Design and implement solutions to a range of computer vision applications and problems, and evaluate their effectiveness.

Learning Outcome 1: Critically evaluate and apply the theories, algorithms, techniques and methodologies involved in computer vision.

Learning Outcome 2: Design and implement solutions to a range of computer vision applications and problems, and evaluate their effectiveness.

Task 1: Image Segmentation and Detection

Download two files: ‘plant image dataset.zip` and ‘leaf_counts.csv` from Blackboard. Unzip the dataset file, you should obtain a set of 32 images. Among those images, there are 16 plant colour images and 16 corresponding leaf labelled images (ground-truth segmentation). Figure 1 shows an example of one plant image and its corresponding leaf labelled image. The "leaf_counts.csv" file contains the number of leaves for each image.

Please use image processing techniques to implement the following three tasks. Please note that you are encouraged to develop one model with same parameter settings for all the images.

Task 1.1: Automated plant object segmentation. For each image, automatically segment plant from background.

Task 1.2: Segmentation evaluation. For each plant image, calculate the Dice Similarity Score (DS) which is defined in Equation 1; where M is the segmented plant mask obtained from Task 1, and S is the corresponding ground-truth binary mask. Please note that, in this case, for the provided plant labelled images, you can convert the color images into binary images (e.g. plant object and background), and use the converted binary images as ground-truth mask.