Digital image segmentation using periodic codings is explored with reference to two applications. First, the application of uniform periodic codings, to the problem of segmenting the in-focus regions in an image from the blurred parts, is discussed. The work presented in this part extends a previous investigation on this subject by considering the leakage effects. The method proposed consists of two stages. In each stage, filtering is done in the spatial frequency domain after uniform grating functions are applied to the images in the spatial domain. Then, algorithms for finding the period and phase of a physical grating are explored for a hybrid optical-digital application of the method. Second, a model for textures as the linear superposition of periodic narrowband components, defined as tones, is proposed. A priori information about the number of the tones, their spatial frequencies, and coefficients is necessary to generate tone and texture indicators. Tone indicators are obtained by filtering the image with complex analytical functions defined by the spatial frequencies of the tones present in the image. A criterion for choosing the dimensions of the filter is also provided. Texture indicators are then generated for each texture in the image by applying the a priori information of the tonal coefficients to the filtered images. Several methods for texture segmentation which employ texture indicators are proposed. Finally, examples which illustrate the characteristics of the method are presented. / Master of Science

The objective of this study was to develop an inexpensive automated device for grading raw oyster meats. The automation technique chosen was digital imaging. Typically, a computer vision system contains a microcomputer and a digital camera. An inexpensive digital camera connected to a personal computer was used to measure the projected area of the oyster meats. Physical characteristics of the oyster meats were important in designing a computer vision grading system and the necessary data were not found in the literature. Selected physical characteristics of oyster meats, including the projected area, weight, height, and volume were measured by independent methods. The digital image areas were found to be highly correlated to oyster meat volumes and weights. Currently oysters are marketed on the basis of volume. The results from this study indicated that the relationship between the oyster meat area as measured by computer vision and volume can be used as a grading criterion. The oysters ranged in volume from 3.5 cm³ to 19.4 cm³ A three dimensional image was not required because the height was not important. Tests showed that the system was consistent and successfully graded 5 oysters per second. The system was calibrated, and the prediction equation was validated with an estimated measurement error of ± 3.04 cm³ at a 95% confidence level. The development of automated graders using digital imaging techniques could help improve the quality and consistency of the graded oyster meats. / Master of Science

A versatile I/O system for a real time image processor and a complex clocking circuit for the I/O system and the image processor have been designed. The I/O system receives data from an arbitrary video source. These data are digitized and conditioned to be compatible with the image processor. The image processor output is conditioned such that these data can be displayed on a standard RS l7O 2:l video monitor. Variable frame rate reduction. circuits and. bit reduction techniques such as line, column and dot interlace are incorporated during output conditioning. Experiments on reducing the frame rate and bit rate of a processed image can be carried out using this I/O system. / Master of Science

In the quest for computer vision, that is the automatic understanding of images, a powerful strategy has been to model the image parametrically. Two prominent kinds of approaches have been those based. on polynomial models and those based on random-field models. This thesis combines these two methodologies, deciding on the proper model by means of a general decision criterion. The unified approach also admits composite polynomial/random-field. models and is applicable to other statistical models as well. This new approach has advantages in many applications, such as image identification and image segmentation. In segmentation, we achieve speed by avoiding iterative pixel-by-pixel calculations. With the general decision criterion as a sophisticated tool, we can deal with images according to a variety of model hypotheses. Our experiments with synthesized images and real images, such as Brodatz textures, illustrate some identification and segmentation uses of the unified approach. / Master of Science / incomplete_metadata

A complete mathematical treatment is given for describing the topographic primal sketch of the underlying grey tone intensity surface of a digital image. Each picture element is independently classified and assigned a unique descriptive label, invariant under monotonically increasing grey tone transformations, from the set {peak, pit, ridge, ravine, saddle, flat, and hillside}, with hillside having subcategories {inflection point, slope, convex hill, concave hill, and saddle hill}. The topographic classification is based on the first and second directional derivatives of the estimated image intensity surface. Three different sets of basis functions, bicubic polynomial (local facet model), generalized splines, and the discrete cosine basis, are used to estimate the image intensity surface using a least squares technique. Zero-crossings of the first directional derivative are identified as locations of interest in the image. / M.S.

No description available.

Three fundamental problems in computer vision are addressed in this dissertation. The first deals with the problem of how to extract and assemble a rich symbolic representation of the gray level intensity changes in an image. Results show that the facet model based feature extraction scheme proposed here is superior to the other existing techniques. The second problem addressed deals with the interpretation of the resulting structures as three-dimensional object surfaces. The three different shape modules described in this dissertation are found to be useful in the recovery of intrinsic scene characteristics. Finally, mechanisms for interaction among different sources of information obtained from different shape modules are studied. It is demonstrated that interactions among shape modules can enhance the data acquired by different means. / Ph. D.

The spatial distribution of discrete sample points from an image affect digital image manipulation. The geometries of the grid lattice and edge are described for digital images. Edge detecting digital filters are considered for segmenting an image. A comparison is developed between digital filters for two different digital image grid lattice geometries — 8-neighbor grid lattice (rectangular tesselation) and the 6-neighbor grid lattice (hexagonal tesselation). Digital filters for discrete images are developed that are best approximations to the Laplacian operator applied to continuous two- dimensional mathematical surfaces. Discrepancies between the calculated Laplacian and the digital filtering results are analyzed and a criterion is developed that compares grid lattice effects. The criterion shows that digital filtering in a 6-neighbor grid lattice is preferable to digital filtering in an 8-neighbor grid lattice. / Master of Science

The Hough transform is a well known method for detecting lines in digital imagery. The results of the transform may be termed accurate if the lines detected correspond to lines occurring in the digital image. The accuracy of the transform is affected by the manner in which the transform is designed. Factors which affect transform accuracy include parameter space resolution, image space quantizing errors, parameter space quantizing errors, and Hough space peak detection strategy. One way to improve the accuracy of the transform is to weight each pixel’s Hough space sinusoid by an amount which reflects the pixel’s grey level contribution to an image space feature. This paper examines the behavior of the transform when a grey level weighting scheme is used. The effects of image space quantizing errors, parameter space quantizing errors, and parameter space resolution on the accuracy of the transform are also investigated. A general Hough space peak detection filter is proposed, and experimental results showing the feasibility of both the grey level weighting scheme and peak detection filter are presented. / Master of Science

Abstract not available