Anastasia Murzova
In this post, you will learn how to apply Background Subtraction with OpenCV and BGS Libraries
In the previous post, we’ve learned how to work with OpenCV Java API with the example of a PyTorch convolutional neural network, integrated into the Java pipeline. Now we are going
OpenCV library is widely used due to its extensive coverage of the computer vision tasks, and availability to involve it in various projects, including deep learning. Usually, OpenCV is used
In this post, we will learn how to convert a PyTorch model to TensorFlow. If you are new to Deep Learning you may be overwhelmed by which framework to use.
In this post, we will examine Otsu’s method for automatic image thresholding. What is Image Thresholding? Image thresholding is used to binarize the image based on pixel intensities. The input
In our recent post about receptive field computation, we examined the concept of receptive fields using PyTorch. We learned receptive field is the proper tool to understand what the network
In a previous post, we covered the concept of fully convolutional neural networks (FCN) in PyTorch, where we showed how we could solve the classification task using the input image
In this article, we will learn about autoencoders in deep learning. We will show a practical implementation of using a Denoising Autoencoder on the MNIST handwritten digits dataset as an
1. Deep Learning Frameworks Deep Learning is a branch of AI which uses Neural Networks for Machine Learning. In the recent years, it has shown dramatic improvements over traditional machine
