/Learning-Image-Processing

This repository includes my work in the journey to learn Image Processing.

Primary LanguageJupyter NotebookMIT LicenseMIT

Learning Image Processing

This repository includes my work in the journey to learn Image Processing.

the repository consists of 9 labs covering fundamental topics in Image Processing.

Lab3: Smoothing

  • median filter to noisy image with S&P Median filter applied to salt and pepper noise

  • Gaussian filter with different sigma Gaussian filter applied to salt and pepper noise

Lab4: Contrast Enhancement

  • Negative transformation Image Negative transformation

  • Histogram Equalization Histogram Equalization

Histogram Equalization - Histogram Equalization

Lab5: Edge Detection

  • Applying Roberts and Sobel edge detectors sobel roberts edge detectors

  • Canny Edge detector with different smoothing sigma and trying different low/high thresholds
    Canny edge detector with different thresholds

Lab6: Morphology

  • Applying erosion and dilation to coins erosion dilation

  • Credit Card Number Extraction: by using erosion and dilation along with contours to get bounding boxes Credit Card Number Extraction

  • Skeltonize the horse Vs thin by only 20 iteration Skeltonize Vs thin

Lab7: Segmentation 1

  • Segmenting the grass from the image by calculating the distance from grass color, then apply threshold. segmentation by threshold and distance

    Lab8: Segmentation 2 - adaptive thresholding techniques

  • histogram automatic thresholding technique result in threshold = 89. adaptive thresholding techniques

    Lab9: textures - segmentation

  • extracting glcm features from different textures then use them to identify and segment these textures in a given image like cotton, jeans, and background. glcm features textures segementation