• Overview
• Requirements
• Project description
  • Task1
  • Task2
  • Task3
  • Task4

• Team meambers

This project was done on 4 stages each with its own features as follows:

• Add different noise types and apply different filtering methods.
• Do histogram equalization and normalization to both RGB & Gray image histograms.
• Hybrid image.
• Apply different Edge detection techniques.

• Hough Transform.
• Snake active image contouring.

• Harris corner detection.
• SIFT Matching.
• sum of differences SSD.

• Diferent Segmentation techniques.
• Different Thresholding algorithms Local&Global

Flask==2.2.2
matplotlib==3.6.1
numpy==1.22.4
opencv_python==4.6.0.66
scipy==1.10.1
skimage==0.0

Front-end => vanillaJS, CSS and HTML
Back-end => Flask

1. Apply image Filters: Median , Gaussian , Averaging , Low-pass and High-pass
2. Add different noise to the image: Uniform , Gaussian and Salt-Pepper
3. Apply histogram Equalization and view RGB & Gray histogram
4. apply Low & High pass filters to two images and show their Hybrid image
5. apply different Edge detection methods: Sobel , Canny , Roberts , Prewitt
6. Apply Local & Global Thresholding

Median filter
Gaussian filter
Averaging filter
Low pass filter
High pass filter
S&P Noise
Hybrid
Sobel edge detection
Canny edge detection
Roperts edge detection
Prewitt edge detection
Local thresholding
Global thresholding
Histogram

1. Apply Hough Transform lines,circles & ellipses
2. Apply active Snake contouring

Line
Circle
Ellipse
Snake contour

1. Harris corner detection
2. SIFT
3. SSD

Harris
SIFT
SSD

1. Apply Kmeans, Mean shift,Region Growing and Agglomerative Segmentation techniques
2. Apply Otsu , Spectral and Optimal Thresholding

K-means
Mean shift
Region Growing
Agglomerative
Ostu
Spectral
Optimal

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