Image Processing & Computer Vision Projects

NOTE: Click on the images to view the full-size.

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1_Contrast Adjustment link

Histogram_Equalization

• Implementation of Histogram Equalization algorithm only on the pixels inside the circle in the image.

Contrast_Stretching

• Implementation of Contrast Stretching algorithm on the image.

Compare

• Comparing Histogram Equalization and Contrast Stretching algorithms.

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2_Image Filtering (Denoising, Sharpening, Deblurring) link

Notch

• Implementation of Notch Filter algorithm on the image.

Wiener

• In this algorithm, we applied the Wiener Filter filter to each of the RGB bands separately and finally, merged the filtered bands to get the final image.

Highboost

• In this algorithm, we applied the High Boost filter to each of the RGB bands separately and finally, merged the filtered bands to get the final image.

Unsharp

• In this algorithm, we applied the Unsharp Masking filter to each of the RGB bands separately and finally, merged the filtered bands to get the final image.

Median

• Implementation of Median algorithm on the image.

Adaptive_Median

• Implementation of Adaptive Median algorithm on the image.

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3_Content-Based Image Retrieval (CBIR) System link

RGB_CBIR

• Content-based image retrieval system by using RGB color histogram.

HSV_CBIR

• Content-based image retrieval system by using HSV color histogram.

RGB_CBIR_Half

• Content-based image retrieval system by using RGB color histogram on half of the image.

HSV_CBIR_Half

• Content-based image retrieval system by using HSV color histogram on half of the image.

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4_Image Pyramids & Blending link

Hybrid_Fourier

• Creating the Hybrid Image using the Fast Fourier Transform (FFT).

Hybrid_Pyramid

• Creating the Hybrid Image using the Laplacian Pyramids.

Blend_Pyramid

• Creating the Blended Image using the Laplacian Pyramids.

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5_Scale-Invariant Feature Transform (SIFT) algorithm link

SIFT_v1

• In this algorithm, we select an image and apply changes (rotation, size change) on the selected image, then extract and display the SIFT features of the two images.

SIFT_v2

• In this algorithm, we select an image and apply changes (rotation, size change) on the selected image, then display the corresponding SIFT features of the two images.

CBIR_SIFT_trainHist & CBIR_SIFT_main

• Improving Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT). CBIR using the 3D histogram (left images) & rectified ranking using the SIFT (right images).

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6_Gradient Domain Fusion (Poisson Blending, Mixed Gradient) link

Gradient_Blend

• Implementation of Poisson Blending and Mixed Gradient algorithms.

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7_Camera Calibration with OpenCV link

Camera_Calibration

• Implementation of Camera Calibration method with OpenCV.
  • This program has two modes:
    1. The images are taken from the camera and the distortion of the camera is removed at the same moment, this mode includes two stages: Train (extracting camera parameters) and Test (testing the camera by simultaneously displaying the two original and corrected parts).
    2. Images are selected from the image folder and the corrected image is displayed and saved in the output folder.

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