Latest posts
#006 Advanced Computer Vision – Object tracking with Mean-Shift and CAMShift algorithms
Highlights: In this post, we’re going to talk about the most common object tracking algorithms – Mean-Shift and CAMShift (Continuously Adaptive Mean-Shift). First, we are going to describe the theory behind the Mean-Shift algorithm. Then, we’ll explain how to apply this algorithm for object tracking in OpenCV with Python. Finally, we’ll learn how to extend the Mean-Shift algorithm into a CAMShift algorithm. So, let’s begin. Tutorial Overview: 1. Mean-Shift algorithm – introduction First, let’s explain…
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#007 Advanced Computer Vision – Video Stabilization
Highlights: Hello and welcome. In this post[1], we will learn to remove unwanted camera movement or shake from a video using a technique called Video stabilization. It is typically used to make videos appear smoother and more professional. There are a variety of techniques that can be used to stabilize a video, including image cropping, image scaling, and frame-by-frame motion estimation. In this post, we will present an efficient and robust implementation of a digital…
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#008 The Mathematics of GANs
Highlights: In the previous posts, we brushed the surface of our book’s hero concept of Generative Adversarial Networks (GANs). We also learned about training stable GANs using Deep Convolutional Generative Adversarial Networks (DCGANs). In this post, we will dive a little bit more into the details of the GANs. We will observe their mathematical foundations through the fundamentals of probability and optimization methods. So, let’s begin. Tutorial overview: Adversarial Learning The Adversarial Game Loss Function…
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#006 Linear Algebra – Inner or Dot Product of two Vectors
Highlight: In this post we will review one of the fundamental operators in Linear Algebra. It is known as a Dot product or an Inner product of two vectors. Most of you are already familiar with this operator, and actually it’s quite easy to explain. And yet, we will give some additional insights as well as some basic info how to use it in Python. Tutorial Overview: Dot product :: Definition and properties Linear functions…
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Featured posts
#004 How to smooth and sharpen an image in OpenCV?
Highlight: In our previous posts we mastered some basic image processing techniques and now we are ready to move on to more advanced concepts. In this post, we are going to explain how to blur and sharpen images. When we want to blur or sharpen our image, we need to apply a linear filter. You will learn several types of filters that we often use in the image processing In addition, we will also show…
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#000 How to access and edit pixel values in OpenCV with Python?
Highlight: Welcome to another datahacker.rs post series! We are going to talk about digital image processing using OpenCV in Python. In this series, you will be introduced to the basic concepts of OpenCV and you will be able to start writing your first scripts in Python. Our first post will provide you with an introduction to the OpenCV library and some basic concepts that are necessary for building your computer vision applications. You will learn…
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#006 Linear Algebra – Inner or Dot Product of two Vectors
Highlight: In this post we will review one of the fundamental operators in Linear Algebra. It is known as a Dot product or an Inner product of two vectors. Most of you are already familiar with this operator, and actually it’s quite easy to explain. And yet, we will give some additional insights as well as some basic info how to use it in Python. Tutorial Overview: Dot product :: Definition and properties Linear functions…
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#008 Linear Algebra – Eigenvectors and Eigenvalues
Highlight: In this post we will talk about eigenvalues and eigenvectors. This concept proved to be quite puzzling to comprehend for many machine learning and linear algebra practitioners. However, with a very good and solid introduction that we provided in our previous posts we will be able to explain eigenvalues and eigenvectors and enable very good visual interpretation and intuition of these topics. We give some Python recipes as well. Tutorial Overview: Intuition about eigenvectors…
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#011 TF How to improve the model performance with Data Augmentation?
Highlights: In this post we will show the benefits of data augmentation techniques as a way to improve performance of a model. This method will be very beneficial when we do not have enough data at our disposal. Tutorial Overview: Training without data augmentation What is data augmentation? Training with data augmentation Visualization 1. Training without data augmentation A familiar question is “why should we use data augmentation?”. So, let’s see the answer. In order…
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