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A model based on computer vision for pose recognition in ballet

  • Authors: Fourie, Margaux
  • Subjects: Computer vision , Ballet , Pattern recognition systems
  • Language: English
  • Type: Masters (Thesis)
  • Identifier: http://hdl.handle.net/10210/417405 , uj:35348
  • Description: Abstract: The presence of computer vision technology is continually expanding into multiple application domains. With the increased availability of new camera and sensor technologies, it is possible to gather the necessary data required to apply computer vision in multiple environments. The task of recognising human activities using technology is one of the primary applications of computer vision. A human activity and an art form that is particularly attractive for the application of computer vision algorithms is ballet. Due to the well-codified poses, along with the challenges that exist within the ballet domain, automation for the ballet environment is a relevant research problem. This dissertation considers the use of computer vision methods to recognise different ballet poses. A literature review is done first to determine if it is a valid problem and explore the current methods used within ballet. Accordingly, the study proposes a model called BaReCo for ballet pose recognition using computer vision, which serves as a guide for the implementation of the prototype system. A specialised benchmark is then created to assess various facets of the model. The developed solution effectively makes use of a captured dataset, which was created for this study. The implemented computer vision pipelines contain various stages including pre-processing, localisation, feature extraction and classification. Results are consequently derived from the prototype to address the benchmark. The results of the benchmark for the BaReCo implementation, show that the study accomplishes the objective of recognising ballet poses using computer vision methods. Some key findings indicate that closely related poses are the potential cause for errors in recognition. The results also reveal that the use of novel deep learning techniques such as OpenPose and neural networks, along with traditional classification approaches, yield promising results. The study additionally provides a ballet pose dataset which serves as a contribution to the ballet and computer vision community. Future work for the study includes making improvements to the current implementation, as well as the application of other computer vision approaches on images and video data. The prototype system has validated the use of computer vision in the ballet domain to achieve pose recognition successfully. , M.Sc. (Computer Science)
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A model for inebriation recognition in humans using computer vision

Aircraft recognition using generalised variable-kernel similarity metric learning

  • Authors: Naudé, Johannes Jochemus
  • Date: 2014-12-01
  • Subjects: Image processing , Computer vision , Pattern recognition systems , Optical pattern recognition , Airplanes - Recognition
  • Type: Thesis
  • Identifier: http://ujcontent.uj.ac.za8080/10210/387397 , uj:13139 , http://hdl.handle.net/10210/13113
  • Description: M.Ing. , Nearest neighbour classifiers are well suited for use in practical pattern recognition applications for a number of reasons, including ease of implementation, rapid training, justifiable decisions and low computational load. However their generalisation performance is perceived to be inferior to that of more complex methods such as neural networks or support vector machines. Closer inspection shows however that the generalisation performance actually varies widely depending on the dataset used. On certain problems they outperform all other known classifiers while on others they fail dismally. In this thesis we allege that their sensitivity to the metric used is the reason for their mercurial performance. We also discuss some of the remedies for this problem that have been suggested in the past, most notably the variable-kernel similarity metric learning technique, and introduce our own extension to this technique. Finally these metric learning techniques are evaluated on an aircraft recognition task and critically compared.
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An evaluation of local two-frame dense stereo matching algorithms

