SALE! Use code blackfriday for 40% off everything!
Hurry, sale ends soon! Click to see the full catalog.

Archive | OpenCV


Support Vector Machines in OpenCV

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 […]

Continue Reading

Extracting Histogram of Gradients with OpenCV

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 […]

Continue Reading

Image Feature Extraction in OpenCV: Edges and Corners

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 […]

Continue Reading

K-Nearest Neighbors Classification Using OpenCV

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 […]

Continue Reading

K-Means Clustering for Image Classification Using OpenCV

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, […]

Continue Reading