Parallelization for image processing algorithms based chain and mid-crack codes

Freeman chain code is a widely-used description for a contour image. Another mid-crack code algorithm was proposed as a more precise method for image representation. We have developed a coding algorithm which is suitable to generate either chain code description or mid-crack code description by switching between two different tables. Since there is a strong urge to use parallel processing in image related problems, a parallel coding algorithm is implemented. This algorithm is developed on a pyramid architecture and a N cube architecture. Using link-list data structure and neighbor identification, the algorithm gains efficiency because no sorting or neighborhood pairing is needed. In this dissertation, the local symmetry deficiency (LSD) computation to calculate the local k-symmetry is embedded in the coding algorithm. Therefore, we can finish the code extraction and the LSD computation in one pass. The embedding process is not limited to the k-symmetry algorithm and has the capability of parallelism. An adaptive quadtree to chain code conversion algorithm is also presented. This algorithm is designed for constructing the chain codes of the resulting quadtree from the boolean operation of two quadtrees by using the chain codes of the original one. The algorithm has the parallelism and is ready to be implemented on a pyramid architecture. Our parallel processing approach can be viewed as a parallelization paradigm - a template to embed image processing algorithms in the chain coding process and to implement them in a parallel approach

A practical vision system for the detection of moving objects

The main goal of this thesis is to review and offer robust and efficient algorithms for the detection (or the segmentation) of foreground objects in indoor and outdoor scenes using colour image sequences captured by a stationary camera. For this purpose, the block diagram of a simple vision system is offered in Chapter 2. First this block diagram gives the idea of a precise order of blocks and their tasks, which should be performed to detect moving foreground objects. Second, a check mark () on the top right corner of a block indicates that this thesis contains a review of the most recent algorithms and/or some relevant research about it. In many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. Background subtraction has been widely used for this purpose as the first step. In this work, a review of the efficiency of a number of important background subtraction and modelling algorithms, along with their major features, are presented. In addition, two background approaches are offered. The first approach is a Pixel-based technique whereas the second one works at object level. For each approach, three algorithms are presented. They are called Selective Update Using Non-Foreground Pixels of the Input Image , Selective Update Using Temporal Averaging and Selective Update Using Temporal Median , respectively in this thesis. The first approach has some deficiencies, which makes it incapable to produce a correct dynamic background. Three methods of the second approach use an invariant colour filter and a suitable motion tracking technique, which selectively exclude foreground objects (or blobs) from the background frames. The difference between the three algorithms of the second approach is in updating process of the background pixels. It is shown that the Selective Update Using Temporal Median method produces the correct background image for each input frame. Representing foreground regions using their boundaries is also an important task. Thus, an appropriate RLE contour tracing algorithm has been implemented for this purpose. However, after the thresholding process, the boundaries of foreground regions often have jagged appearances. Thus, foreground regions may not correctly be recognised reliably due to their corrupted boundaries. A very efficient boundary smoothing method based on the RLE data is proposed in Chapter 7. It just smoothes the external and internal boundaries of foreground objects and does not distort the silhouettes of foreground objects. As a result, it is very fast and does not blur the image. Finally, the goal of this thesis has been presenting simple, practical and efficient algorithms with little constraints which can run in real time

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Document preprocessing and fuzzy unsupervised character classification