  • Authors: Van der Merwe, Juliaan Werner
  • Date: 2012-06-06
  • Subjects: Computer vision , Stereo vision , Depth perception , Image processing , Computer algorithms
  • Type: Thesis
  • Identifier: uj:2512 , http://hdl.handle.net/10210/4966
  • Description: M. Ing. , The process of extracting depth information from multiple two-dimensional images taken of the same scene is known as stereo vision. It is of central importance to the field of machine vision as it is a low level task required for many higher level applications. The past few decades has witnessed the development of hundreds of different stereo vision algorithms. This has made it difficult to classify and compare the various approaches to the problem. In this research we provide an overview of the types of approaches that exist to solve the problem of stereo vision. We focus on a specific subset of algorithms, known as local stereo algorithms. Our goal is to critically analyse and compare a representative sample of local stereo algorithm in terms of both speed and accuracy. We also divide the algorithms into discrete interchangeable components and experiment to determine the effect that each of the alternative components has on an algorithm’s speed and accuracy. We investigate even further to quantify and analyse the effect of various design choices within specific algorithm components. Finally we assemble all of the knowledge gained through the experimentation to compose and optimise a novel algorithm. The experimentation highlighted the fact that by far the most important component of a local stereo algorithm is the manner in which it aggregates matching costs. All of the top performing local stereo algorithms dynamically define the shape of the windows over which the matching costs are aggregated. This is done in a manner that aims to only include pixels in a window that is likely to be at the same depth as the depth of the centre pixel of the window. Since the depth is unknown, the cost aggregation techniques use colour and proximity information to best guess whether pixels are at the same depth when defining the shape of the aggregation windows. Local stereo algorithms are usually less accurate than global methods but they are supposed to be faster and more parallelisable. These cost aggregation techniques result in very accurate depth estimates but unfortunately they are also very expensive computationally. We believe the focus of local stereo algorithm development should be speed. Using the experimental results we developed an algorithm that achieves accuracies in the same order of magnitude as the state-of-the-art algorithms while reducing the computation time by over 50%.
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Computer vision based method for electrode slip measurement in a submerged arc-furnace

  • Authors: Jordan, Dominic Timothy
  • Date: 2012-06-04
  • Subjects: Smelting furnaces , Electrode slip measurement , Computer vision
  • Type: Thesis
  • Identifier: uj:2377 , http://hdl.handle.net/10210/4832
  • Description: M. Ing. , The purpose of this study is to investigate the use of computer vision techniques to measure the electrode slip. The study investigates a potential location for camera placement in the furnace housing, as well as the use of computer vision algorithms that could be used to solve the problem. A slip measurement algorithm is then designed, implemented and tested. The implemented slip measurement algorithm is based on the manual slip measurement technique, by measuring relative electrode and slip arm displacement between the electrode and the slip arm. The algorithm uses SURF invariant features to extract the electrode features and slip arm features in one frame, and match these features to the next frame SURF. Scene calibration is then used to relate the pixel slip measurement to a metric distance measurement. The experimental results proved that there is scope for applying computer vision techniques to address the slip measurement problem, using a single HD camera. However, there is room for improvement and the recommendations and future work are also discussed.
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Distributed image processing in an intranet environment

  • Authors: Van den Berg, Pierre
  • Date: 2012-08-28
  • Subjects: Image processing , Computer vision , Computer networks , Intranets (Computer networks)
  • Type: Thesis
  • Identifier: uj:3342 , http://hdl.handle.net/10210/6742
  • Description: M.Sc. , Image Processing/Computer Vision and Computer Networks (in particular Intranets) may seem to have very little in common if one only looks at these fields of study superficially. In this dissertation we will look at some fundamentals and characteristics of Image Processing and examine them to see where the problem areas lie, focusing on the problem of computational requirements. We will also examine the fundamental characteristics of Computer Networks and Distributed Processing, looking for areas where we can potentially find a synergy with computational problems inherent in Computer Vision/Image Processing. To accomplish the goals stated above, the dissertation is divided into three parts. The first part examines Computer Vision and Image Processing and is followed by a section examining Distributed Computing models and Computer Networks. The final part is dedicated to suggesting a model to solve the problem of computational load associated with Image Processing. The aim of the model is to take advantage of and use the latent processing power available in an Intranet environment by distributing the processing among the machines on the network. The model is also intended to be flexible and to minimize the network load incurred by distributing the processing. In order to do so, the model is split into units that deliver specialized functionality in order to keep the components small and also to incur the minimum load on a specific machine.
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Feature recognition in 3D surface models using self-organizing maps

