Abstract: A computer-implemented image processing technique for selectively recovering the features of an original CAD model after the original CAD model has been converted to a digitized image and a new CAD model generated from the digitized image. The original boundary representation provides a template to transform the representation through processing under governance of a programmed processor so as to recover accuracy and reintroduce feature edges and feature corners as well as other detailed features to the CAD model obtained from the digitized image, e.g., to enable detailed features to be retained that would otherwise have been lost due to the lossy conversion into image space. The method operates to better ensure that reconstructed boundary vertices lie on original CAD model surfaces and feature edges and corners are recovered.

Abstract: A computer-implemented image processing technique for selectively recovering the features of an original CAD model after the original CAD model has been converted to a digitized image and a new CAD model generated from the digitized image. The original boundary representation provides a template to transform the representation through processing under governance of a programmed processor so as to recover accuracy and reintroduce feature edges and feature corners as well as other detailed features to the CAD model obtained from the digitized image, e.g., to enable detailed features to be retained that would otherwise have been lost due to the lossy conversion into image space. The method operates to better ensure that reconstructed boundary vertices lie on original CAD model surfaces and feature edges and corners are recovered.

Abstract: A computer-implemented image processing technique for selectively recovering the features of an original CAD model after the original CAD model has been converted to a digitized image and a new CAD model generated from the digitized image. The original boundary representation provides a template to transform the representation through processing under governance of a programmed processor so as to recover accuracy and reintroduce feature edges and feature corners as well as other detailed features to the CAD model obtained from the digitized image, e.g., to enable detailed features to be retained that would otherwise have been lost due to the lossy conversion into image space. The method operates to better ensure that reconstructed boundary vertices lie on original CAD model surfaces and feature edges and corners are recovered.

Abstract: A computer-implemented image processing technique for selectively recovering the features of an original CAD model after the original CAD model has been converted to a digitized image and a new CAD model generated from the digitized image. The original boundary representation provides a template to transform the representation through processing under governance of a programmed processor so as to recover accuracy and reintroduce feature edges and feature corners as well as other detailed features to the CAD model obtained from the digitized image, e.g., to enable detailed features to be retained that would otherwise have been lost due to the lossy conversion into image space. The method operates to better ensure that reconstructed boundary vertices lie on original CAD model surfaces and feature edges and corners are recovered.

Abstract: A method for preparing image data for three-dimensional printing in which a digitized (e.g. voxelized) representation of a virtual three-dimensional object (e.g. CAD model) is eroded to create an internal volume for the object. Subsequently, a vector-based surface representation of this internal volume is generated and simply combined with a corresponding vector-based surface representation of the original virtual three-dimensional object to yield a hollowed out model in a format suitable for three-dimensional printing. A microstructure may be introduced into the interior of the hollowed out model, e.g. by extracting a volume corresponding to the inverse of that microstructure from the eroded digitized representation.

Abstract: A modified VOMAC mesh generation method for image data sampled from an actual object in which mesh resolution can vary locally within the mesh while permitting control of the distortion of cells in the mesh to maintain suitability for performing finite element (or finite volume) analysis on a representation of the object. The method includes computer-implemented instructions that calculate a variable sampling point distribution (SPD) in image data space, the variable SPD having localized variations in SPD within the image data space, and the distribution of sampling points having the same topology as a uniform SPD suitable for obtaining a regular structured is mesh. The method includes generating an indication of the magnitude of cell distortion between the generated mesh and the regular structured mesh Varying the mesh resolution may enable smaller elements to be located in regions of particular interest or activity when subsequently performing analysis using the mesh model.

Abstract: A method for preparing image data for three-dimensional printing in which a digitised (e.g. voxelized) representation of a virtual three-dimensional object (e.g. CAD model) is eroded to create an internal volume for the object. Subsequently, a vector-based surface representation of this internal volume is generated and simply combined with a corresponding vector-based surface representation of the original virtual three-dimensional object to yield a hollowed out model in a format suitable for three-dimensional printing. A microstructure may be introduced into the interior of the hollowed out model, e.g. by extracting a volume corresponding to the inverse of that microstructure from the eroded digitised representation.

Abstract: A modified VOMAC mesh generation method for image data sampled from an actual object in which mesh resolution can vary locally within the mesh whilst permitting control of the distortion of cells in the mesh to maintain suitability for performing finite element (or finite volume) analysis on a representation of the object. The method includes computer-implemented instructions that calculate a variable sampling point distribution (SPD) in image data space, the variable SPD having localized variations in SPD within the image data space, and the distribution of sampling points having the same topology as a uniform SPD suitable for obtaining a regular structured is mesh. The method includes generating an indication of the magnitude of cell distortion between the generated mesh and the regular structured mesh Varying the mesh resolution may enable smaller elements to be located in regions of particular interest or activity when subsequently performing analysis using the mesh model.

Abstract: An image processing method operates on a colored digitised image in a computer (2) to derive the image in abstract computer representation. Color is used to pre-classify symbols and other features of the digital image in order to simplify subsequent character recognition steps. By preparing the image in accordance with rules such that a unique color is used for all features of a defined set, and by storing those features in the computer in computer representation in corresponding sets, once a match has been made between a graphic feature and a stored feature, then the color of that graphic feature is made unique to the set of stored features containing the matched feature. The remaining graphic features of that color then need to be compared only with the stored features in the same set, rather than with all the stored features (of all the sets), thus significantly reducing the character recognition processing that is required to effect matching.