Student Projects

A vacation project at the PBRC is a great way to get research experience and learn valuable new skills. Below is a selection of example vacation projects. Different projects are available, and looking at our research page and individual staff home pages will give an idea on the breadth of topics available. For more information please contact Professor Stan Miklavcic or the PBRC staff member that you are interested in undertaking a project with.

Automatic Alignment of Mass Spectrometer Signals

The science of Metabolomics aims to measure the presence and flux of small molecules known as metabolites within a living system. Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) technique produces detailed information about the spatial distribution of metabolites in a given biological sample by using only a small section of a tissue. Mass Spectrometry devices produce high volume of metabolomics data. However, these data are often corrupted with time-warping device-dependent noise. As a result, signals measured on the same tissue but by different devices are often misaligned. This makes direct comparison and analysis of signals produced by different devices not feasible.

The purpose of this project is to develop and implement methods for automatic alignement of signals produced by MALDI-IMS.  This is the first step and a building block for high-level data analysis, which will enable deeper understanding of the distribution of the important metabolites in living organisms.

Requirements

  • •          Ability to program in Matlab
  • •          Familiarity with signal processing
  • •          Motivation to learn and explore new concepts

Supervisors

Dr Pankaj Kumar, Professor Stan Miklavcic, Dr Hamid Laga

Automatic Segmentation of 3D shapes

The purpose of this project is to implement and test a new method for the segmentation of 3D shapes that have tubular structure. Examples of such shapes are roots and shoots of plants. 3D segmentation is an important step towards the analysis of the plant structure. Several 3D segmentation methods have been already developed by the hosting research group but intended for various types of 3D shapes. The role of the student is to extend these methods to 3D plants (root and shoot) and evaluate their performance.

The student will have the unique opportunity to work within a multi-disciplinary research centre composed of computer scientists (working on image processing and computer vision), mathematicians and plant biologists.

 Requirements

  • •          Ability to program in Matlab
  • •          Familiarity with 3D geometry
  • •          Motivation to learn and explore new concepts

Supervisors

Dr Pankaj Kumar, Dr Jinhai Cai

References

1. Hamid Laga, Michela Mortara and Michela Spagnuolo. “Geometry and Context for Semantic Correspondences and Functionality Recognition in Manmade 3D Shapes. ACM Transactions on Graphics, 32(5), 2013.

2. Pankaj Kumar, Jinhai Cai, and Stanley J. Miklavcic. Improved Ellipse Fitting by Considering the Eccentricity of Data Point Sets. In IEEE International Conference on Image Processing (ICIP) 2013.

Automatic Extraction of the Vein Structure of Plant Leaves from Images

The 2D shape of a plant leaf, its margin and its vein structure are of great importance to plant scientists as it can help in distinguishing species, measuring plant health, analysing growth patterns and understanding relations between various species. While shape and margin have been extensively studied and used in classification tasks, the vein structure has seldom been explored due to the difficulty in segmenting it from images. The goal of this project is to investigate and develop techniques for automatic extraction of the leaf vein structure from 2D images and to characterize the extracted structure with descriptors for classification tasks. The output of this project will provide computational tools to plant scientists for studying evolution and evolutionary relationships among species and for modelling their continuous variability from the shape perspective.

The student will have the unique opportunity to work within a multi-disciplinary research centre composed of computer scientists (working on image processing and computer vision), mathematicians and plant biologists.

Requirements

  • Ability to program with Matlab
  • Motivation to learn and explore new concepts.

Supervisors

Dr Pankaj Kumar, Professor Stan Miklavcic, Dr Hamid Laga

References

1. Hamid Laga, Sebastian Kurtek, Anuj Srivastava, Stanley J. Miklavcic. Statistical Shape Models of Plant Leaves. International Conference on Image Processing (ICIP) 2013.

2. Hamid Laga, Sebastian Kurtek, Anuj Srivastava, Mahmood Golzarian, and Stanley J. Miklavcic. A Riemannian Elastic Metric for Shape-based Plant Leaf Classification. IEEE International Conference on Digital Image Computing (DICTA), pp. 1-7, 2012.

