A Practical Use for AI-Generated Images
- Boby, Alden, Brown, Dane L, Connan, James
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463345 , vital:76401 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-43838-7_12"
- Description: Collecting data for research can be costly and time-consuming, and available methods to speed up the process are limited. This research paper compares real data and AI-generated images for training an object detection model. The study aimed to assess how the utilisation of AI-generated images influences the performance of an object detection model. The study used a popular object detection model, YOLO, and trained it on a dataset with real car images as well as a synthetic dataset generated with a state-of-the-art diffusion model. The results showed that while the model trained on real data performed better on real-world images, the model trained on AI-generated images, in some cases, showed improved performance on certain images and was good enough to function as a licence plate detector on its own. The study highlights the potential of using AI-generated images for data augmentation in object detection models and sheds light on the trade-off between real and synthetic data in the training process. The findings of this study can inform future research in object detection and help practitioners make informed decisions when choosing between real and synthetic data for training object detection models.
- Full Text:
- Date Issued: 2023
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463345 , vital:76401 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-43838-7_12"
- Description: Collecting data for research can be costly and time-consuming, and available methods to speed up the process are limited. This research paper compares real data and AI-generated images for training an object detection model. The study aimed to assess how the utilisation of AI-generated images influences the performance of an object detection model. The study used a popular object detection model, YOLO, and trained it on a dataset with real car images as well as a synthetic dataset generated with a state-of-the-art diffusion model. The results showed that while the model trained on real data performed better on real-world images, the model trained on AI-generated images, in some cases, showed improved performance on certain images and was good enough to function as a licence plate detector on its own. The study highlights the potential of using AI-generated images for data augmentation in object detection models and sheds light on the trade-off between real and synthetic data in the training process. The findings of this study can inform future research in object detection and help practitioners make informed decisions when choosing between real and synthetic data for training object detection models.
- Full Text:
- Date Issued: 2023
Enabling Vehicle Search Through Robust Licence Plate Detection
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc, Kuhlane, Luxolo L
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc , Kuhlane, Luxolo L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463372 , vital:76403 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220508"
- Description: Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.
- Full Text:
- Date Issued: 2023
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc , Kuhlane, Luxolo L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463372 , vital:76403 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220508"
- Description: Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.
- Full Text:
- Date Issued: 2023
Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463478 , vital:76412 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220534"
- Description: Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.
- Full Text:
- Date Issued: 2023
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463478 , vital:76412 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220534"
- Description: Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.
- Full Text:
- Date Issued: 2023
An evaluation of hand-based algorithms for sign language recognition
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465124 , vital:76575 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856310"
- Description: Sign language recognition is an evolving research field in computer vision, assisting communication between hearing disabled people. Hand gestures contain the majority of the information when signing. Focusing on feature extraction methods to obtain the information stored in hand data in sign language recognition may improve classification accuracy. Pose estimation is a popular method for extracting body and hand landmarks. We implement and compare different feature extraction and segmentation algorithms, focusing on the hands only on the LSA64 dataset. To extract hand landmark coordinates, MediaPipe Holistic is implemented on the sign images. Classification is performed using poplar CNN architectures, namely ResNet and a Pruned VGG network. A separate 1D-CNN is utilised to classify hand landmark coordinates extracted using MediaPipe. The best performance was achieved on the unprocessed raw images using a Pruned VGG network with an accuracy of 95.50%. However, the more computationally efficient model using the hand landmark data and 1D-CNN for classification achieved an accuracy of 94.91%.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465124 , vital:76575 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856310"
- Description: Sign language recognition is an evolving research field in computer vision, assisting communication between hearing disabled people. Hand gestures contain the majority of the information when signing. Focusing on feature extraction methods to obtain the information stored in hand data in sign language recognition may improve classification accuracy. Pose estimation is a popular method for extracting body and hand landmarks. We implement and compare different feature extraction and segmentation algorithms, focusing on the hands only on the LSA64 dataset. To extract hand landmark coordinates, MediaPipe Holistic is implemented on the sign images. Classification is performed using poplar CNN architectures, namely ResNet and a Pruned VGG network. A separate 1D-CNN is utilised to classify hand landmark coordinates extracted using MediaPipe. The best performance was achieved on the unprocessed raw images using a Pruned VGG network with an accuracy of 95.50%. However, the more computationally efficient model using the hand landmark data and 1D-CNN for classification achieved an accuracy of 94.91%.