This dissertation presents document preprocessing and fuzzy unsupervised character classification for automatically reading daily-received office documents that have complex layout structures, such as multiple columns and mixed-mode contents of texts, graphics and half-tone pictures. First, the block segmentation algorithm is performed based on a simple two-step run-length smoothing to decompose a document into single-mode blocks. Next, the block classification is performed based on the clustering rules to classify each block into one of the types such as text, horizontal or vertical lines, graphics, and pictures. The mean white-to-black transition is shown as an invariance for textual blocks, and is useful for block discrimination. A fuzzy model for unsupervised character classification is designed to improve the robustness, correctness, and speed of the character recognition system. The classification procedures are divided into two stages. The first stage separates the characters into seven typographical categories based on word structures of a text line. The second stage uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. A fuzzy model of unsupervised character classification, which is more natural in the representation of prototypes for character matching, is defined and the weighted fuzzy similarity measure is explored. The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition procedure is simply applied on the limited versions of the fuzzy prototypes. To avoid information loss and extra distortion, an topography-based approach is proposed to apply directly on the fuzzy prototypes to extract the skeletons. First, a convolution by a bell-shaped function is performed to obtain a smooth surface. Second, the ridge points are extracted by rule-based topographic analysis of the structure. Third, a membership function is assigned to ridge points with values indicating the degrees of membership with respect to the skeleton of an object. Finally, the significant ridge points are linked to form strokes of skeleton, and the clues of eigenvalue variation are used to deal with degradation and preserve connectivity. Experimental results show that our algorithm can reduce the deformation of junction points and correctly extract the whole skeleton although a character is broken into pieces. For some characters merged together, the breaking candidates can be easily located by searching for the saddle points. A pruning algorithm is then applied on each breaking position. At last, a multiple context confirmation can be applied to increase the reliability of breaking hypotheses

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated

An automated targeting mechanism with free space optical communication functionality for optomechatronic applications

This thesis outlines the development of an agile, reliable and precise targeting mechanism complete with free space optical communication (FSOC) capabilities for employment in optomechatronic applications. To construct the complex mechanism, insight into existing technologies was required. These are inclusive to actuator design, control methodology, programming architecture, object recognition and localization and optical communication. Focusing on each component individually resulted in a variety of novel systems, commencing with the creation of a fast (1.3 ms⁻¹), accurate (micron range) voice coil actuator (VCA). The design, employing a planar, compact composition, with the inclusion of precision position feedback and smooth guidance fulfills size, weight and power (SWaP) characteristics required by many optomechatronic mechanisms. Arranging the VCAs in a parallel nature promoted the use of a parallel orientation manipulator (POM) as the foundation of the targeting structure. Motion control was achieved by adopting a cascade PID-PID control methodology in hardware, resulting in average settling times of 23 ms. In the pursuit of quick and dependable computation, a custom printed circuit board (PCB) containing a field programmable gate array (FPGA), microcontroller and image sensing technology were developed. Subsequently, hardware-based object isolation and parameter identification algorithms were constructed. Furthermore, by integrating these techniques with the dynamic performance of the POM, mathematical equations were generated to allow the targeting of an object in real-time with update rates of 70 ms. Finally, a FSOC architecture utilizing beam splitter technology was constructed and integrated into the targeting device. Thus, producing a system capable of automatically targeting an infrared (IR) light source while simultaneously receiving wireless optical communication achieving ranges beyond 30 feet, at rates of 1 Mbits per second

Three-dimensional eddy current pulsed thermography and its applications

Ph. D. Thesis.The measurement and quantification of defects is a challenge for Non-DestructiveTesting and Evaluation (NDT&E). Such challenges include the precise localisation and detection of surface and sub-surface defects, as well as the quantification of such defects. This work first reports a three-dimensional (3D) Eddy Current Pulsed Thermography (ECPT) system via integration with an RGB-D camera. Then, various quantitative measurements and analyses of defects are carried out based on the 3D ECPT system. The ECPT system at Newcastle University has been prooven to be an effective nondestructive testing (NDT) method in surface and sub-surface detection over the past few years. Based on the different numerical or analytical models, it has achieved precise defect detection on the rail tracks, wind turbines, carbon fibre reinforced plastic (CFRP) and so on. The ECPT system has the advantage of fast inspection and a large lift-off range. However, it involves a trade-off between detectable defect size and inspection area compared with other NDT methods. In addition, there are challenges of defect detection in a complex structure. Thus, the quantification of defects gives a higher requirement of the measurement the object geometry information. Furthermore, the analysis of thermal diffusion requires a precise 3D model. For this reason, a 3D ECPT system is proposed that adds each heat pixel with an exact X-Y-Z coordinate. In this work, first, the 3D ECPT system is built. A feature-based automatic calibration of the infrared camera and the RGB-D camera is proposed. Second, the software platform is built. A fast 3D visualization is completed with multi-threading technology and the Point Cloud Library. Lastly, various studies of defect localization, quantification and thermal tomography reconstruction are carried ou