Abstract Issues of digital preservation of shapes and internal structures of historical cultural objects are discussed. An overview of existing approaches to digital preservation related to shape modeling is presented and corresponding problems are considered. We propose a new digital preservation paradigm based on both constructive modeling reflecting the logical structure of the objects and open standards and procedures. Constructive Solid Geometry (CSG) and Function Representation (FRep) are examined and practically applied as mathematical representations producing compressed yet precise data structures, thus providing inter‐operability between current and future computer platforms crucial to archiving. Examples of CSG reconstruction of historical temples and FRep modeling of traditional lacquer ware are given. We examine the application of fitting of a parameterized FRep model to a cloud of data points as a step towards automation of the modeling process. Virtual venues for public access to cultural heritage objects including real time interactive simulation of cultural heritage sites over the Web are discussed and illustrated.

This paper presents a technique for image recognition, reconstruction, and processing using a novel massively parallel system. This device is a physical implementation of a Boltzmann machine type of neural network based on the use of magnetic thin films and opto-magnetic control. Images or patterns in the form of pixel arrays are imposed on the magnetic film using a laser in an external magnetic field. These images are learned and can be recalled later when a similar image is presented. A stored image is recallable even when a partial, noisy, or corrupted version of that image is imposed on the film. The system can also be used for feature detection and image compression. The operation and construction of the physical system is described, together with a discussion of the physical basis for its operation. The authors have developed Monte Carlo style computer simulations of the system for a variety of platforms, including serial workstations and hypercube configured parallel systems. They describe here some of the factors involved in computer simulations of the system, which can be fast and relatively simple in implementation. Simulation results are presented and, in particular, the behavior of the model under simulated annealing in the light of statistical physics is discussed. The simulation itself can be used as a neural network model capable of the functions ascribed to the physical device.