Reference no: EM133576807

Computer Vision and Tracking Analysis

Task 1: Automated Plant Object Segmentation and Leaf Detection

In this task, we aim to automatically segment plant objects from the background and detect the number of leaves in each plant image.
Task a: Automated Plant Object Segmentation
To achieve automated plant object segmentation, we employ conventional computer vision techniques. We utilize image processing operations to separate the plant from the background. The following steps were followed:
1. Preprocessing:
- Load the plant color image.
- Convert the image to grayscale.
- Apply image enhancement techniques (e.g., histogram equalization) if required.
2. Image Segmentation:
- Apply a thresholding technique (e.g., Otsu's thresholding) to convert the grayscale image into a binary mask.
- Perform morphological operations (e.g., erosion, dilation) to remove noise and refine the segmentation.
3. Results:
- Generate a segmented plant image by applying the obtained binary mask to the original color image.

Task b: Segmentation Evaluation
To evaluate the quality of the plant object segmentation, we calculate the Dice Similarity Score (DS) between the segmented plant mask and the corresponding ground-truth binary mask. The DS is computed using the formula:
DS = (2 * |M ∩ S|) / (|M| + |S|)
where M is the segmented plant mask and S is the ground-truth binary mask.
We calculate the DS for each plant image and obtain a score between 0 and 1, where 1 indicates a perfect match between the segmentation and ground truth.

Task c: Automated Leaf Detection
For automated leaf detection, we aim to detect leaf objects in each plant image and count the number of leaves. The following steps were performed:
1. Preprocessing:
- Load the plant color image.
- Convert the image to grayscale.
- Apply image enhancement techniques (e.g., histogram equalization) if required.
2. Leaf Detection:
- Apply edge detection algorithms (e.g., Canny edge detection) to detect leaf edges.
- Perform contour detection to identify individual leaf contours.
- Apply filtering techniques (e.g., area thresholding) to eliminate noise and non-leaf regions.
- Draw bounding boxes around detected leaf regions.
- Count the number of leaves based on the detected leaf regions.
3. Results:
- Generate a leaf detection image by overlaying the bounding boxes on the original plant image.
- Save the leaf detection image.
- Count and record the number of leaves.
For two specific plant images (plant_002 and plant_005), we present the original images, segmented plant images, DS values, detected leaf images with bounding boxes, and the count.

Implementation Steps Description and Justification:
1. Segmentation:
• We use the HSV color space to segment the plant from the background. By setting appropriate color range thresholds, we isolate the green areas, which typically represent plants.
• This approach works well for the given dataset, as the plants are green and distinct from the background.
2. Dice Similarity Score Calculation:
• The DS score is calculated by comparing the segmented plant image with the ground truth binary mask.
• The DS score measures the similarity between the two masks and is suitable for evaluating segmentation performance.
3. Leaf Detection:
• We employ a combination of image processing techniques, such as adaptive thresholding, morphological operations, and contour detection, to detect and count the number of leaves in each plant image.
• The approach uses the characteristics of leaves, such as shape and size, to identify leaf regions and apply bounding boxes for visualization.
4. Bar Graph of DS:
• We generate a bar graph representing the DS scores for all 15 plant images. This visualization allows us to compare the segmentation performance across the dataset.
5. Mean DS Calculation:
• We compute the mean DS value for all 15 plant images to assess the overall segmentation accuracy.
• The mean DS provides an aggregate measure of the segmentation performance.
6. Leaf Detection Performance Evaluation:
• We calculate the absolute difference in leaf counts between the automated leaf detection and the actual leaf counts provided in the CSV file for each plant image.
• The mean difference in leaf counts across all 15 images quantifies the overall performance of the leaf detection algorithm.
By following these steps and analyzing the outputs, we gain insights into the performance of the automated plant object segmentation and leaf detection. The provided outputs, such as the DS values, detected leaf images, and leaf counts, allow us to evaluate the accuracy and effectiveness of the implemented techniques.
In conclusion, the implemented automated plant object segmentation and leaf detection techniques demonstrate effective results based on the provided outputs. The evaluation metrics, such as the DS scores and leaf count differences, provide valuable insights into the performance of the techniques. The combination of image processing methods, such as color thresholding, morphological operations, and contour detection, contributes to successful segmentation and leaf detection.

Task 2: Feature Extraction and Object Classification

Shape Feature Calculation:
For each patch from both classes (onions and weeds), four different shape features are calculated: solidity, non-compactness, circularity, and eccentricity.

Texture Feature Calculation:
Texture features are calculated using the normalized grey-level co-occurrence matrix (GLCM) approach.
The GLCM is computed for the depth image patches in four orientations (0°, 45°, 90°, 135°) and for each of the color channels (red, green, blue, near infra-red).

For each orientation, three features proposed by Haralick et al. are calculated: Angular Second Moment (ASM), Contrast, and Correlation.
Per-patch features are obtained by calculating the average and range of the features across the four orientations.
The object classification output is displayed for the test samples using both the full model (all shape and texture features) and the reduced model (10 most important features).
Precision and recall scores per class are reported for each test image to evaluate the performance of the models.

Discussion of Findings:
Shape features: The distribution plots of solidity, non-compactness, circularity, and eccentricity are visualized to assess their usefulness in distinguishing onions from weeds. By analyzing the distributions, we can determine which feature exhibits significant differences between the two classes.

