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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 …
Read more “Image Feature Extraction in OpenCV: Keypoints and Description Vectors”
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 …
Read more “Image Feature Extraction in OpenCV: Edges and Corners”
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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 …
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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, …
Read more “K-Means Clustering for Image Classification Using OpenCV”
When you work with OpenCV, you most often work with images. However, you may find it useful to create animation from multiple images. Chances are that showing images in rapid succession may give you different insight or it is easier to visualize your work by introducing a time axis. In this post, you will see …
Read more “How to Transform Images and Create Video with OpenCV”
The k-means clustering algorithm is an unsupervised machine learning technique that seeks to group similar data into distinct clusters, with the aim of uncovering patterns in the data that may not be apparent to the naked eye. It is possibly the most widely known algorithm for data clustering, and it comes implemented in the OpenCV …
Read more “K-Means Clustering in OpenCV and Application for Color Quantization”
Digital videos are close relatives of digital images because they are, indeed, made up of many digital images that are sequentially displayed in rapid succession to create the effect of moving visual data. The OpenCV library provides several methods to work with videos, such as reading video data from different sources as well as accessing …
When working with images, some of the most basic operations that are essential to get a grip on include reading the images from disk, displaying them, accessing their pixel values, and converting them from one color space to another. This tutorial explains these basic operations, starting first with a description of how a digital image …
Read more “How to Read, Write, Display Images in OpenCV and Converting Color Spaces”