title: Image processing and machine learning for segmentation and classification of aluminium extrusions

authors: Pehrson, Michelle Josefina

abstract: Inventory management is one of the most important components of administration in a large-scale manufacturing business. Architectural Profile Limited produces tonnes of aluminium profiles every single day and have over one thousand different extrusions available. Many of the profiles look almost identical to each other and are very difficult for even a highly experienced employee to distinguish. Sending out an incorrect extrusion will cost time and money for both the manufacturer and the customer.

Automation using machine vision is already a prominent tool used in factory environments worldwide. The ability to automate repetitive tasks frees up time for higher skill tasks and reduces manual data entry errors. A real time inventory monitoring system would allow for quick and confident decision making and reduce order problems due to out of date stock numbers.

The aim of this project is to explore whether classification of extrusion profiles from images is possible and what factors ensure an accurate classification system. To achieve the aim, new data was acquired that captures a range of variation such as different angles, positions and lighting.

In this thesis two main paths were explored, segmentation followed by classification of binary images using generic Fourier descriptors, and direct classification from colour images. This thesis presents a qualitative and quantitative analysis of the different approaches, and discusses the challenges and limitations found with the data. The methods are compared and recommendations are highlighted regarding the next steps that aid creation of an automated aluminium extrusion inventory management system.

Presented is a pipeline of extracting features using techniques such as Gabor filters, Gaussian blur, and edge detectors. The output filters are used to train a random forest pixel classifier to segment the extrusions. Perfect reference binary images generated via a semi-manual threshold process are used to train a Logistic Regression classifier, and the test images were automatically segmented and classified. This classifier makes predictions on the segmented images, and the testing accuracy achieved was 88%. Instances that were misclassified had poor segmentations, in most cases from uneven illumination on the extrusion end. The other promising method is training convolutional neural networks using transfer learning. VGG-19 was trained with only the dataset acquired and achieved a testing accuracy of 89%, while ResNet-50 was trained using data augmentation and achieved a testing accuracy of 90%. The resulting confusion matrices show misclassifications between extrusions that are very similar in shape and size.

Further work will improve the performance of the classifiers by fine-tuning the parameters and carrying out parameter searching. Further investigation should be carried out given more requirements for the application, such as the processors and exact lighting conditions.

text: Image processing and machine learning for segmentation and classification of aluminium extrusions Inventory management is one of the most important components of administration in a large-scale manufacturing business. Architectural Profile Limited produces tonnes of aluminium profiles every single day and have over one thousand different extrusions available. Many of the profiles look almost identical to each other and are very difficult for even a highly experienced employee to distinguish. Sending out an incorrect extrusion will cost time and money for both the manufacturer and the customer.

Automation using machine vision is already a prominent tool used in factory environments worldwide. The ability to automate repetitive tasks frees up time for higher skill tasks and reduces manual data entry errors. A real time inventory monitoring system would allow for quick and confident decision making and reduce order problems due to out of date stock numbers.

The aim of this project is to explore whether classification of extrusion profiles from images is possible and what factors ensure an accurate classification system. To achieve the aim, new data was acquired that captures a range of variation such as different angles, positions and lighting.

In this thesis two main paths were explored, segmentation followed by classification of binary images using generic Fourier descriptors, and direct classification from colour images. This thesis presents a qualitative and quantitative analysis of the different approaches, and discusses the challenges and limitations found with the data. The methods are compared and recommendations are highlighted regarding the next steps that aid creation of an automated aluminium extrusion inventory management system.

Presented is a pipeline of extracting features using techniques such as Gabor filters, Gaussian blur, and edge detectors. The output filters are used to train a random forest pixel classifier to segment the extrusions. Perfect reference binary images generated via a semi-manual threshold process are used to train a Logistic Regression classifier, and the test images were automatically segmented and classified. This classifier makes predictions on the segmented images, and the testing accuracy achieved was 88%. Instances that were misclassified had poor segmentations, in most cases from uneven illumination on the extrusion end. The other promising method is training convolutional neural networks using transfer learning. VGG-19 was trained with only the dataset acquired and achieved a testing accuracy of 89%, while ResNet-50 was trained using data augmentation and achieved a testing accuracy of 90%. The resulting confusion matrices show misclassifications between extrusions that are very similar in shape and size.