- Full Text:
- Date Issued: 2022
Deep Learning Approach to Image Deblurring and Image Super-Resolution using DeblurGAN and SRGAN
- Kuhlane, Luxolo L, Brown, Dane L, Connan, James, Boby, Alden, Marais, Marc
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
Exploring the Incremental Improvements of YOLOv7 over YOLOv5 for Character Recognition
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- Full Text:
- Date Issued: 2022
Improving signer-independence using pose estimation and transfer learning for sign language recognition
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463406 , vital:76406 , xlink:href="https://doi.org/10.1007/978-3-031-35644-5"
- Description: Automated Sign Language Recognition (SLR) aims to bridge the com-munication gap between the hearing and the hearing disabled. Com-puter vision and deep learning lie at the forefront in working toward these systems. Most SLR research focuses on signer-dependent SLR and fails to account for variations in varying signers who gesticulate naturally. This paper investigates signer-independent SLR on the LSA64 dataset, focusing on different feature extraction approaches. Two approaches are proposed an InceptionV3-GRU architecture, which uses raw images as input, and a pose estimation LSTM architecture. MediaPipe Holistic is implemented to extract pose estimation landmark coordinates. A final third model applies augmentation and transfer learning using the pose estimation LSTM model. The research found that the pose estimation LSTM approach achieved the best perfor-mance with an accuracy of 80.22%. MediaPipe Holistic struggled with the augmentations introduced in the final experiment. Thus, looking into introducing more subtle augmentations may improve the model. Over-all, the system shows significant promise toward addressing the real-world signer-independence issue in SLR.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463406 , vital:76406 , xlink:href="https://doi.org/10.1007/978-3-031-35644-5"
- Description: Automated Sign Language Recognition (SLR) aims to bridge the com-munication gap between the hearing and the hearing disabled. Com-puter vision and deep learning lie at the forefront in working toward these systems. Most SLR research focuses on signer-dependent SLR and fails to account for variations in varying signers who gesticulate naturally. This paper investigates signer-independent SLR on the LSA64 dataset, focusing on different feature extraction approaches. Two approaches are proposed an InceptionV3-GRU architecture, which uses raw images as input, and a pose estimation LSTM architecture. MediaPipe Holistic is implemented to extract pose estimation landmark coordinates. A final third model applies augmentation and transfer learning using the pose estimation LSTM model. The research found that the pose estimation LSTM approach achieved the best perfor-mance with an accuracy of 80.22%. MediaPipe Holistic struggled with the augmentations introduced in the final experiment. Thus, looking into introducing more subtle augmentations may improve the model. Over-all, the system shows significant promise toward addressing the real-world signer-independence issue in SLR.
- Full Text:
- Date Issued: 2022
Investigating signer-independent sign language recognition on the lsa64 dataset
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden, Kuhlane, Luxolo L
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
Investigating the Effects of Image Correction Through Affine Transformations on Licence Plate Recognition
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465190 , vital:76581 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856380"
- Description: Licence plate recognition has many real-world applications, which fall under security and surveillance. Deep learning for licence plate recognition has been adopted to improve existing image-based processing techniques in recent years. Object detectors are a popular choice for approaching this task. All object detectors are some form of a convolutional neural network. The You Only Look Once framework and Region-Based Convolutional Neural Networks are popular models within this field. A novel architecture called the Warped Planar Object Detector is a recent development by Zou et al. that takes inspiration from YOLO and Spatial Network Transformers. This paper aims to compare the performance of the Warped Planar Object Detector and YOLO on licence plate recognition by training both models with the same data and then directing their output to an Enhanced Super-Resolution Generative Adversarial Network to upscale the output image, then lastly using an Optical Character Recognition engine to classify characters detected from the images.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465190 , vital:76581 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856380"
- Description: Licence plate recognition has many real-world applications, which fall under security and surveillance. Deep learning for licence plate recognition has been adopted to improve existing image-based processing techniques in recent years. Object detectors are a popular choice for approaching this task. All object detectors are some form of a convolutional neural network. The You Only Look Once framework and Region-Based Convolutional Neural Networks are popular models within this field. A novel architecture called the Warped Planar Object Detector is a recent development by Zou et al. that takes inspiration from YOLO and Spatial Network Transformers. This paper aims to compare the performance of the Warped Planar Object Detector and YOLO on licence plate recognition by training both models with the same data and then directing their output to an Enhanced Super-Resolution Generative Adversarial Network to upscale the output image, then lastly using an Optical Character Recognition engine to classify characters detected from the images.
