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1. Ting Lee Siang (85935)

2. Chien Shoa Jun (83477)

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1. Computational Efficiency: Analyzing the time complexity of different algorithms and exploring optimization techniques.

2. MATLAB Functions and Toolboxes: Utilizing relevant MATLAB functions and toolboxes such as Image Processing Toolbox.

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Graphic User Interface (GUI) developed using App Designer

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can found the tutorial from MathWorks

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What are the problems or limitations faced in this part of the project?

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What are the problems or limitations faced in this part of the project?

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1. In cross-correlation the binary correlation image does not come out output.

2. MATLAB struggles with memory when processing large image datasets.

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Present the interactive user interface using MATLAB programming

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Present the interactive user interface using MATLAB programming

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Show the Identify objects with a circle shape from a set of images (in the Test Images folder)

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Show the Identify objects with a circle shape from a set of images (in the Test Images folder)

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can found the tutorial from MathWorks

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3. Denoised Image Obtained Using Bilateral Filtering

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3. Denoised Image Obtained Using Bilateral Filtering

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4. Denoised Image Obtained Using Multiscale CAN

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4. Denoised Image Obtained Using Multiscale CAN

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5. List of outputs (Region-of-interest (ROI))

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5. List of outputs (Region-of-interest (ROI))

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Show TWO (2) examples of your noise images, and show how the denoised images are obtained using multiscale CAN. 

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Show TWO (2) examples of your noise images, and show how the denoised images are obtained using multiscale CAN. 

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can found the tutorial from MathWorks

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What are the problems or limitations faced in this part of the project?

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What are the problems or limitations faced in this part of the project?

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When install MATLAB in Data(D:) , it was unable load the pretrained CAN ("trainedBilateralFilterNet.mat")

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What are the problems or limitations faced in this part of the project?

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What are the problems or limitations faced in this part of the project?

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1. Non-Perfect Circles: Real-world objects have slight imperfections, making it challenging to determine if a shape is truly circular.

2. Object Overlap: if circles overlap, it can be challenging to separate them and measure their individual properties

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Provide the answer to all the questions (a-f) 

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Provide the answer to all the questions (a-f) 

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a. How many training images are used in the example? What is the size of the input image?

1. Number of training images:

The number of training images is determined by the pristineImages datastore. This information can be extracted using:

disp(['Number of pristine images: ', num2str(numel(pristineImages.Files))]);

Based on the code, the output would indicate the total number of pristine images in the specified directory (e.g., "D:\MATLAB\R2024b\Examples\R2024b\iaprtc12\images\39").

2. Input image size:

The input image size is defined in the network using the imageInputLayer:

imageInputLayer([256 256 3], 'Name', 'InputLayer', 'Normalization', 'none')

The input images are resized to 256x256 pixels with 3 color channels (RGB).

b. What are the two types of images used for the training process?

1. Pristine images:

These are the original high-quality images from the pristineImages datastore.

2. Filtered images:

These are the pristine images processed with a bilateral filter, which serve as the ground truth for training. They are stored in the bilatFilteredImages datastore.

c. What are the inputs of the network? What are the desired responses of the network?

1. Inputs of the network:

The noisy image patches extracted from pristineImages.

2. Desired responses of the network:

The corresponding denoised image patches (bilaterally filtered patches) from bilatFilteredImages.

d. What are the layers used in the multiscale CAN from the Deep Learning Toolbox™?

Based on the code, the multiscale CAN includes the following layers:

1. Image input layer:

Accepts input images of size [256x256x3].

2. Convolutional layers:

Multiple convolution2dLayer layers with 3x3 filters and padding.

3. ReLU layers:

Rectified Linear Units (ReLU) are applied after each convolutional layer.

4. Regression layer:

Outputs the final predicted image values.

e. What is the first layer, middle layer, and last layer of the CAN network in the example?

1. First layer:

imageInputLayer([256 256 3], 'Name', 'InputLayer', 'Normalization', 'none')

2. Middle layer:

For a 10-layer network, the approximate middle layer would be:

convolution2dLayer(3, numberOfFilters, 'Padding', 'same', 'Name', 'Conv3')

reluLayer('Name', 'ReLU3')

3. Last layer:

convolution2dLayer(3, 3, 'Padding', 'same', 'Name', 'ConvFinal')

regressionLayer('Name', 'OutputLayer')

f. What is the training optimizer used to train the network? What are the default values of the hyperparameters of the network?

1. Training optimizer:

The optimizer used is Adam, as specified in the training options:

trainingOptions('adam', ...)

2. Default hyperparameters for Adam optimizer:

Based on the MATLAB Deep Learning Toolbox, the typical default values are:

Learning rate: 0.001 (overridden by your code using 'InitialLearnRate', 1e-3).

Beta1: 0.9

Beta2: 0.999

Epsilon: 1e-8

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1. Sylvia Kong Li Yee (85868)

2. Eva Natasha anak Spencer (83805)

3. Sherina Mae anak Diwong (85683)

4. Alexander Gudang Anak Colline (83142)

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Reminder before copy this link to paste it into our report. 

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Reminder before copy this link to paste it into our report. 

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Note: Please select the visitor permission to “Writer” and Link privacy to “Secret” so that I can comment on your Padlet after you have submitted your group project.

Can reach it at share button icon.

after done it, don't forget to delete. (peace)

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Load Image

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1. Median Filtering - Noisy Image

2. Median Filtering - Filtered Image

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1. Thresholding - Thresholded Image (T=50)

2. Thresholding - Thresholded Image (T=200)

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1. Sobel Edge Detection

2. Canny Edge Detection

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1. Image with Gaussian Noise

2. Sobel Gradient Magnitude

3. Prewitt Gradient Magnitude

4. Custom Roberts Gradient Magnitude

5. Smoothed Gradient Magnitude

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1. Rotated Image

2. Edge Detection Using Canny

3. Hough Transform Accumulator

4. Detected Lines and Longest Highlighted

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1. Detected symbols using cross correlation

2. Correlation Map

3. Binary Correlation Image

4. 3D Surface Plot of Correlation

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Ver.2 Video Image with Candy Line Detection

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Ver.2 Video Image with Candy Line Detection