Subset Warping: Rubber Sheeting with Cuts

Image warping, often referred to as "rubber sheeting" represents the deformation of a domain image space into a range image space. In this paper, a technique is described which extends the definition of a rubber-sheet transformation to allow a polygonal region to be warped into one or more subsets of itself, where the subsets may be multiply connected. To do this, it constructs a set of "slits" in the domain image, which correspond to discontinuities in the range image, using a technique based on generalized Voronoi diagrams. The concept of medial axis is extended to describe inner and outer medial contours of a polygon. Polygonal regions are decomposed into annular subregions, and path homotopies are introduced to describe the annular subregions. These constructions motivate the definition of a ladder, which guides the construction of grid point pairs necessary to effect the warp itself

Circumference imaging for optical based identification of cylindrical and conical objects

Inspection and identification of cylindrical or conical shaped objects presents a unique challenge for a machine vision system. Due to the circular nature of the objects it is difficult to image the whole object using traditional area cameras and image capture methods. This work describes a unique technique to acquire a two dimensional image of the entire surface circumference of a cylindrical/conical shaped object. The specific application of this method is the identification of large caliber (155 mm) ammunition rounds in the field as they are transported between or within vehicles. The proposed method utilizes a line scan camera in combination with high speed image acquisition and processing hardware to acquire images from multiple cameras and generate a single, geometrically accurate, surface image. The primary steps involved are the capture of multiple images as the ammunition moves by on the conveyor followed by warping to correct for the distortion induced by the curved projectile surface. The individual images are then tiled together to form one two-dimensional image of the complete circumference. Once this image has been formed an automatic identification algorithm begins the feature extraction and classification process

MOMS: Maximal-Order Interpolation of Minimal Support

We consider the problem of interpolating a signal using a linear combination of shifted versions of a compactly-supported basis function φ(x). We first give the expression of the φ's that have minimal support for a given accuracy (also known as "approximation order"). This class of functions, which we call maximal-order-minimal-support functions (MOMS), is made of linear combinations of the B-spline of same order and of its derivatives. We provide the explicit form of the MOMS that maximize the approximation accuracy when the step-size is small enough. We compute the sampling gain obtained by using these optimal basis functions over the splines of same order. We show that it is already substantial for small orders and that it further increases with the approximation order L. When L is large, this sampling gain becomes linear; more specifically, its exact asymptotic expression is (2 L ⁄ (π × e)). Since the optimal functions are continuous, but not differentiable, for even orders, and even only piecewise continuous for odd orders, our result implies that regularity has little to do with approximating performance. These theoretical findings are corroborated by experimental evidence that involves compounded rotations of images

Circular slit maps of multiply connected regions with application to brain image processing

In this paper, we present a fast boundary integral equation method for the numerical conformal mapping and its inverse of bounded multiply connected regions onto a disk and annulus with circular slits regions. The method is based on two uniquely solvable boundary integral equations with Neumann-type and generalized Neumann kernels. The integral equations related to the mappings are solved numerically using combination of Nyström method, GMRES method, and fast multipole method. The complexity of this new algorithm is O((M+ 1) n) , where M+ 1 stands for the multiplicity of the multiply connected region and n refers to the number of nodes on each boundary component. Previous algorithms require O((M+ 1) 3n3) operations. The numerical results of some test calculations demonstrate that our method is capable of handling regions with complex geometry and very high connectivity. An application of the method on medical human brain image processing is also presented

The Application Of RISC Processors To Training Simulators

Report on a study of the utility of reduced instruction set computer processors as the control computers in a training simulator. Report includes a master\u27s thesis on detailed hardware design for interfacing transputer hardware to the NeXT computer

Evaluation and optimization of central vision compensation techniques

Non-costly, non-invasive, safe, and reliable electronic vision enhancement systems (EVES) and their methods have presented a huge medical and industrial demand in the early 21st century. Two unique, vision compensation and enhancement algorithms are reviewed and compared, qualitatively optimizing the view of a restricted (or truncated) image. The first is described as the convex or fish-eye technique, and the second is the cartoon superimposition or Peli technique (after the leading author for this research). The novelty in this dissertation is in presenting and analyzing both of these with a comparison to a novel technique, motivated by characterization of quality vision parameters (or the distribution of photoreceptors in the eye), in an attempt to account for and compensate reported viewing difficulties and low image quality measures associated with these two existing methods.;This partial cartoon technique is based on introducing the invisible image to the immediate left and right of the truncated image as a superimposed cartoon into respective sides of the truncated image, yet only on a partial basis as not to distract the central view of the image. It is generated and evaluated using MatlabRTM to warp sample grayscale images according to predefined parameters such as warping method, cartoon and other warping parameters, different grayscale values, as well as comparing both the static and movie modes. Warped images are quantitatively compared by evaluating the Root-Mean-Square Error (RMSE) and the Universal Image Quality Index (UIQI), both representing image distortion and quality measures of warped, as compared to original images for five different scenes; landscape, close-up, obstacle, text, and home (or low-illumination) views. Remapped images are also evaluated through surveys performed on 115 subjects, where improvement is assessed using measures of image detail and distortion.;It is finally concluded that the presented partial cartoon method exhibits superior image quality for all objective measures, as well as for a majority of subjective distortion measures. Justification is provided as to why the technique does not offer superior subjective detail measures. Further improvement is suggested, as well as additional techniques and research

