A systematic algorithm development for image processing feature extraction in automatic visual inspection : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in the Department of Production Technology, Massey University

Image processing techniques applied to modern quality control are described together with the development of feature extraction algorithms for automatic visual inspection. A real-time image processing hardware system already available in the Department of Production Technology is described and has been tested systematically for establishing an optimal threshold function. This systematic testing has been concerned with edge strength and system noise information. With the a priori information of system signal and noise, non-linear threshold functions have been established for real time edge detection. The performance of adaptive thresholding is described and the usefulness of this nonlinear approach is demonstrated from results using machined test samples. Examination and comparisons of thresholding techniques applied to several edge detection operators are presented. It is concluded that, the Roberts' operator with a non-linear thresholding function has the advantages of being simple, fast, accurate and cost effective in automatic visual inspection

The signatures of the new particles h2h_2 and ZμτZ_{\mu\tau} at e-p colliders in the U(1)Lμ−LτU(1)_{L_\mu-L_\tau} model

Considering the superior performances of the future e-p colliders, LHeC and FCC-eh, we discuss the feasibility of detecting the extra neutral scalar $h_{2}$ and the light gauge boson $Z^{}_{\mu\tau}$, which are predicted by the ${U(1)}_{L^{}_{\mu} - L^{}_{\tau}}$ model. Taking into account the experimental constraints on the relevant free parameters, we consider all possible production channels of $h_{2}$ and $Z^{}_{\mu\tau}$ at e-p colliders and further investigate their observability through the optimal channels in the case of the beam polarization P($e^{-}$)= -0.8. We find that the signal significance above 5$\sigma$ of $h_{2}$ as well as $Z^{}_{\mu\tau}$ detecting can be achieved via $e^{-}p\to{e^{-}jh_{2}(\to{Z_{\mu\tau}Z_{\mu\tau}})}\to~e^{-}j+/\!\!\!\!{E}^{}_{T}$ process and a 5$\sigma$ sensitivity of $Z^{}_{\mu\tau}$ detecting can be gained via $e^{-}p\to{e^{-}jh_{1}(\to{Z^{}_{\mu\tau}Z^{}_{\mu\tau}})\to}~e^{-}j+/\!\!\!\!{E}^{}_{T}$ process at e-p colliders with appropriate parameter values and a designed integrated luminosity. However, the signals of $h_{2}$ decays into pair of SM particles are difficult to be detected.Comment: 22 pages, 9 figures, references added and typos are correcte

Structured Attention for Image Analysis

Attention mechanism, an approach to maintain the local and global features over the input, is the crucial element of the Transformer. This dissertation explores structured attention for image analysis, proposing attention-based methods for multi-label learning and Alzheimer’s Disease (AD) diagnosis. For the multi-label learning task, I present two works under the Vision Transformer (ViT) framework. The first work focuses on supervised learning of multi-label classification. I address the problems of the multi-label classification and propose a model named AssocFormer, which adopts the association module to access the objects’ association relationship to improve the model performance. The second work addresses the semi-supervised learning of multi-label classification. I work on Single-Positive Multi-Label Learning (SPML), an extremely challenging task in which only one positive label is known with the rest annotations unknown. I present VLPL, a novel and efficient frame-work that leverages the similarity of the visual and text embeddings to get the pseudo-label of the given image. In the context of AD diagnosis, this study works on two tasks. The first task centers on efficient training using 3D brain images of AD. A novel module is proposed, which transforms 3D brain images into 2D fused images across the slice dimension. This conversion reduces input image dimensions, enhancing training efficiency. The second work combines different positron emission tomography (PET) modalities under the ViT Structure for AD diagnosis, namely ADViT. Throughout my work, a collection of novel methods rooted in the attention framework is proposed. The results demonstrate the significant enhancements of these methods in computer vision and medical imaging analysis