Mahmoud Ahmed Mohamed Ali (@Mahmoud-Ali-FCIS)
github

Mahmoud-Ali-FCIS

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Top repositories

1

Features-Detection-Describtion-and-Matching

A Comparative Analysis of SIFT, SURF, AKAZE, ORB, BRIEF and BRISK
Python
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2

Person-re-identification-using-Siamese-Network

Train the network with the Triplet loss function and create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embedding.
Jupyter Notebook
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3

Digital-Signal-Processing-Package

This system aim to cover most of part in Signal Processing using Simple GUI to allow to any user to practice and understand the signals and this system divided to several part : 1. 2. 3. 4. 5. 6. 7. 8. Main Window. Signals. Quantization and Encoding. Discrete Fourier Transform (DFT) and (Inverse DFT). Fast Fourier Transform (FFT). Operation On Signals. Convolution and Correlation. Filters and Sampling.
C#
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4

Non-Local-means-filter

Non-local mean filter is an algorithm in image processing for image denoising. Like other algorithm, it based on the basic remark: denoising is achieved by averaging.
MATLAB
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5

Build-Basic-Generative-Adversarial-Networks-GANs-

Started my journey with GANs: 1- Generative adversarial network (GAN) 2- Deep Convolutional GAN (DCGAN) 3- Wasserstein GAN with Gradient Penalty (WGAN-GP) 4- Conditional GAN 5- Controllable Generation
Jupyter Notebook
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6

Camera-Calibration

Find unknown Intrinsic and Extrinsic camera parameter (o = K [R|t] O), Intrinsic Matrix are Parameters component internal to the camera such as (focal length, geometry and radial distortion coefficients of the lens) and use to project Point from camera coordinate to image plan. Extrinsic Matrix are Parameters of camera position [R|t].External to camera and specific to location camera in world coordinate frame. Used to transform 3D point from world to camera coordinate.
Python
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7

Histogram-of-Oriented-Gradients-HOG-

HOG descriptors: are mainly used to describe the structural shape and appearance of an object in an image, making them excellent descriptors for object classification. However, since HOG captures local intensity gradients and edge directions, it also makes for a good texture descriptor.
Python
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