Python代写 | COMP 9517 Computer Vision Project Specification

本次Python代写是探索基于图像的植物表型分析方法

COMP 9517 Computer Vision
Project Specification

Project Description
In genetics, phenotype [1] is the term used for the composite observable traits or
characteristics of an organism. The phenotype of a plant describes its characteristics such as
number of leaves, architecture, visual age or maturity level, height, leaf shape and so on. In
this project we shall explore an image-based approach to plant phenotyping, exploring
interesting vision tasks to identify plant types, localize their position in an image and segment
the plant and its leaves. See Figure 1 for a high-level overview of image-based plant
phenotyping.
Figure 1: Image-based plant phenotyping [2]
1 DATASET
For this project you will be using the Plant Phenotyping Dataset available here [2]. To
download the data, follow the very first link provided after filling out the form. Once
downloaded, the dataset is organized into three subfolders ‘Plant’, ‘Stacks’ and ‘Tray’. In this
project, we shall be using the data from the ‘Plant’ and ‘Tray’ folders only. The dataset to be
used with each task will be specified along with the Task specification. Some image samples
and leaf segmentation results are shown in Figure 2.
Figure 2: Image Samples from the dataset, (Left) Plant image; (Right) Leaf segmentation.
2 INDIVIDUAL COMPONENT (15 MARKS)
For this component you will perform image classification of plants into two plant classes,
namely Arabidopsis and tobacco.
TASK: In this task you will implement a Python solution to distinguish Arabidopsis plant
images from tobacco plant images. You can implement either a supervised or an unsupervised
classification technique. If you are implementing a supervised technique, you may use either
all the training data provided or a portion of it, depending on the algorithm of your choice /
computational resource limitations
3.1 TASK 1
For this task, implement a Python solution to detect and localise plants in a tray image. The
dataset has a total of 70 tray images, with a bounding box around each individual plant in the
tray. Detect every plant in a given test image, draw bounding boxes around each of them and
display the total number of plants in the image. Also evaluate the performance of the
algorithm using Average Precision (AP) [4] as there is only one class.
3.2 TASK 2
An important plant breeding trait that reflects the overall plant quality is its biomass,
measured as projected leaf area (PLA), which is effectively the number of plant pixels. For this
task implement a Python solution to find the PLA by segmenting the plant from its
background. Evaluate the segmentation algorithm performance using Dice Similarity
coefficient (DSC) and Intersection over Union (IOU) measures.
3.3 TASK 3
When leaves are highly overlapping as in rosette plants (plants having a circular leaf
arrangement), PLA may not be an accurate measure of the plant biomass. In such instances
segmentation of the individual leaves are required. For this task implement a Python solution
to perform individual leaf segmentation, which is a multi-instance segmentation problem.
Evaluate the performance using Symmetric Best Dice measure [2].
3.4 DELIVERABLES FOR GROUP COMPONENT
The deliverables for the group project are 1) a group demo and 2) a group report. Both are
due in Week 10. More detailed information on the two deliverables:
3.4.1 Demo
Project group demos will be scheduled in week 10. Each group will make a 12 minute online
live presentation cum demo to your own tutor and one assessor, and students from other
groups may tune in as well. The demo should include a short slide-show presentation (5 slides
maximum) explaining your methods and evaluation, followed by a demonstration of your
methods, and a brief discussion of how they perform on the given data. Afterwards, you will
answer questions from the tutor/assessor/audience. All group members must be present for
this demo. The demo roster will be released closer to the deadline.


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