- Full Text:
- Date Issued: 2022
Iterative Refinement Versus Generative Adversarial Networks for Super-Resolution Towards Licence Plate Detection
- Boby, Alden, Brown, Dane L, Connan, James
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463417 , vital:76407 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_26"
- Description: Licence plate detection in unconstrained scenarios can be difficult because of the medium used to capture the data. Such data is not captured at very high resolution for practical reasons. Super-resolution can be used to improve the resolution of an image with fidelity beyond that of non-machine learning-based image upscaling algorithms such as bilinear or bicubic upscaling. Technological advances have introduced more than one way to perform super-resolution, with the best results coming from generative adversarial networks and iterative refinement with diffusion-based models. This paper puts the two best-performing super-resolution models against each other to see which is best for licence plate super-resolution. Quantitative results favour the generative adversarial network, while qualitative results lean towards the iterative refinement model.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463417 , vital:76407 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_26"
- Description: Licence plate detection in unconstrained scenarios can be difficult because of the medium used to capture the data. Such data is not captured at very high resolution for practical reasons. Super-resolution can be used to improve the resolution of an image with fidelity beyond that of non-machine learning-based image upscaling algorithms such as bilinear or bicubic upscaling. Technological advances have introduced more than one way to perform super-resolution, with the best results coming from generative adversarial networks and iterative refinement with diffusion-based models. This paper puts the two best-performing super-resolution models against each other to see which is best for licence plate super-resolution. Quantitative results favour the generative adversarial network, while qualitative results lean towards the iterative refinement model.
- Full Text:
- Date Issued: 2022
A Robust Portable Environment for First-Year Computer Science Students
- Brown, Dane L, Connan, James
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
Early dehydration detection using infrared imaging
- Poole, Louise C, Brown, Dane L, Connan, James
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465656 , vital:76629 , xlink:href="https://www.researchgate.net/profile/Louise-Poole-3/publication/357578445_Early_Dehydration_Detection_Using_Infrared_Imaging/links/61d5664eb8305f7c4b231d50/Early-Dehydration-Detection-Using-Infrared-Imaging.pdf"
- Description: Crop loss and failure have devastating impacts on a country’s economy and food security. Developing effective and inexpensive systems to minimize crop loss has become essential. Recently, multispectral imaging—in particular visible and infrared imaging—have become popular for analyzing plants and show potential for early identification of plant stress. We created a directly comparable visible and infrared image dataset for dehydration in spinach leaves. We created and compared various models trained on both datasets and concluded that the models trained on the infrared dataset outperformed all of those trained on the visible dataset. In particular, the models trained to identify early signs of dehydration yielded 45% difference in accuracy, with the infrared model obtaining 70% accuracy and the visible model obtaining 25% accuracy. Infrared imaging thus shows promising potential for application in early plant stress and disease identification.
- Full Text:
- Date Issued: 2021
Using Technology to Teach a New Generation
- Connan, James, Brown, Dane L, Watkins, Caroline
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
JSON schema for attribute-based access control for network resource security
- Linklater, Gregory, Smith, Christian, Connan, James, Herbert, Alan, Irwin, Barry V W
- Authors: Linklater, Gregory , Smith, Christian , Connan, James , Herbert, Alan , Irwin, Barry V W
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/428368 , vital:72506 , https://researchspace.csir.co.za/dspace/bitstream/handle/10204/9820/Linklater_19660_2017.pdf?sequence=1andisAllowed=y
- Description: Attribute-based Access Control (ABAC) is an access control model where authorization for an action on a resource is determined by evalu-ating attributes of the subject, resource (object) and environment. The attributes are evaluated against boolean rules of varying complexity. ABAC rule languages are often based on serializable object modeling and schema languages as in the case of XACML which is based on XML Schema. XACML is a standard by OASIS, and is the current de facto standard for ABAC. While a JSON profile for XACML exists, it is simply a compatibility layer for using JSON in XACML which caters to the XML object model paradigm, as opposed to the JSON object model paradigm. This research proposes JSON Schema as a modeling lan-guage that caters to the JSON object model paradigm on which to base an ABAC rule language. It continues to demonstrate its viability for the task by comparison against the features provided to XACML by XML Schema.
- Full Text:
- Date Issued: 2017
- Authors: Linklater, Gregory , Smith, Christian , Connan, James , Herbert, Alan , Irwin, Barry V W
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/428368 , vital:72506 , https://researchspace.csir.co.za/dspace/bitstream/handle/10204/9820/Linklater_19660_2017.pdf?sequence=1andisAllowed=y
- Description: Attribute-based Access Control (ABAC) is an access control model where authorization for an action on a resource is determined by evalu-ating attributes of the subject, resource (object) and environment. The attributes are evaluated against boolean rules of varying complexity. ABAC rule languages are often based on serializable object modeling and schema languages as in the case of XACML which is based on XML Schema. XACML is a standard by OASIS, and is the current de facto standard for ABAC. While a JSON profile for XACML exists, it is simply a compatibility layer for using JSON in XACML which caters to the XML object model paradigm, as opposed to the JSON object model paradigm. This research proposes JSON Schema as a modeling lan-guage that caters to the JSON object model paradigm on which to base an ABAC rule language. It continues to demonstrate its viability for the task by comparison against the features provided to XACML by XML Schema.
- Full Text:
- Date Issued: 2017
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