Tony Pridmore

UG/MSc Projects

The Leaf Segmentation Challenge/Leaf Counting Challenge

The image processing and computer vision literature contains many segmentation algorithms, each aiming to break an input image into regions corresponding to individual objects, or parts of objects. Climate change and a growing population have recently put huge demands on the world’s food supply, creating a pressing need for methods that can measure various properties of plants - the idea being that if your goal is to create new plant varieties that produce more food you need to be able to assess them in various ways. Measurements of leaves are very important, and so a number of leaf segmentation methods have been proposed. To help this, the Leaf Segmentation Challenge ( was recently set up. This provides a large set of plant images, with manual segmentations giving the idea result. The goal of this project is to attempt the leaf segmentation challenge - to develop an image segmentation tool that can identify leaves in the challenge images. An alternative project, based on the same data, would be to count the leaves, which might be done without a complete segmentation.

plant001_rgb plant001_label

Painterly Rendering

Much computer graphics work is aimed at producing photo-realistic images of artificial objects. Painterly rendering takes a different approach and aims to produce images which appear to be have been painted, by a particular artist, or in a particular artistic style. This is done by taking an initial, real image (or set of images) and manipulating it to produce a new image that appears painted. This project involves selecting an artist or style that interests you, and developing a software tool capable of rendering input images in that style.

Original HuangShan1 mtn-exp Expressionist

Root Phenomics Hub

Phenotyping, or phenomics, is the science of determining the structure and function of living things. At Nottingham, a significant body of work has been done on the development of methods for phenotyping plant roots, usually through the analysis of various types of image, including laser microscopes, standard colour images, and X-ray tomography. A number of root measurement tools have been developed and are in wide use in the biological community. Deployment of these tools has now generated a sizeable and rich body of data, which is growing quickly. The goal of this project is to design and develop a web portal which will allow users to store, browse, access and compare the various datasets available. The long-term here goal is to create a resource for root biologists along the lines of the Ionomics Hub ( or MorphoSource (

image_2 Untitled art-rsml

3D Shape from Image Pairs

Binocular stereo - the recovery of 3D models from pairs of images - is a longstanding problem in computer vision. The Middlebury Stereo site ( provides several sets of image pairs, along with ground truth data showing what the true 3D structure of the scene is. This is a valuable resource for those attempting to develop new stereo methods. The goal of this project is to atempt the Middlebury challenge - to develop software capable of extracting some 3D measurements from a stereo image pair, and compare it to the ground truth.

Input image im1 disp0 Ground truth depth map

Tracking Mitochondria in Image Sequences of Neuronal Cells

Mitochondria are sub-cellular components which move around inside neuronal and other cells. Disruption of mitochondrial function is associated with a number of serious degenerative illnesses, and understanding of the factors which influence mitochondrial motion is key to understanding those diseases. Mitochondria appear as small bright dots in confocal laser microscope images, this project will seek to develop tracking methods which can estimate the movement of mitochondria, by tracking the movement of the corresponding dots through image sequences.


Online Learning to Identify Cell Types

This project will develop machine learning methods capable of recognising different types of cell in laser microsope images. Members of the Computer Vision lab have developed an image analysis tool, CellSet ( which recovers descriptions of cell networks from images. It includes a manual annotation phase in which the user can annotate the results with cell type and other information. Ideally, this should be automated. The goal of this project is to add an online learning module to CellSet, so that as the user annotates cells the tool learns how to make those annotations automatically, and the annotation process becomes collaborative, rather than manual.


Recovering the Structure of Rice Shoots

Biologists at Nottingham are working on ways to increase the amount of food provided by rice plants, as climate change makes growing rice more challenging in many parts of the world. To do this they need to understand what affects the physical structure of the rice plant. To do that they need a way to generate data on plant structure from images. The goal of this project is to develop image processing and analysis software that can process and analyse images like that below to recover a description of the branching structure of the plant, and/or count the ears of rice.