The intent of this study was to quantify the fracture surface area of dental composites subjected to different aging media. Dental composites, a combination of a resin and glass filler particles, were examined using a high resolution microtomography system developed at beamline 2-BM of the Advanced Photon Source (APS). The composite specimens were 2 mm in diameter and 3 mm in height subjected to a compression load. The initial data set of images was taken with no load, then the load was incrementally increased, a new scan taken, repeatedly, until failure occurred. The images obtained from the tomography scans were reconstructed and analyzed to provide a 3D representation of the crack. This reconstruction involved determining the total solid area, the total area which includes the crack interfaces, and then just the total crack interface area. A ratio was then determined between the control and the loaded specimen. The specimens were aged in various media for 3 months. Preliminary 3D analysis corresponded to previous studies with respect to the aging media and load, i.e., higher loads and aging in ethanol resulted in weaker materials and in this case increased crack areas and compression of the material. When sufficient samples are processed (at present N=6) this 3D analysis will allow statistical comparison of crack area. Supported by NIDCR grant DE07979. Use of the APS was supported by the U.S. DOE, Office of Science, Basic Energy Sciences, under Contract No. W-31-109-ENG-38.
Silhouette-based shape retrieval and recognition have been well studied, because silhouettes are compact representations of object shape, and because they can be reliably extracted in controlled-environment applications such as digitizing museum collections. In past work, we developed a fast and accurate method for retrieval and recognition of object silhouettes and other closed planar contours. The method is based on a combination of alignment, correspondence, eigenspace dimensionality reduction, and example-based retrieval. Its efficiency and accuracy result from the particular forms of each of these components and the way they are combined. This paper presents two improvements to the method: non-uniform sampling and a new similarity measure. The improved method ranks first in retrieval accuracy in comparison with eight prior methods tested on a benchmark database of 1,400 shapes. Its classification accuracy is 96.8% for the first-ranked class hypothesis, and it returns the correct classification in the top ten hypotheses 99.8% of the time. Average time for retrieval and recognition is approximately 0.6 seconds in Matlab on a PC.
This paper presents a method for fast and effective similarity-based shape retrieval. Shape similarity is determined by comparing the frequencies with which different types of local structure occur in each shape. The system consists of three processes. (1) The segmentation process uses a scale-space approach to find convex segments that lie between curvature zero-crossings at all scales. Local shape structure is represented by short sequences of segments, called terms. (2) The representation process classifiers the terms into types based on a set of local shape features. Then the distribution of term types within the shape is computed. (3) The retrieval process compares the term type distribution of the query shape to the term type distributions of the database shapes and retrieves the most similar database shapes. Efficient data structures are used to store the distributions compactly and to support fast retrieval. The performance of the method on a test database ranged from 69 percent to 100 percent of ideal performance, depending on the number of items retrieved.
A new approach to finding the 3D orientation of a textured planar surface is presented. By decomposing image space in a novel way that reflects the structure of the problem to be solved, a natural set of filters indexed by 3D orientation is obtained. The filters are applied at a single point in the image and the maximally responding filter is found. Its 3D orientation indices specify the orientation of the surface. The set of filters is large, making this 'pure' approach computationally expensive, so a method for using Gabor filters to select a subset of the 3D orientation detecting filters is presented. The result is a computationally efficient, practical algorithm. Only filter applications at a single point and simple operations on the filter outputs are needed. This method makes use of texture gradient information without the need for combining explicit measurements from multiple image points. The surface texture is assumed to be locally homogeneous, but not necessarily isotropic. The algorithm has an average error of 4 degrees in slant and tilt on a set of twelve images of real textured surfaces. The idea of decomposing the space of images according to the structure of the 3D information to be computed is a powerful one that we expect will apply to other vision problems. Also, the image space decomposition and filter functions developed here can serve as a model for surface orientation perception in biological vision.
We present a method for measuring the three-dimensional orientation of planar surfaces. We derive a model relating the spatially varying instantaneous frequency of the image texture to the instantaneous frequency of the surface texture, to the orientation of the surface, and to the parameters of the imaging system. We measure the localized frequency at each image point with Gabor wavelets and use it to solve for the surface orientation according to the model. The method does not require the extraction of discrete texture elements. The algorithm has a mean error of about 5 degrees in the measured slant and tilt on a test set of 12 real-world surfaces.
We present a method for making accurate measurements of the instantaneous fractal dimension of (1) images
modeled as fractal Brownian surfaces, and (2) images of physical surfaces modeled as fractal Brownian surfaces. Fractal
Brownian surfaces have the property that their apparent roughness increases as the viewing distance decreases. Since this
true of many natural surfaces, fractal Brownian surfaces are excellent candithtes for modeling rough surfaces.
To obtain accurate local values of the fractal dimension, spatio-spectrally localized measurements are necessary.
Our method employs Gabor filters, which optimize the conflicting goals of spatial and speciral localization as constrained
by the functional uncertainty principle. The outputs from multiple Gabor filters are fitted to a fractal power-law curve
whose parameters determine the fractal dimension. The algorithm produces a local value of the fractal dimension for every
point in the image. We also introduce a variational technique for producing a fractal dimension function which varies
smoothly across the image. This technique is implemented using an iterative relaxation algorithm.
A test of the method on 50 synthetic images of known global fractal dimensions shows that the method is accurate
with an error of approximately 4.5% for fractal Brownian images and approximately 8.5% for images of physical fractal