Highlights: Hi. Welcome to our new lecture. In our previous posts, we already explained in great detail how to extract distinct features (also called keypoints) from an image. Now we will learn how to compare two or more images by extracting pairs of identical feature points from those images. To accomplish this, we can apply several different feature matching methods that OpenCV provides. We hope that this post will complete your knowledge in this area…
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Highlights: A common problem in computer vision applications is the estimation of motion of every pixel in a sequence of images or video sequences. While many methods have been proposed to solve this problem, Optical Flow stands out as one of the most efficient ones. In this post, we will learn how to better understand a video sequence by analyzing how objects are moving across the frames using a popular Optical Flow estimation algorithm called…
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Highlights: Often, we come across projects or tasks where we need to merge or mix two images. However, due to various parametric restrictions such as sizing, the task seems difficult to execute. Also, it can be boring when it is done in Photoshop. However, this process can be quite interesting if it is fully automated. In this post, we will make the blending and pasting of images on top of each other, fun and interesting.…
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Highlights: In this post, we will learn how to detect contours in images. First, we will remind ourselves how to detect lines and circles using the Hough transform. Then, we will move on by showing how to detect more complex shapes like contours. Moreover, we will learn how to analyze these shapes that will help us to better understand the content of the picture. Let’s begin with our post. Tutorial overview: What are contours in…
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