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OpenCV #012 Discrete Fourier Transform, Part 2

10.06.2019

Highlights: In the previous post,  we learnt some fundamental details about the Fourier transform and why it’s worth learning. We also showed how to transform an image into its frequency domain. I recommend you read about it here if you haven’t. In this section, we would focus on filtering in the frequency domain. We would see the effects of applying a low and high pass filter. Almost all natural images have similar power spectrum Tutorial…
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CamCal 004 What does R look like?

18.04.2019

Highlights: In this post we will continue working on camera calibration, and we will take a detailed look how does \(R \) look like. If you don’t remember what is \(R \), that is a rotation operator. Tutorial Overview: Intro Example: Rotation About Z-Axis Rotation in Homogeneous Coordinates Rigid Transformation 1. Intro There are two ways to think about this rotation operator. First, we will think in a hard way. \(_{A}^{B}\textrm{R} \) expresses how each…
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#FA 002 Face Detection with OpenCV in Images

09.04.2019

Face detection represents the ability of a computer technology to locate peoples’s faces within digital images. Face detection applications employ algorithms focused on detecting human faces within larger images that also contain other objects such as landscapes, houses, cars and others. Table of Contents: Import required packages Select the network Preprocess the image, standardise, mean subtraction Process the image with a Neural Network Analyze detections The importance of face detection can be seen as: The…
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FR 001 Face Recognition with Celebrities

09.04.2019

Highlights: In the world today, there are a lot of visual data and it is important how we utilize and interpret this data. The project is more of an evolution between traditional algorithms and deep learning techniques. How accurately can we predict and find the correct name of the celebrity in a given image or video frame. Tutorial Overview: This post covers the following topics: What is a facial recognition system? Applications of face recognition.…
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#008 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – digits dataset

08.02.2019

In this post we will see how we can classify handwritten digits using shallow neural network implemented with tf.keras. Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digits dataset First, let us import all necessary libraries. After imports, we can use imported module to load digits data. The load_digits() function will just download data and we need to split it into train and test sets. We can also…
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The hundred-page Computer Vision book
What are morphological transformations?
Learn how to align Faces in OpenCV in Python

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