Plant Segmentation Using Colour Pixel Classification: Analysis and Comparison

This project will involve labelling of plant image data into foreground and background.  A study will then be conducted into three important aspects of the colour pixel classification approach to plant segmentation:  colour representation, colour quantization, and classification algorithms.  The colour spaces to be studied will be RGB, HSV, YCbCr, CIE-lab, and Chromacity and intensity spaces.  The behaviour of segmentation with respect to the colour spaces and the separation of chromacity and intensity spaces will be analysed in different segmentation algorithms.  The effect of different levels of quantization in different colour spaces is also to be studied.  The segmentation algorithms to be studied are Bayesian classification with Gaussian Mixture modelling, Support vector machines, Neural networks, thresholding, and some commonly used heuristics.  This study will lead to the study of some research ideas on phototyping study of plant senescence by imaging of plants.

Supervisor

Dr Pankaj Kumar

Root Phenotyping

 Abstract:  Phenotyping of plant root growth by image processing and analysis of plant root images.  In this research automated algorithms for localization and identification of root features in image sequences of different cereal plants will be developed. The plant roots are grown in transparent gellan gum medium and imaged daily at different rotation angles. Feature extraction and feature selection techniques are employed to extract phenotyping features in root images. Features are matched and tracked across spatially separated images to extract 3D information of the phenotyping feature. Time series of 3D features are obtained by temporally tracking the features.  The project will involve development of

  • Feature extraction algorithms for plant root images
  • Feature selection and classification algorithm
  • 3D thinning algorithms

Supervisor

Dr Pankaj Kumar

Statistical Analysis of the Shape of Plant Leaves

The 2D shape of a plant leaf is of great importance to plant scientists as it can help in distinguishing species, measuring their health, analysing their growth patterns and understanding relations between various species. We propose in this project to implement statistical shape analysis techniques and evaluate their performance on plant leaf shapes. We will study :

  • Statistical analysis using the standard Principal Component Analysis (PCA), known also as eigenshapes,
  • Evaluate the performance of the PCA-based method and compare it with the Riemannian metric that has been previously developed within the hosting research group, see the papers [1, 2, 3].

The output of this project will provide computational tools to plant scientists for studying evolution and evolutionary relationships among species and for modelling their continuous variability from the shape perspective.

The student will also have the unique opportunity to work within a multi-disciplinary research centre composed of computer scientists (working on image processing and computer vision), mathematicians and plant biologists.

Requirements

  • Ability to program with Matlab
  • Familiarity with vector calculus.
  • Motivation to learn and explore new concepts.

Supervisors

Dr Pankaj Kumar, Professor Stan Miklavcic, Dr Hamid Laga

References

1. Sebastian Kurtek, Anuj Srivastava, Eric Klassen, and Hamid Laga. “Landmark-Guided Elastic Shape Analysis of Spherically-Parameterized Surfaces”. Computer Graphics Forum (Proceedings of Eurographics 2013). 32(2), pp. 429-438, 2013.

2. Hamid Laga, Sebastian Kurtek, Anuj Srivastava, Stanley J. Miklavcic. Statistical Shape Models of Plant Leaves. International Conference on Image Processing (ICIP) 2013.

3. Hamid Laga, Sebastian Kurtek, Anuj Srivastava, Mahmood Golzarian, and Stanley J. Miklavcic. A Riemannian Elastic Metric for Shape-based Plant Leaf Classification. IEEE International Conference on Digital Image Computing (DICTA), pp. 1-7, 2012.

Study of inter & intra root interaction in cereal plants

Project will attempt to study inter root tropism in cereal roots grown in transparent Gel medium. Candidate will learn to grow plants in transparent medium like gellan gum or agar in various proportions, combinations, and nutrient gradients. By imaging we will study how by modifies the root system architecture inter root tropism. Can root tropism be parameterised and modelled?  Candidate will learn of novel imaging techniques and techniques of culturing cereal plants and its application to plant functional genomics. This project is a component of a large goal of developing transgenic wheat and barley, which will be more tolerant to drought and salt stress.

The candidate should have some knowledge of mathematical modelling and interest in computational Biology and its application to real life solutions.

Supervisor

Dr Pankaj Kumar

Areas of study and research

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