Kittipong Tapyou

Variable Thresholding

Sun, 02 Nov 2025 00:00:00 +0000

Variable Thresholding

This lab implements adaptive thresholding to handle uneven illumination in grayscale images. Instead of one global threshold, the image is divided into smaller partitions, and each part gets its own local Otsu threshold. Result: Local thresholds adapt to illumination, correctly separating dark circular objects from the background much better than global thresholding.

Image Restoration

Tue, 28 Oct 2025 00:00:00 +0000

Blurring Function and Restoration

Simulates linear motion blur and performs restoration in the frequency domain.

Simulates sequential horizontal and vertical motion blurs (T₁, T₂).
Restores blurred images by dividing Fourier-transformed images with known H(u, v).

Observation:
Effective restoration achieved when motion parameters (a, b, T) are known and noise is negligible.

Morphological Operation

Tue, 21 Oct 2025 00:00:00 +0000

Morphological Operations

Implements connected-component labeling using geodesic dilation:

Invert the binary image to make objects white.
Select an unlabeled white pixel as a seed.
Perform geodesic dilation constrained by the original mask.
Label each connected region iteratively.

Hough Transform

Tue, 14 Oct 2025 00:00:00 +0000

Hough Transform

Manual implementation for:

Line detection: Based on Sobel edge maps, ρ–θ parameterization, and local maxima in Hough space.

Circle detection: Uses tangent direction estimation to compute (a, b, r).

Result:
Successfully detects multiple lines and circles in complex images such as clocks and coins by iterative voting and duplicate suppression.

Spatial Filtering

Tue, 23 Sep 2025 00:00:00 +0000

Spatial Filtering

Implements:

Low-pass filters: Mean and Gaussian
High-pass filters: Laplacian and Laplacian of Gaussian (LoG)
Includes a custom convolution with edge-padding (ConvPadded).

Observation:

Mean filter increases blur with kernel size.
Gaussian filter maintains more structure.
LoG detects sharper edges compared to Laplacian alone.

Histogram Equalization

Tue, 16 Sep 2025 00:00:00 +0000

Histogram Equalization

Steps:

Compute histogram and cumulative distribution function (CDF).
Normalize CDF and map it to new intensity values (HistEqualize function).
Compare manual results with Mathematica’s HistogramTransform.

Result:
Custom implementation produces contrast enhancement nearly identical to the built-in method, with well-distributed intensity levels.

Thresholding

Wed, 20 Aug 2025 00:00:00 +0000

Maximum Normal Line and Average Intensities Method