3d Visualisation Sydney

[Ortiz Ullery]

TheMaster of Animation and Visualisation has been developed in partnership with the industry-leading digital animation studio Animal Logic and is offered through the UTS Animal Logic Academy. The course develops creative professional practice, conceptual skills and technical dexterity in animation and visualisation production. Under the guidance and mentorship of practitioners and leaders from the industry, learning is modelled on real-world production work structures in a custom-built digital production studio, engineered to the highest industry standards with tools and technologies that are leading the animation and visualisation industries into the future.

The course provides challenges and opportunities that encourage exploration and skills-building across the spectrum of roles in digital production, 3D animation, digital asset creation, visual effects and emerging visualisation disciplines. Collaborative work practices guide the development of strong competencies in critical thinking, problem solving, teamwork, and effective communication in a production environment. Graduates are able to work productively and effectively in a professional-style workplace.

Fees for future year(s) published in the online calculator, whilst unlikely to change, are estimates only. UTS makes every effort to provide up to date future year(s) fee estimates and to limit any changes, however, UTS reserves the right to vary fees for future year(s) at any time.

A small number of postgraduate courses offer government subsidised Commonwealth Supported Places (CSP). Find out whether there are CSPs available in this course by visiting our postgraduate fees page.

Tuition fees must be paid in advance each session and are subject to annual increase. Fees for future year(s) published in fees search, whilst unlikely to change, are estimates only. UTS makes every effort to provide up to date future year(s) fee estimates and to limit any changes, however, UTS reserves the right to vary fees for future year(s) at any time.

Graduates are able to enter industry with advanced knowledge, skills and collaborative large-project experience. In addition, they gain creative project development and presentation experience, and can contribute to the development of technology projects and industry sectors. They gain experience that can be applied across a range of roles, from story development, pre-visualisation, modelling, rigging, asset creation, 3D animation, visual effects and digital pipeline production, to creating immersive visualisation experiences and across emerging technologies such as virtual reality, augmented reality and real-time production.

Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.

Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.

Inherent requirements are academic and non-academic requirements that are essential to the successful completion of a course. For more information about inherent requirements and where prospective and current students can get assistance and advice regarding these, see the UTS Inherent requirements page.

UTS will make reasonable adjustments to teaching and learning, assessment, professional experiences, course related work experience and other course activities to facilitate maximum participation by students with disabilities, carer responsibilities, and religious or cultural obligations in their courses.

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.

Our research focuses on meeting healthcare challenges by developing core theories in information technologies and computer science research, including machine learning, computer vision, data science, artificial intelligence, bioinformatics, information visualisation and behavioural informatics. We work closely with several industry partners, including major hospitals and healthcare companies, conducting research from algorithms to prototypes, all the way to clinical trials and commercialisation.

Breakthroughs in these core theories and enabling techniques will be a major step forward, improving patient care and healthcare infrastructure such as multimedia patient record systems, advanced computer-assisted surgery and treatment, and telehealth for remote patient monitoring.

One in four people will be affected by cancer in their lifetime. Our research aims to produce cancer disease maps that extract and quantify important disease characteristics from a very large biomedical image data repository. The outcome will vastly improve personalised diagnosis and treatment of these cancers by providing new insights on how some cancers spread and resist our current treatments.

The next generation of medical imaging scanners are introducing new diagnostic capabilities that improve patient care. These medical images are multi-dimensional (3D), multi-modality (fusion of PET and MRI for example) and also time varying (that is, 3D volumes taken over multiple time points and functional MRI). Our research innovates in coupling volume rendering technologies with machine learning/image processing to render realistic and detailed 3D volumes of the human body.

Great advances in biological tissue labeling and automated microscopic imaging have revolutionised how biologists visualise molecular, sub-cellular, cellular and super-cellular structures and study their respective functions. How to interpret such image datasets in a quantitative and automatic way has become a major challenge in current computational biology. The essential methods of bioimage informatics involve generation, visualisation, analysis and management. This project aims to develop novel algorithms for content analysis in microscopic images, such as segmentation of cell nuclei, detection of certain cell structures and tracing of cell changes over time. Such algorithms would be valuable in turning image data into useful biological knowledge. These studies will focus on computer vision methodologies in feature extraction and learning-based modelling.

Neuroimaging technologies, such as MRI, have transformed how we study the brain under normal or pathological conditions. As imaging facilities become increasingly accessible, more and more imaging data are collected from patients with chronic disorders in longitudinal settings. Such big neuroimaging data enables new possibilities to study the brain with high translational impact, such as early detection of the longitudinal changes in the brain, and large-scale evaluation of imaging-based biomarkers. This project aims to develop novel computational methods to automatically detect the longitudinal changes in the brain based on large-scale longitudinal neuroimaging data, using machine-learning and deep-learning techniques.

Content-based medical image retrieval is a valuable mechanism to assist patient diagnosis. Different from text-based search engines, the similarity of images is evaluated based on a comparison between visual features. Consequently, how to best encode the complex visual features in a comparable mathematic form is crucial. Different from the image retrieval techniques proposed for general imaging in the medical domain, disease-specific contexts need to be modelled as the retrieval target. This project aims to study the various techniques of visual feature extraction and context modeling in medical imaging, and to develop new methodologies for content-based image retrieval of various medical applications.

Australia has one of the highest rates of melanoma in the world. Melanoma can be treated by simple lesion excision if diagnosed at an early stage. Sequential digital dermoscopy imaging is a technique that allows early detection; however, manual visual interpretation by physicians is subjective, with even well-trained physicians showing inter-observer variability. To overcome these limitations, we are investigating machine learning algorithms to develop a computer-aided diagnosis system to detect and track changes of the skin lesions.

Telehealth technologies enhance the delivery of healthcare through the introduction of powerful mechanisms such as social networking, notification, patient education/information portals, and patient monitoring, either remotely (by the care team) or by family and friends. Telehealth can be broadly applied to many diseases. As a case study, obesity in multiple family members is common and the importance of a family-based approach to weight management is well known. Our research aims to develop a family-focused application (app) with novel concepts to incentivise the whole family through family (social) networking, gamification, notifications, personalised analytics, goal setting and a reward mechanism. The app will be supported by a remote study nurse to encourage adherence. The proposed app features will need evaluation in regard to usefulness in helping to induce lifestyle change.

Recently the research community has seen great success using deep learning for image analysis tasks. For example, the Convolutional Neural Network (CNN) is one of the most widely used methods for object detection/recognition. This project will use a deep learning approach for predictive, prognostic prediction of patient treatment outcomes of malignant brain tumors. The multi-layer convolution of CNN will be utilised for detection and segmentation of tumors/lesions, and the project will include the investigation on the effective features for training and design of more feasible deep learning scheme for treatment outcomes prediction.

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