoramic images representing real world scenes, perspective-corrected views of the real world scene that are usable in a wide variety of applications can be generated.

This paper introduces a framework for the creation, management and deployment of interactive virtual tours. A panoramic image acquisition unit is adapted to acquire panoramic images and video streams from a wide variety of sources. The acquired panoramic images and video streams are fed into a transform engine that performs any required transformations on the input data in a uniform and seamless manner. A package generator coupled with the transform engine synthesizes complete virtual tour packages by combining the transformed data with a broad range of multimedia resources to create navigable virtual tours. Perspective correction and related transformations on the data are carried out by a viewing engine allowing a plurality of viewers to independently and simultaneously interact with the virtual tours. User interaction with the virtual tour packages is enhanced by a control engine that facilitates bi-directional communication between various elements of the system.

This paper presents a system for real-time visualization of very large image data sets using ondemand loading and dynamic view prediction. We use a robust image representation scheme for efficient adaptive rendering and a perspective view generation module to extend the applicability of the system to panoramic images. We demonstrate the effectiveness of the system by applying it both to imagery that does not require perspective correction and to very large panoramic data sets requiring perspective view generation. The system permits smooth, real-time interactive navigation of very large panoramic and non-panoramic image data sets on average personal computers without the use of specialized hardware.