NOTIFICACIÓN DE LA OBLIGACIÓN CON LA GLOSA

The document is structured to present the results of the research through the description and illustration of the various CV models, their functionality and methods when applied to AR and RAL environments. This works is presented in an incremental form (chapters) as a number of building blocks to achieve the eventual goals of this research. The chapters consist of the following information.

Chapter 2; Consists of the literature review of Remote Access Laboratories, Augmented Reality and relevant Computer Vision research. The review is discussed as a broad summary of the current state of the research.

Chapter 3: Develops the narrative for AR RAL research, and describes the framework for current vision analysis systems. The shortcomings of the current computer vision models in respect to AR RAL systems are presented and builds the requirements for this research. From the requirements, the research questions are posed.

Chapter 4: Discusses the various sources of noise within digital images, and defines a number of image filter functions which improve the signal-to-noise ratio within the images. The types of CV filters, their nature and mathematical representations are described. The purpose of filter functions is explained in relation to these works. Also

provides a series of test results regarding the processing time constraints of the CV models, and their suitability for use within the AR RAL environment.

Chapter 5: Introduces a contribution regarding the objective performance testing of CV edge detection models via ground truth models. This novel method has removed a significant level of subjectiveness in image output assessments after undergoing CV edge detection processes.

Chapter 6: Discusses the various Computer Vision image analysis functions, which covers digital image segmentation, colour indexing and edge or feature detection. The nature of the models, mathematical representation, response functions and key features are described. The testing regime is defined and the means to validate the performance. Each of the CV image analysis model is tested and its performance measured. The needs and suitability for AR RAL systems are explained.

Chapter 7: Introduces a contribution defining a method to create a unique signature associated with an image object. This approach calculates a gradient vector from neighbourhood pixel gradients, to create a unique vector representing the object of interest. The performance of the contribution is evaluated in follow-on chapters. Chapter 8: Introduces a contribution for object segmentation and/or object detection through colour histograms. The method employed reduces current colour histograms to two-dimensions instead of the normal three, improving processing speeds and also improving the relationship between colour spatial distances. Use as a segmentation process and for object matching is verified in follow-on chapters.

Chapter 9: Defines the methods available to determine the extent to which two objects match. Object matching methods are key to successful object tracking, therefore fast and efficient mathematic comparison methods are required to measure the likelihood that two object signatures match. Methods discussed are implemented within the object detection and object tracking chapters.

Chapter 10: Defines one of the major experimentation portions of this research. Object detection models are selected for testing, based on previous chapter reviews. Test scenarios are defined which includes the testing regime and results. Validation and verification of model performance is included.

Chapter 11: Defines the second major experimentation portion of this research. Key requirements for CV object tracking within the AR RAL environment is discussed. The object tracking testing regime is defined and the selected CV object detection methods are assessed as to their suitability as an object tracking agent. Performance measures for candidate object tracking mechanisms are reviewed to ascertain models that are suitable for the AR RAL environment.

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Literature Review

This chapter lists the development pathways for remote access laboratories and augmented reality, providing the key literature associated with the investigation of the research question.

Remote Access Laboratories provide a mechanism for students to access teaching resources from a distance. The capabilities and implementations of Remote Access Laboratories vary and are determined by the institution implementing the system. Science and Engineering faculties generally lead the ventures as a result of the nature the cohort’s training and skill base. (Engineers are more likely to build the hardware and software required). Developing and building Remote Access Laboratories benefits both the student and institution through greater access of expensive laboratory resources [37]. Equipment utilisation is able to reach 100%, greatly enhancing the cost benefits of such equipment. Students are able to access and interact with the equipment at any time in any location. Students also develop a sense of independence through autonomous learning and confidence by means of familiarity with the equipment and the experiment.

Augmented Reality is built upon reality. It involves enhancing our senses and experience of reality through the interactive computer-generated feedback of information not normally available or formatted for our senses. Without the user modifying parameters of the environment to interact with the interface, then any presented data is modeless. Key to the AR definition is the user interactivity with the environment [68]. The differing realities can be seen on Figure 1-2 in which Augmented is located between Reality and Virtual Reality. Milgram [41] places Augmented Reality within the left half of the continuum, and Augmented Virtuality on the right half. For

many tasks, user interfaces could be considered to move anywhere along the continuum [75].

The difference between Augmented Reality and Virtual Reality is that Virtual Reality replaces our sense of reality while Augmented Reality maintains it, but enhances it in a homogeneous environment.

Augmented Reality within the Remote Access Laboratory framework is a very new area of research, and consequently has very little literature available. The aspects of the research question rely upon the current work undertaken in the fields of remote laboratories, augmented reality and more specifically, computer vision. A review of relevant aspects for each field in relation to the research question follows.