In a previous tutorial, we have explored the use of the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques that comes implemented in the OpenCV library. So far, we have seen how to apply Support Vector Machines to a custom dataset that we have generated, consisting of two-dimensional points […]
One of the pre-processing steps that are often carried out on images before feeding them into a machine learning algorithm is to convert them into a feature vector. As we will see in this tutorial, there are several advantages to converting an image into a feature vector that makes the latter more efficient. Among the […]
The Support Vector Machine algorithm is one of the most popular supervised machine learning techniques, and it comes implemented in the OpenCV library. This tutorial will introduce the necessary skills to start using Support Vector Machines in OpenCV, using a custom dataset that we will generate. We will then apply these skills for the specific […]
In the previous post, you saw that OpenCV can extract features from an image using a technique called the Histogram of Oriented Gradients (HOG). In short, this is to convert a “patch” of an image into a numerical vector. This vector, if set up appropriately, can identify key features within that patch. While you can […]
At the very start of your machine learning journey, making use of publicly available datasets alleviates the worry of having to create the datasets yourself, and rather lets you focus on learning to use the machine learning algorithms. It also helps if the datasets are moderately sized and do not require too much pre-processing, to […]
Besides the feature descriptor generated by SIFT, SURF, and ORB, as in the previous post, the Histogram of Oriented Gradients (HOG) is another feature descriptor you can obtain using OpenCV. HOG is a robust feature descriptor widely used in computer vision and image processing for object detection and recognition tasks. It captures the distribution of […]
In the previous post, you learned some basic feature extraction algorithms in OpenCV. The features are extracted in the form of classifying pixels. These indeed abstract the features from images because you do not need to consider the different color channels of each pixel, but to consider a single value. In this post, you will […]
In the world of computer vision and image processing, the ability to extract meaningful features from images is important. These features serve as vital inputs for various downstream tasks, such as object detection and classification. There are multiple ways to find these features. The naive way is to count the pixels. But in OpenCV, there […]
The OpenCV library comes with a module that implements the k-Nearest Neighbors algorithm for machine learning applications. In this tutorial, you are going to learn how to apply OpenCV’s k-Nearest Neighbors algorithm for the task of classifying handwritten digits. After completing this tutorial, you will know: Several of the most important characteristics of the k-Nearest […]
In a previous tutorial, we have explored the use of the k-means clustering algorithm as an unsupervised machine learning technique that seeks to group similar data into distinct clusters, to uncover patterns in the data. We have, so far, seen how to apply the k-means clustering algorithm to a simple two-dimensional dataset containing distinct clusters, […]
