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 […]
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 […]
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 […]
One of the most popular plotting libraries in R is not the plotting function in R base, but the ggplot2 library. People use that because it is flexible. This library also works using the philosophy of “grammar of graphics”, which is not to generate a visualization upon a function call, but to define what should […]
When you are working on a data science project, the data is often tabular structured. You can use the built-in data table to handle such data in R. You can also use the famous library dplyr instead to benefit from its rich toolset. In this post, you will learn how dplyr can help you explore […]
One reason people would like to use RStudio for their work is because of the R Markdown. This made the RStudio not only an IDE for programming in R, but also a notepad in which they could put down their thoughts with R code and results. In this post, you will learn how to use […]
R as a data analytics platform is expected to have a lot of support for various statistical tests. In this post, you are going to see how you can run statistical tests using the built-in functions in R. Specifically, you are going to learn: What is t-test and how to do it in R What […]
Whether working on a machine learning project, a simulation, or other models, you need to generate random numbers in your code. R as a programming language, has several functions for random number generation. In this post, you will learn about them and see how they can be used in a larger program. Specifically, you will […]
