The Support Vector Machine algorithm is one of the most popular supervised machine learning techniques, and it is implemented in the OpenCV library. This tutorial will introduce the necessary skills to start using Support Vector Machines in OpenCV, using a custom dataset we will generate. In a subsequent tutorial, we will then apply these skills […]
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, publicly available datasets alleviate the worry of creating the datasets yourself and let 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 get you to practice using the […]
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 […]
Sponsored Content From November 6 (07:00 PST) to 20 (07:00 PST), 365 Data Science offers free unlimited access to its entire curriculum, featuring interactive courses, hands-on data projects, and certificates of achievement. This opportunity allows for in-depth data science and AI exploration—presenting a no-cost method to enhance one’s skills and knowledge. Enhancing Careers […]
The OpenCV library has a module that implements the k-Nearest Neighbors algorithm for machine learning applications. In this tutorial, you will learn how to apply OpenCV’s k-Nearest Neighbors algorithm for classifying handwritten digits. After completing this tutorial, you will know: Several of the most important characteristics of the k-Nearest Neighbors algorithm. How to use the […]
Sponsored Content By Travis Addair & Geoffrey Angus If you’d like to learn more about how to efficiently and cost-effectively fine-tune and serve open-source LLMs with LoRAX, join our November 7th webinar. Developers are realizing that smaller, specialized language models such as LLaMA-2-7b outperform larger general-purpose models like GPT-4 when fine-tuned with proprietary […]
In a previous tutorial, we explored using 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. So far, we have seen how to apply the k-means clustering algorithm to a simple two-dimensional dataset containing distinct clusters and the problem […]