Evaluation of the Optical Laser Scanning System for Facial Identification

Facial reconstruction is applied when there is no information available with regard to the possible identity of the deceased and no information such as antemortem records obtainable for comparison with the postmortem records of the human remains in question. The aim of the techniques involved in facial reconstruction is to produce a face, which can be recognized as belonging to a specific person, by relatives or friends of the missing person. Once such a recognition has been made, then the specialist can apply other techniques to confirm or refute identity. The reconstructed face can be publicized in newspapers or other mass media, in order to facilitate recognition. Facial reconstruction is based essentially on the data taken from measurements of soft tissue thickness, primarily on the cadaver head and face and on the relationships between facial features of the face and underlying bone. Recently measurements of soft tissue thickness have been carried out in living persons using ultrasound. Facial reconstruction has until now been carried out by the sculpting technique. There are two methods or a combination of both that can be used to achieve the reconstruction of facial features from a skull. The first is to use soft tissue thickness tables available to reconstruct the contour of the face at selected classical points. The depths of soft tissue are represented by markers placed directly on the skull which are then connected using bands of clay or plasticine or similar materials. The facial features are then formed using the same material. The second method involves reconstruction of the anatomical features of the face, applying the observation and comparison of sites of muscle attachments and other features to sculpt the facial muscles onto the skull using their bony attachments as indicators of size and extent, then applying more clay to the depths specified in tissue thickness tables to approximate the various structures, according to the anatomy of the face. The sculptor thus rebuilds the face anatomically as it would be found in life or from anatomical dissection. A combination of both methods can be used, as is the practice in Great Britain. These two major methods represent the two principal schools involved in the development of facial reconstruction; these are the American and Russian respectively. Application of the current techniques of facial reconstruction to individual identification has proven successful in obtaining personal identification in many forensic cases. However, according to the literature, the techniques on their own are still a long way from being accepted as definitive methods for identification. The latest investigations all agree that much research remains to be carried out to produce improvement in the reconstruction techniques. Problems remain unsolved which have a great impact on the final results, especially the relationship between the details of the facial features such as the eyes, nose, lips and ears for which the underlying bone does not provide information. Recently studies have shown the importance of these features as "good indicators" of facial recognition. This study presents a method of facial reconstruction using an optical surface laser scanner system with an evaluation of the system for facial identification. The comparative analysis was carried out using facial anthropometry. The study was performed on a sample of plaster casts of skulls exhumed from a mass grave from a south American country. Photographs of missing persons thought to be of persons from this grave were also supplied to the Facial Identification Centre. These samples were all examined in the Facial Identification Centre of the Department of Forensic Medicine and Science of Glasgow University. By collecting a set of measurements and calculation of proportion indices, using computerized facial anthropometry and photogrammetry, a comparison was made between the facial reconstructions and photographs and the results are presented in this study. The computer method involves initially digitising plaster casts of skulls using a laser scanner and video camera interfaced to a computer. An average male face from a databank, is then placed over the plaster casts of skulls as a mask and the soft tissue thicknesses are modified to conform with the underlying skull. The advantage of this technique is its speed and flexibility. Nevertheless the technique is not perfect. It shares the same problems when the reconstruction is performed by sculpting; i.e., the relationship between facial features such as eyes, nose, lips and ears. Results from the study have assessed the reliability of facial reconstruction using an optical surface laser scanner system. The system has some limitations but was able to produce a good resemblance between the finished reconstructed faces and the photographs of the missing persons. The morphological assessment was supported by facial anthropometry. Results from facial anthropometry were in turn strongly supported by statistical methods. The optical surface laser scanner in fact played an important role in the positive identification of sixteen cases of the sample studied. The identification, acting as supporting evidence for more positive techniques. The technique has been shown to be useful in personal identification, acting as supporting evidence for positive techniques