Browsing by Author "Chang, Yuan"

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Explainable spectral super-resolution based on a single RGB image : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
(Massey University, 2024-04-09) Chang, Yuan
Hyperspectral imaging offers fine spectral measurements of target surfaces, finding utility in various fields. However, traditional hyperspectral systems grapple with high-cost issues. On the other hand, conventional RGB cameras, which provide relatively coarse measurements of surface spectra, are widely accessible. Consequently, the recovery of spectral information from RGB images has emerged as a popular approach for low-cost hyperspectral imaging, a venture also known as single-image spectral super-resolution. Yet, existing methods, mostly rooted in deep convolutional neural networks, tend to suffer from limited interpretability. In our research, we propose an explainable method for single-image spectral super-resolution. This method relies on the RGBPQR colour space, a low-dimensional spectral data model representing the spectrum. Leveraging the RGBPQR spectral model, we can transform the spectral reconstruction task into a regression problem. To tackle the metamerism issue, we analysed existing spectral super-resolution networks and discovered that these networks often depend on local textural information as context to mitigate metamerism. Informed by this insight, we utilized features extracted from multiscale local binary patterns as contextual information to design our explainable method. Furthermore, in this study, we discussed the error measurements and loss functions employed in this research area and proposed a new error measurement that can represent performance more accurately. We also endeavoured to put forward a method for quantitatively measuring the ability to resolve metamerism, a critical problem in spectral super-resolution. Through our research, we offered a simple, low-dimensional, and explainable spectral super-resolution solution.
Exploring consumer justification of overconsumption in live streaming e-commerce in China : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Marketing at Massey University, Albany, New Zealand
(Massey University, 2025) Chang, Yuan
The aim of this research is to explores how Chinese consumers perceive sustainable consumption and how these perceptions are shaped and enacted within livestream e-commerce environments. While sustainable consumption has gained increasing scholarly attention, most existing research adopts quantitative approaches and focuses on Western contexts, leaving a gap in understanding how sustainability is subjectively constructed and behaviorally rationalized in China’s rapidly evolving digital marketplaces. Drawing on 18 semi-structured interviews and guided by a constructivist epistemology, this study employs thematic analysis to uncover the layered understandings, tensions, and justifications surrounding sustainability in the context of livestream shopping. The findings from this research reveal that consumers often equate sustainability with product durability, frugality, and personal responsibility, but also experience internal conflicts when navigating entertainment-driven shopping platforms. The study highlights how platform features—such as interactivity, persuasive streamer tactics, and ease of returns—both complicate and facilitate sustainable decision-making. Furthermore, consumers adopt various rationalization strategies, including neutralization techniques, to justify overconsumption. This research contributes to sustainability literature by contextualizing consumer behavior in emerging digital economies and expanding the application of ethical consumption theories to livestream retail. Practical implications are offered for platform designers, marketers, and policy makers seeking to promote sustainability in e-commerce.
Lens distortion correction by analysing the shape of patterns in Hough transform space : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
(Massey University, 2018) Chang, Yuan
Many low cost, wide angle lenses suffer from lens distortion, resulting from a radial variation in the lens magnification. As a result, straight lines, particularly those in the periphery, appear curved. The Hough transform is a commonly used linear feature detection technique within an image. In Hough transform space, straight lines and curved lines have different shapes of peaks. This thesis proposes a lens distortion correction method named SLDC based on analysing the shape of patterns in the Hough transform space. It works by reconstructing the distorted line from significant points on the smile-shaped Hough pattern. It then optimises the distortion parameter by mapping the reconstructed curved line into a straight line and minimising the RMSE. From both simulation and correcting real world images, the SLDC provides encouraging results.