Efficient implementations of machine vision algorithms using a dynamically typed programming language

Current machine vision systems (or at least their performance critical parts) are predominantly implemented using statically typed programming languages such as C, C++, or Java. Statically typed languages however are unsuitable for development and maintenance of large scale systems. When choosing a programming language, dynamically typed languages are usually not considered due to their lack of support for high-performance array operations. This thesis presents efficient implementations of machine vision algorithms with the (dynamically typed) Ruby programming language. The Ruby programming language was used, because it has the best support for meta-programming among the currently popular programming languages. Although the Ruby programming language was used, the approach presented in this thesis could be applied to any programming language which has equal or stronger support for meta-programming (e.g. Racket (former PLT Scheme)). A Ruby library for performing I/O and array operations was developed as part of this thesis. It is demonstrated how the library facilitates concise implementations of machine vision algorithms commonly used in industrial automation. I.e. this thesis is about a different way of implementing machine vision systems. The work could be applied to prototype and in some cases implement machine vision systems in industrial automation and robotics. The development of real-time machine vision software is facilitated as follows 1. A JIT compiler is used to achieve real-time performance. It is demonstrated that the Ruby syntax is sufficient to integrate the JIT compiler transparently. 2. Various I/O devices are integrated for seamless acquisition, display, and storage of video and audio data. In combination these two developments preserve the expressiveness of the Ruby programming language while providing good run-time performance of the resulting implementation. To validate this approach, the performance of different operations is compared with the performance of equivalent C/C++ programs

Hybrid optical and magnetic manipulation of microrobots

Microrobotic systems have the potential to provide precise manipulation on cellular level for diagnostics, drug delivery and surgical interventions. These systems vary from tethered to untethered microrobots with sizes below a micrometer to a few microns. However, their main disadvantage is that they do not have the same capabilities in terms of degrees-of-freedom, sensing and control as macroscale robotic systems. In particular, their lack of on-board sensing for pose or force feedback, their control methods and interface for automated or manual user control are limited as well as their geometry has few degrees-of-freedom making three-dimensional manipulation more challenging. This PhD project is on the development of a micromanipulation framework that can be used for single cell analysis using the Optical Tweezers as well as a combination of optical trapping and magnetic actuation for recon gurable microassembly. The focus is on untethered microrobots with sizes up to a few tens of microns that can be used in enclosed environments for ex vivo and in vitro medical applications. The work presented investigates the following aspects of microrobots for single cell analysis: i) The microfabrication procedure and design considerations that are taken into account in order to fabricate components for three-dimensional micromanipulation and microassembly, ii) vision-based methods to provide 6-degree-offreedom position and orientation feedback which is essential for closed-loop control, iii) manual and shared control manipulation methodologies that take into account the user input for multiple microrobot or three-dimensional microstructure manipulation and iv) a methodology for recon gurable microassembly combining the Optical Tweezers with magnetic actuation into a hybrid method of actuation for microassembly.Open Acces

Efficient implementations of machine vision algorithms using a dynamically typed programming language

Current machine vision systems (or at least their performance critical parts) are predominantly implemented using statically typed programming languages such as C, C++, or Java. Statically typed languages however are unsuitable for development and maintenance of large scale systems. When choosing a programming language, dynamically typed languages are usually not considered due to their lack of support for high-performance array operations. This thesis presents efficient implementations of machine vision algorithms with the (dynamically typed) Ruby programming language. The Ruby programming language was used, because it has the best support for meta-programming among the currently popular programming languages. Although the Ruby programming language was used, the approach presented in this thesis could be applied to any programming language which has equal or stronger support for meta-programming (e.g. Racket (former PLT Scheme)). A Ruby library for performing I/O and array operations was developed as part of this thesis. It is demonstrated how the library facilitates concise implementations of machine vision algorithms commonly used in industrial automation. I.e. this thesis is about a different way of implementing machine vision systems. The work could be applied to prototype and in some cases implement machine vision systems in industrial automation and robotics. The development of real-time machine vision software is facilitated as follows 1. A JIT compiler is used to achieve real-time performance. It is demonstrated that the Ruby syntax is sufficient to integrate the JIT compiler transparently. 2. Various I/O devices are integrated for seamless acquisition, display, and storage of video and audio data. In combination these two developments preserve the expressiveness of the Ruby programming language while providing good run-time performance of the resulting implementation. To validate this approach, the performance of different operations is compared with the performance of equivalent C/C++ programs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Template reduction of feature point models for rigid objects and application to tracking in microscope images.

This thesis addresses the problem of tracking rigid objects in video sequences. A novel approach to reducing the template size of shapes is presented. The reduced shape template can be used to enhance the performance of tracking, detection and recognition algorithms. The main idea consists of pre-calculating all possible positions and orientations that a shape can undergo for a given state space. From these states, it is possible to extract a set of points that uniquely and robustly characterises the shape for the considered state space. An algorithm, based on the Hough transform, has been developed to achieve this for discrete shapes, i.e. sets of points, projected in an image when the state space is bounded. An extended discussion on particle filters, that serves as an introduction to the topic, is presented, as well as some generic improvements. The introduction of these improvements allow the data to be better sampled by incorporating additional measurements and knowledge about the velocity of the tracked object. A partial re-initialisation scheme is also presented that enables faster recovery of the system when the object is temporarily occluded.A stencil estimator is introduced to identify the position of an object in an image. Some of its properties are discussed and demonstrated. The estimator can be efficiently evaluated using the bounded Hough transform algorithm. The performance of the stencilled Hough transform can be further enhanced with a methodology that decimates the stencils while maintaining the robustness of the tracker. Performance evaluations have demonstrated the relevance of the approach. Although the methods presented in this thesis could be adapted to full 3-D object motion, motions that maintain the same view of the object in front of a camera are more specifically studied

Computer vision methods for optical microscopes

As the fields of micro- and nano-technology mature, there will be an increased need to build tools that are able to work in these areas. Industry will require solutions for assembling and manipulating components, much as it has done in the macro range. With this need in mind, a new set of challenges requiring novel solutions have to be met. One of them is the ability to provide closed-loop feedback control for manipulators. We foresee that machine vision will play a leading role in this area. This paper introduces a technique for integrating machine vision into the field of micro-technology including two methods, one for tracking and one for depth reconstruction under an optical microscope. (C) 2006 Elsevier B.V. All rights reserved