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-consuming and costly. In recent years, computer vision techniques have shown promise in bridge displacement measurement and damage detection. Aims The overall aim of this project is to develop a cost
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on leveraging advanced computer vision (CV) and deep learning (DL) techniques to develop a state-of-the-art computer-aided detection and diagnosis (CAD) system for early-stage Alzheimer's disease (AD) detection
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This scholarship is jointly funded by a leading computer vision company in Australia and the STEM College of RMIT University. The research includes investigation into automated product quality
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representation. While LMMs have demonstrated remarkable success in various domains such as natural language processing and computer vision, their application in the field of robotics remains relatively uncharted
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experienced supervisors, each with over 20 years of expertise in machine learning and computer vision. These supervisors have strong track records of research excellence, with numerous publications in top-tier
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the development of a machine learning and computer vision solution to predict the likelihood of ad-skipping behaviour. Both supervisors are EMCRs. The primary supervisor, Professor Billy Sung, has an extensive
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vision - research to meet the physical and mental health needs of people, and live personally meaningful and enriched lives. An internship may be available for this project. The supervisory team have
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The project will develop new deep learning methods to improve machine vision comprising several sensors in collaboration with industry partners 4AI Systems and 4Tel Pty Ltd. In this project we will
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members of the public, healthcare providers, other key stakeholders, and policy makers (from Objective 1) to ensure it fits their vision and is clear, concise, and compelling. Based on the feedback
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who desire a united voice in advocating for and supporting a collective vision for the future of the Hellenic business diaspora. It fosters connection, interaction, and exchanges of knowledge and ideas