Before the deep learning revolution redefined computer vision, Haar features and Haar cascades were the tools you must not ignore for object detection. Even today, they are very useful object detectors because they are lightweight. In this post, you will learn about the Haar cascade and how it can detect objects. After completing this post, […]
Random Forest for Image Classification Using OpenCV
The Random Forest algorithm forms part of a family of ensemble machine learning algorithms and is a popular variation of bagged decision trees. It also comes implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s Random Forest algorithm for image classification, starting with a relatively easier banknote dataset and […]
Normal Bayes Classifier for Image Segmentation Using OpenCV
The Naive Bayes algorithm is a simple but powerful technique for supervised machine learning. Its Gaussian variant is implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s normal Bayes algorithm, first on a custom two-dimensional dataset and subsequently for segmenting an image. After completing this tutorial, you will […]
Support Vector Machines for Image Classification and Detection Using OpenCV
In a previous tutorial, we explored using the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques 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 gathered into two classes. In […]
Hardware-Accelerated AI for Windows Apps Using ONNX RT
Sponsored Content By Rajan Mistry Sr. Applications Engineer with the Qualcomm Developer Network Today, you can’t help but read the media headlines about AI and the growing sophistication of generative AI models like Stable Diffusion. A great example of a use case for generative AI on Windows is Microsoft 365 Copilot. This AI assistant can […]
Support Vector Machines in OpenCV
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 […]
How to Train a Object Detection Engine with HOG in OpenCV
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 […]
Image Datasets for Practicing Machine Learning in OpenCV
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 […]
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 […]
Image Feature Extraction in OpenCV: Keypoints and Description Vectors
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 […]