This project is based on image processing techniques to enhance and analyze digital images. The process involves downsampling, quantization, edge detection, filtering, and segmentation using a variety of algorithms.
This phase involves reducing the resolution of the image through downsampling. Then also adjusting the number of intensity levels using quantization techniques.
Canny edge detection is employed to identify edges in the image by applying a multi-stage algorithm involving gradient calculation and non-maximum suppression.
Hough transform is utilized to detect shapes, particularly lines, in the image. This technique is valuable for identifying lines even in the presence of noise.
These edge detection methods are applied to highlight abrupt changes in intensity. Laplacian, Sobel Gradient, and Robert Edge Detector each offer distinct advantages in detecting edges.
Gaussian filtering is implemented to blur and smooth the image, reducing noise and emphasizing important features.
The box filter is used for simple smoothing, averaging pixel values in a local neighborhood.
Median filtering helps in noise reduction by replacing each pixel's value with the median value in its neighborhood.
Laplacian filtering accentuates regions with rapid intensity changes, aiding in edge detection.
Normal and Otsu thresholding techniques are employed to segment the image by dividing it into distinct regions based on intensity levels.
K-Means clustering is utilized to group pixels into clusters based on similarity, providing insights into the image's underlying structure.
This comprehensive image processing project encompasses a range of techniques to enhance, analyze, and segment digital images. Each phase contributes to a holistic approach to image manipulation and understanding.
Happy Coding!