Further work will improve the performance of the classifiers by fine-tuning the parameters and carrying out parameter searching. Further investigation should be carried out given more requirements for the application, such as the processors and exact lighting conditions.

year: 2022

title: A qualitative analysis of parent's reminiscing with their child with autism: Motivations, enablers and barriers

authors: Wilson, Bryony

subjects: reminiscing

abstract: Parent-child reminiscing is a critical learning process associated with many cognitive developmental outcomes, including language acquisition, memory development and socio-emotional functioning (see Waters et al., 2019 for review). Many of these developmental outcomes are compromised for autistic children. However, only a few published studies have examined parent-child reminiscing for neurodiverse families (Faust, 2009; McDonnell et al., 2021; Raman, 2022). Moreover, none have qualitatively explored parents’ experience of and goals for reminiscing. Thus, we know little about how reminiscing might differ for children with autism or their parents. This study aimed to explore parents’ experiences of reminiscing with their child with autism, including their goals, motivations, and barriers and enablers to their participation in a three-session reminiscing intervention. Using a qualitative approach, we conducted semi-structured interviews with five parent-child dyads before and after a three-session reminiscing intervention. All children had a prior diagnosis of autism spectrum disorder (ASD). We used reflective thematic analysis (RTA) (Braun & Clarke, 2006, 2020) to explore and generate themes from pre- and post-
intervention interviews related to motivations, goals, challenges and benefits of reminiscing with a child on the autism spectrum. Findings highlight the significant influence of emotional states on conversation engagement, the challenges autistic children face in understanding conversational structure and narrative retelling, and the unique motivations parents have for reminiscing, including reminiscing as a tool for emotional regulation, preparing for and understanding social situations, and fostering a positive relationship. Participation in the intervention provided insights into the benefits and challenges of reminiscing, with parents emphasising the need for individualised support and flexibility. Our findings contribute to a small but growing body of research on parent-child reminiscing in autism, offering practical implications for future interventions. Our approach highlights the importance of aligning intervention goals with parents’ needs to enhance their effectiveness. Future research should explore parents’ motivations across larger, more diverse samples to inform autism-specific reminiscing interventions further.

text: A qualitative analysis of parent's reminiscing with their child with autism: Motivations, enablers and barriers Parent-child reminiscing is a critical learning process associated with many cognitive developmental outcomes, including language acquisition, memory development and socio-emotional functioning (see Waters et al., 2019 for review). Many of these developmental outcomes are compromised for autistic children. However, only a few published studies have examined parent-child reminiscing for neurodiverse families (Faust, 2009; McDonnell et al., 2021; Raman, 2022). Moreover, none have qualitatively explored parents’ experience of and goals for reminiscing. Thus, we know little about how reminiscing might differ for children with autism or their parents. This study aimed to explore parents’ experiences of reminiscing with their child with autism, including their goals, motivations, and barriers and enablers to their participation in a three-session reminiscing intervention. Using a qualitative approach, we conducted semi-structured interviews with five parent-child dyads before and after a three-session reminiscing intervention. All children had a prior diagnosis of autism spectrum disorder (ASD). We used reflective thematic analysis (RTA) (Braun & Clarke, 2006, 2020) to explore and generate themes from pre- and post-
intervention interviews related to motivations, goals, challenges and benefits of reminiscing with a child on the autism spectrum. Findings highlight the significant influence of emotional states on conversation engagement, the challenges autistic children face in understanding conversational structure and narrative retelling, and the unique motivations parents have for reminiscing, including reminiscing as a tool for emotional regulation, preparing for and understanding social situations, and fostering a positive relationship. Participation in the intervention provided insights into the benefits and challenges of reminiscing, with parents emphasising the need for individualised support and flexibility. Our findings contribute to a small but growing body of research on parent-child reminiscing in autism, offering practical implications for future interventions. Our approach highlights the importance of aligning intervention goals with parents’ needs to enhance their effectiveness. Future research should explore parents’ motivations across larger, more diverse samples to inform autism-specific reminiscing interventions further.

year: 2025

title: Ohaaki: A power station on Maori land

authors: Stokes, Evelyn

subjects: Ohaaki Geothermal Power Station

abstract: This paper is about a geothermal power station called Ohaaki, constructed over the period 1982-1989 on Maori land close by Te Ohaaki Marae and the ancestral papakainga of Ngati Tahu. This is a personal account of the impact of this project on Ngati Tahu rather than a history of the construction of the power station.

text: Ohaaki: A power station on Maori land This paper is about a geothermal power station called Ohaaki, constructed over the period 1982-1989 on Maori land close by Te Ohaaki Marae and the ancestral papakainga of Ngati Tahu. This is a personal account of the impact of this project on Ngati Tahu rather than a history of the construction of the power station.

year: 2003