Here are my project suggestions for Part II or Diploma students in the academic year 2008/2009. Some of the information on last year's suggestions may also be relevant. I have supervised about 25 Part II and Diploma projects in recent years, most of which received 1st class marks, with several being singled out for special commendation by the examiners. I also recently co-authored five academic papers together with former project students of mine. In short, my project suggestions are likely to be challenging but I am fully committed to putting in a lot of effort to provide the best support I can to make sure the project is completed successfully. Who knows, you might end up having a lot of fun too!
The platform of choice for implementation of most projects is Matlab, which is available in most Colleges and in the CL. Matlab has excellent facilities for numerical computation and visualisation, and there are many useful toolboxes (e.g. for image processing, statistics, optimisation, neural networks). For reasons of runtime efficiency, it might however be appropriate to implement part of the required functionality in a lower level compiled language such as Java or C++ and integrate such modules into Matlab by means of the Matlab compiler package. There are various free computer vision packages available which use or support C/C++ such as OpenCV, VXL, and Lush.
As regards general references, I particularly recommend the textbook by Forsyth and Ponce on computer vision. References on image processing, such as the book by Gonzales and Woods, and on numerical methods, such as Numerical Recipes in C++, might also be handy. Useful online resources for computer vision include CVonline and the Computer Vision Homepage at CMU. The best (free) online tools for finding papers etc. are Google, Google Scholar, and Citeseer. There are many online tutorials for Matlab, this is a local one at CUED. Eventually you might want to use Tex/LaTex to produce your dissertation, here is a basic introduction and further information can be found here and here.
Some of the following descriptions are still a bit brief and open to interpretation, watch this space or (better) contact me to find out more. I may add or change project suggestions as the various deadlines approach.
Content based image retrieval (CBIR) lies at the intersection of information retrieval and computer vision. It is concerned with the process of analysing the content of images in order to facilitate retrieval of related images based on overall similarity or the existence of particular objects or characteristics within images.
This project will focus on defining and evaluating measures of genral image similarity. The project will be implemented in Java taking advantage of the LIRE framework which is based on the Lucene search engine library, both of which are open source.
As an extension, the project will consider the application of relevance feedback techniques where human interaction is introduced to the retrieval process. Another possible extension would be to compare the system to tagging information gathered from a resource such as flickr.
Most object recognition methods are either appearance based or model based. However, some recent work has shown that simpler features such as line and outline based models can be more efficient in practice.
This project will implement a generic object detection module based on the work of Jamie Shotton at the Engineering Department. This will entail development of a fast tree based fitting procedure for finding generic objects from line and region boundary information in images. The code and datasets used for Jamie's PhD work are available here and I have various other datasets that can be used to assess and optimise the performance of the methods generated by the project. Also look here for an overview of object detection and recognition methods.