High-speed pattern cutting using real-time computer vision techniques

This thesis presents a study of computer vision for guiding cutting tools to perform high-speed pattern cutting on deformable materials. Several new concepts on establishing a computer vision system to guide a C02 laser beam to separate lace are presented. The aim of this study is to determine a cutting path on lace in real-time by using computer vision techniques, which is part of an automatic lace separation project. The purpose of this project is to replace the current lace separation process which uses a mechanical knife or scissors. The research on computer vision has concentrated on the following aspects: 1. A weighted incremental tracking algorithm based on a reference map is proposed, examined and implemented. This is essential for tracking an arbitrarily defined path across the surface of a patterned deformable material such as lace. Two methods, a weighting function and infinite impulse response filter, are used to cope with lateral distortions of the input image. Three consecutive map lines matching with one image line is introduced to cope with longitudinal distortion. A software and hardware hybrid approach boosts the tracking speed to hnls that is 2-4 times faster than the current mechanical method. 2. A modified Hough transform and the weighted incremental tracking algorithm to find the start point for tracking are proposed and investigated to enable the tracking to start from the correct position on the map. 3. In order to maintain consistent working conditions for the vision system, the light source, camera threshold and camera scan rate synchronisation with lace movement are studied. Two test rigs combining the vision and cutting system have been built and used to cut lace successfully

Automated image-based quality control of molecularly imprinted polymer films

We present results of applying a feature extraction process to images of coatings of molecularly imprinted polymers (MIPs) coatings on glass substrates for defect detec- tion. Geometric features such as MIP side lengths, aspect ratio, internal angles, edge regularity, and edge strength are obtained by using Hough transforms, and Canny edge detection. A Self Organizing Map (SOM) is used for classification of texture of MIP surfaces. The SOM is trained on a data set comprised of images of manufactured MIPs. The raw images are first processed using Hough transforms and Canny edge detection to extract just the MIP-coated portion of the surface, allowing for surface area estimation and reduction of training set size. The training data set is comprised of 20-dimensional feature vectors, each of which is calculated from a single section of a gray scale image of a MIP. Haralick textures are among the quantifiers used as feature vector components. The training data is then processed using principal component analysis to reduce the number of dimensions of the data set. After training, the SOM is capable of classifying texture, including defects

Engineering Approaches for Neurobiology

Neurobiological systems span a wide dimensional range. We present a scale-driven methodological development for three biological systems to demonstrate the utility of applied engineering approaches in neurobiology and provide an avenue for future study. Concepts in computational modeling, microfluidic device platforms, and MRI phantoms are examined - starting from the level of a single synapse and concluding with long-distance cortical connectivity.Single synapse models were developed using a Monte Carlo simulation environment to study biophysically realistic mechanisms of spike timing dependent plasticity (STDP). A model of spatiotemporal intracellular Calcium detection was extended to include subunit-specific receptor kinetics and distributions. Using STDP-based activation protocols, global and local molecular time courses were then produced for NR2a and NR2b knockout models. To study network level oscillatory activity, a model of spatially-constrained networks was created based on cyclic geometry to look at the effects of circumference and track-width on spontaneous network activity. Transverse wave activity is demonstrated and characterized by velocity and origin. Microfluidic technology provides an experimental means to extend the study of network organization and activity in vitro. We have developed a microfluidic control platform that integrates multiple design strategies to address the intrinsic spatiotemporal resolution of neurons. Microfluidic devices were fabricated using multilayer soft-lithography with internal valves to guide multiple laminar streams. A control platform using dynamic pressure produces a targeted hydrodynamic stream from variable internal resistance control. Feedback containing video and pressure data provides online analysis of the microfluidic device. Devices were characterized with arbitrary profile generation, profile repeatability, flow rate measurement, and lid-driven flow production. Finally, a microfluidic phantom for diffusion-weighted magnetic resonance imaging was developed for validation studies of long-distance cortical white matter connections. The diffusion phantom provides a reliable physical structure with which high resolution fiber tractography methods can be tested against. The diffusion phantom was fabricated using conventional photolithographic techniques with an internal channel network that mimics white matter fiber tracts and crossings. We show mapped tracts to the features inside of the phantom via post-processing of diffusion-weighted images

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

COMPUTATIONAL APPROACHES TO UNDERSTAND PHENOTYPIC STRUCTURE AND CONSTITUTIVE MECHANICS RELATIONSHIPS OF SINGLE CELLS

The goal of this work is to better understand the relationship between the structure and function of biological cells by simulating their nonlinear mechanical behavior under static and dynamic loading using image structure-based finite element modeling (FEM). Vascular smooth muscle cells (VSMCs) are chosen for this study due to the strong correlation of the geometric arrangement of their structural components on their mechanical behavior and the implications of that behavior on diseases such as atherosclerosis. VSMCs are modeled here using a linear elastic material model together with truss elements, which simulate the cytoskeletal fiber network that provides the cells with much of their internal structural support. Geometric characterization of single VSMCs of two physiologically relevant phenotypes in 2D cell culture is achieved using confocal microscopy in conjunction with novel image processing techniques. These computer vision techniques use image segmentation, 2D frequency analysis, and linear programming approaches to create representative 3D model structures consisting of the cell nucleus, cytoplasm, and actin stress fiber network of each cell. These structures are then imported into MSC Patran for structural analysis with Marc. Mechanical characterization is achieved using atomic force microscopy (AFM) indentation. Material properties for each VSMC model are input based on values individually obtained through experimentation, and the results of each model are compared against those experimental values. This study is believed to be a significant step towards the viability of finite element models in the field of cellular mechanics because the geometries of the cells in the model are based on confocal microscopy images of actual cells and thus, the results of the model can be compared against experimental data for those same cells

Higher level techniques for the artistic rendering of images and video

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