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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 …
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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 …
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The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text. Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual …
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Asymmetric Certified Robustness via Feature-Convex Neural Networks TLDR: We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios. This focused setting allows us to introduce feature-convex classifiers, which produce closed-form and deterministic certified radii on the order of milliseconds. Figure 1. Illustration of feature-convex classifiers …
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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 …
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 …
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