  • Authors: Buhr, Richard Otto
  • Date: 2008-11-18T09:06:20Z
  • Subjects: Self-organizing maps , Neural networks (Computer science) , Computer vision
  • Type: Thesis
  • Identifier: uj:14724 , http://hdl.handle.net/10210/1729
  • Description: M.Ing. , This project investigates the use of Self-Organizing Maps (SOM) for feature recognition and analysis in 3D objects. Object data was generated to simulate data obtained from 3D scanning and trained using SOM. The trained data was analysed using speci cally developed software. The feature recognition and analysis process can be summarized as follows: a 3D object le is converted to a pure 3D data le, this data le is trained using the SOM algorithm after which the output is analyzed using a 3D object viewer and SOM data display.
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Image analysis using digitized video input

  • Authors: Spijkerman, Lambertus Gerrit
  • Date: 2014-11-20
  • Subjects: Computer vision , Image processing - Digital techniques
  • Type: Thesis
  • Identifier: uj:13098 , http://hdl.handle.net/10210/12976
  • Description: M.Sc. (Computer Science) , This dissertation examines the field of computer vision, with special attention being given to vision systems that support digitized video image analysis. The study may be broadly divided into three main sections. The first part offers an introduction to standard vision systems, focusing on the hardware architectures and the image analysis techniques used on them. Hardware configurations depend mainly on the selected frame-grabber and processor type. Parallel architectures are highlighted, as they represent the most suitable platform for a real-time digitized video image analysis system. The image analysis techniques discussed include: image preprocessing, segmentation, edge detection, optical flow analysis and optical character recognition. The second part of the study covers a number of real-world computer vision applications, and commercially available development environments. Several traffic surveillance systems are discussed in detail, as they relate to the practical vehicle identification system developed in the third part of the study. As mentioned above, the development of a Vehicle Identification Prototype, called VIP, forms the basis for the third and final part of this study. The VIP hardware requirements are given, and the software development and use is explained. The VIP's source code is provided so that it may be evaluated, or modified, by any interested parties.
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Intelligent system for automated components recognition and handling

  • Authors: Findlay, Peter
  • Date: 2012-02-06
  • Subjects: Computer vision , Artificial intelligence , Pattern recognition systems
  • Type: Thesis
  • Identifier: uj:2012 , http://hdl.handle.net/10210/4365
  • Description: M.Ing. , A machine vision system must, by definition, be intelligent, adaptable and reliable to satisfY the objectives of a system that is highly interactive with its dynamic environment and therefore prone to outside error factors. A machine vision system is described that utilizes a 2D captured web cam image for the purpose of intelligent object recognition, gripping and handling. The system is designed to be generic in its application and adaptable to various gripper configurations and handling configurations. This is achieved by using highly adaptable and intelligent recognition algorithms the gathers as much information as possible from a 2D colour web cam image. Numerous error-checking abilities are also built into the system to account for possible anomalies in the working environment. The entire system is designed around four separate but tightly integrated systems, namely the Recognition, Gripping and Handling structures and the Component Database which acts as the backbone of the system. The Recognition system provides all the input data that is then used for the Gripping and Handling systems. This integrated system functions as a single unit but a hierarchical structure has been used so that each of the systems can function as a stand-alone unit. The recognition system is generic in its ability to provide information such as recognized object identification, position and other orientation information that could be used by another handling system or gripper configuration. The Gripping system is based on a single custom designed gripper that provides basic gripping functionality. It is powered by a single motor and is highly functional with respect to the large range of object sizes that it can grip. The Handling Sub-system controls gripper positioning and motion. The Handling System incorporates control of the robot and the execution of both predetermined and online adaptable handling algorithms based on component data. It receives data from the Component database. The database allows the transparent ability to add and remove objects for recognition as well as other basic abilities. Experimental verification of the system is performed using a fully integrated and automated program and hardware control system developed for this purpose. The integration of the proposed system into a flexible and reconfigurable manufacturing system is explained.
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Linking digitized video input with optical character recognition

