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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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The detection of human activities is crucial for effective monitoring purposes. The challenge lies in accurately and promptly identifying various types of activities from videos and images captured
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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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Roentgen’s Nobel Prize-winning discovery of X-rays enabled us to non-destructively image inside the body, birthing medical diagnostic imaging and revolutionising materials characterisation
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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Conventional x-ray imaging is firmly established as an invaluable tool in medicine, security, research and manufacturing. However, conventional methods extract only a fraction of the sample
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find