PamiTC Job Board - Posting Details

Title: Phd studentship- computer vision & machine learningPosted: February 23, 2016
Company/Institution: Northumbria University
Location: Newcastle, UK
Department: Computer Science

Description: Multi-Modal Object Recognition and Scene Understanding Employing Computer Vision and Machine Learning Techniques (RDF16/CSDT/HAN) ----------------------------------------------------------- Project Description: Recent advances in imaging, networking, data processing and storage technology have resulted in an explosion in the use of multi-modal image/video in a variety of fields, including video surveillance, urban monitoring, cultural heritage area protection and many others. The integration of videos/images from multiple channels can provide complementary information and therefore increase the accuracy of the overall decision making process. Currently, seeking an efficient way to analyse, mine and understand such large-scale, multimodal and noisy data is a challenging and interesting research topic, where the core problem is learning representations from the data. The problem of learning representations from the data has received considerable attention in machine learning. Deep learning approaches in particular have achieved close to human accuracy at recognition tasks in limited domains (e.g. ImageNet image recognition). However, these approaches usually require vast, high-quality datasets in the training phase in order to obtain a good performance, which makes their use expensive and limits to domains where gathering large numbers of training examples and correct labels are feasible. For the real-life computer vision applications such as video surveillance and ambient assisted living, using such approaches seems impractical. In contrast to deep neural networks, humans can learn concepts from very few examples, and can generalize them effortlessly across domains (unlike deep learning). Even 2-year olds can learn new words and generalize them to new situations after seeing only a few examples. The abilities of humans including integration information acquired through different modalities, reasoning, planning, and problem solving are highly challenging for current artificial intelligence models. In this PhD work, we will formalize the ideas of representational geometry/conceptual spaces in a tractable, deep probabilistic framework, and use it to develop a new type of bio-inspired models capable of several novel aspects of human-like learning, including learning 1) from few examples, 2) from multiple modalities (visual, spatiotemporal, and text data), 3) grounding concept representations in perceptions and action possibilities. The targeted applications include video surveillance, robot vision and smart environment for elderly assisted living. The successful candidate is expected to work in an international research group with 10+ Ph.D. students and Post-doc researchers and will be jointly supervised by Dr. Jungong Han (https://sites.google.com/site/jungonghan77/) and Prof. Ling Shao (Head of computer vision and artificial intelligence group: http://lshao.staff.shef.ac.uk/) -------------------------------------------------------- Eligibility requirement: * Academic excellence of the proposed student i.e. normally an Honours Degree: 1st or 2:1 (or equivalent) or possession of a Masters degree, with merit (or equivalent study at postgraduate level). * We expect experience in computer vision, video analysis, and machine learning as well as good mathematical and programming skills (either C/C++ or MATLAB). * Appropriate IELTS score (overall score of 6.5 with no component below 6.0), if required (evidence required by 1 August). ---------------------------------------------- For further details of how to apply, entry requirements and the application form, see https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/ Please ensure you quote the advert reference above on your application form. ------------------------------------------------ Deadline for applications: 18 March 2016 Interview date/s (if known): to be confirmed Start Date: 1 Oct 2016 Informal Enquiries: Enquiries regarding this studentship should be made to pgr.admissions@northumbria.ac.uk . Further information about the content can be obtained from Dr. Jungong Han (jungong.han@northumbria.ac.uk) or Prof. Ling Shao (ling.shao@northumbria.ac.uk) ------------------------------------------------- Funding Notes The studentship includes a full stipend, paid for three years at RCUK rates (in 2016/17 this is £14,296 pa) and fees (Home/EU £4,350 / International £13,000).

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