  • Authors: Bosch, Johannes Brits
  • Date: 2014-11-20
  • Subjects: Computer vision , Optical character recognition devices
  • Type: Thesis
  • Identifier: uj:13097 , http://hdl.handle.net/10210/12975
  • Description: M.Com. (Informatics) , This dissertation examines the field of computer vision, with special attention given to the recognition of alpha numeric characters on video images using OCR software. The study may be broadly divided into four sections. The first section offers an introduction to standard OCR (Optical Character Recognition) methods that have evolved over the years and have been incorporated into some commercial software packages currently. The second section covers the problem of reading characters in a dynamic environment and also the problems experienced with the compatibility of current OCR software products. The third section of the dissertation looks at solutions for the problem mentioned in section two and creates a framework for a generic model in which any application should fit. The generic model is then described in detail. The framework should provide a foundation for interested parties to build, modify or improve the model. The final section gives examples of how the model should present a solution. Experimental results are looked at and the model is critically evaluated.
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Super features: a probabilistic approach to feature matching and correction

TB detection using modified Local Binary Pattern features

  • Authors: Leibstein, Joshua , Nel, Andre
  • Date: 2017
  • Subjects: Computer vision , Image processing , Biomedical imaging , Tuberculosis - Diagnosis
  • Language: English
  • Type: Conference proceedings
  • Identifier: http://hdl.handle.net/10210/218346 , uj:21760 , Citation: Leibstein, J. & Nel, A. 2017. TB detection using modified Local Binary Pattern features.
  • Description: Abstract: This paper explores a computer-aided detection scheme to aid radiologists in making a higher percentage of correct diagnoses when analysing chest radiographs. The approach undertaken in the detection process is to use several proprietary image processing algorithms to adjust, segment and classify a radiograph. Firstly, a Difference of Gaussian (DoG) energy normalisation method is applied to the image. By doing this, the effect of differing equipment and calibrations is normalised. Thereafter, the lung area is detected using Active Shape Models (ASMs). Once identified, the lungs are analysed using Local Binary Patterns (LBPs). This technique is combined with a probability measure that makes use of the the locations of known abnormalities in the training dataset. The results of the segmentation when compared to ground truth masks achieves an overlap segmentation accuracy of 87,598±3,986%. The challenges faced during classification are also discussed.
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The enhancement of long range imagery captured through a turbulent atmosphere

The use of formal language theory in computer vision

  • Authors: Van Niekerk, Graeme Neill
  • Date: 2014-11-20
  • Subjects: Computer vision
  • Type: Thesis
  • Identifier: uj:13088 , http://hdl.handle.net/10210/12966
  • Description: M.Sc. (Computer Science) , In this dissertation, a study of the field of computer vision as well as various fields relating to computer vision is made. An investigation of organic vision is made involving the study of the organic focusing device and visual cortex in humans. This is also done from a psychological point-of-view. Various network models emulating the neuronic networks as well as component networks of the human visual cortex are investigated. Recent work done in the area of neural networks and computer vision is also mentioned. The mathematical theory and techniques used in the area of image formation and image processing, is studied. The study of the field of artificial intelligence and its relation towards the computer vision problem, is made as well as a discussion of numerous application systems that have been developed. Existing industrial applications of computer vision are studied as well as the mentioning of systems that have been developed for this purpose. The use of parallel architectures and multiresolution systems for computer vision application, are investigated. Finally, a discussion of the formal language theory and automata is given in terms of its relevance to computer vision. The discussion centers around the the recognition of two and three-dimensional structures by various automata in the two dimensions. From this study, a formal model for the recognition of three-dimensional digital structures, is proposed and informally defined. It will be the aim of further study to fully develop and implement this model.
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VFFD : voting features for face detection

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