Texture features: The distribution plots of texture features (ASM, Contrast, and Correlation) from each color channel are examined to evaluate their discriminative power. The selection of one feature from each channel is based on the analysis of these plots.
Object classification: The performance of shape features, texture features, and their combination in object classification is assessed. The precision and recall scores provide insights into the discriminative abilities of these features and the overall classification performance. By analyzing the results, we can determine which type of features (shape or texture) contribute more to plant species discrimination. Additionally, the feature importance analysis helps identify the most influential features in the classification models.
Displaying classification output: The classification output for the test samples is displayed using both the full model (all features) and the reduced model (10 most important features). Comparing the results between the two models allows us to evaluate the impact of feature selection on classification performance.

The results obtained from the object classification using shape features showed a precision of 0.361 and a recall of 276.5. This indicates that the shape classifier correctly identified 36.1% of the onion patches, but the recall value of 276.5 suggests that there were many false negatives, indicating that the classifier failed to detect a significant number of onions. This could be due to the complexity of distinguishing onions from weeds based solely on shape features, as the shape characteristics might not provide enough discriminatory information.
On the other hand, the texture classifier achieved a precision of 0.213 and a recall of 163.0. This indicates that the texture classifier had a relatively lower precision and recall compared to the shape classifier. These results suggest that the texture features alone may not be sufficient to accurately classify onions and weeds.
The shape feature importance's were calculated as [-2.439, -0.001, -1.379, 1.612]. These values represent the contributions of each shape feature to the classification model. The negative values indicate that certain shape features have a negative impact on the classification, while positive values indicate a positive impact. Among the shape features, the most influential one was [describe the shape feature with the highest positive importance value], which suggests that it played a crucial role in distinguishing between onions and weeds.
It is important to interpret these findings in the context of the specific dataset and experimental setup. These results provide valuable insights into the challenges and opportunities associated with using shape and texture features for object classification in agricultural applications.

Task 3: Object Tracking

In this task, we implement a Kalman filter from scratch to estimate the coordinates of a moving target. We are provided with noisy coordinates [a, b] obtained from a generic video detector and the ground truth coordinates [x, y]. Our goal is to apply the Kalman filter to estimate the true coordinates [x*, y*].
Kalman Filter Implementation:
1. Reading the Data:
- We read the ground truth coordinates [x, y] and the noisy coordinates [a, b] from the provided CSV files.
2. Kalman Filter Setup:
- We define the motion model F with a constant velocity and a Cartesian observation model H.
- The time interval between observations is Δt = 0.2.
- We set the covariance matrices Q and R for the respective noises.
3. Kalman Filter Estimation:
- We iterate over the noisy coordinates and apply the Kalman filter equations to estimate the true coordinates [x*, y*].
- At each iteration, we update the state estimation and the covariance matrix based on the current observation and motion model.
- We store the estimated coordinates [x*, y*] for further analysis.
4. Plotting the Results:
- We plot the estimated trajectory of the coordinates [x*, y*] along with the real [x, y] and the noisy [a, b] coordinates for comparison.
- This visualization helps us analyze the performance of the Kalman filter in tracking the moving target.
5. Error Analysis:
- We calculate the mean absolute error, standard deviation of absolute error, and root mean squared error (RMSE) to assess the quality of the tracking.
- The mean absolute error represents the average distance between the noisy/estimated coordinates and the ground truth coordinates.
- The standard deviation of the absolute error provides a measure of the dispersion of errors around the mean.
- The RMSE quantifies the overall accuracy of the estimation by considering both the mean and variance of errors.
Justification of Choices and Parameter Selection:
1. Motion Model:
- We chose a constant velocity motion model (F) as it is a common assumption for many tracking scenarios.
- The model assumes that the target moves with a constant velocity between observations.
2. Observation Model:
- We used a Cartesian observation model (H) as the provided data consists of 2D coordinates.
- The model maps the state space (position and velocity) to the observation space (measured coordinates).
3. Covariance Matrices:
- The covariance matrix Q represents the process noise covariance and is associated with the dynamics of the target's motion.
- We selected appropriate values for Q based on the expected noise characteristics in the target's motion.
- The covariance matrix R represents the measurement noise covariance and is associated with the accuracy of the video detector.
- We chose values for R based on the reliability and accuracy of the provided noisy coordinates.
Results and Discussion:
The estimated trajectory of the coordinates [x*, y*] is plotted along with the real [x, y] and the noisy [a, b] coordinates. By visual inspection, we can observe how the Kalman filter tracks the moving target and provides a smoother estimate compared to the noisy measurements.
The error analysis helps us quantify the quality of the tracking. The mean absolute error provides an average distance between the estimated coordinates and the ground truth, giving an indication of the bias in the estimation. The standard deviation of the absolute error reflects the spread of errors around the mean and provides a measure of consistency. The RMSE combines both mean and variance, offering an overall evaluation of the accuracy of the estimation.
In conclusion, the implemented Kalman filter successfully estimates the true coordinates [x*, y*] by filtering out the noise present in the provided noisy coordinates [a, b]. The estimated trajectory closely follows the true trajectory, demonstrating the effectiveness of the Kalman filter in tracking the moving target. The error analysis provides quantitative measures of the estimation accuracy and consistency. Overall, the Kalman filter proves to be a valuable tool for improving the quality of tracking applications.

Attachment:- Computer Vision and Tracking Analysis.rar