Historically, there have been many Deep Learning (DL) frameworks, like Theano, CNTK, Caffe2, and MXNet. Nowadays, they appear to be dead or dying, as just two frameworks heavily dominate the DL scene: Google TensorFlow (TF), which includes Keras; and PyTorch from Meta aka FaceBook. However, there is no reason to believe such a duopoly will persist forever. All the time, new DL frameworks are proposed.
This blog post covers some important aspects of deploying and running classical computer vision algorithms as well as convolutional neural networks in a web front-end. Please make sure you have read the first part of the blog post. This will definitely help you to follow all technical aspects much easier. How can you pass an image or a video frame from JS to C++ and back? We’ll give a minimal example.
Are you interested in Computer Vision (CV)? Probably yes, if you are reading this. If you read CV tutorials, you might have noticed that most of them are in Python. This applies to both traditional CV (without neural networks) and, even more, to deep learning (neural networks). Occasionally, CV tutorials use C++ instead of Python, but any other programming languages are very rare.
Automatic floor segmentation can serve many interesting purposes including mixed reality (MR) applications, interior design, entertainment, computation of available space in a room, or indoor robot navigation. In this project, we have been solving a problem of scene understanding and, in particular, determining which pixels of the image belong to the floor. The problem of floor segmentation is a good example of how the same task can be solved with classical computer vision algorithms or deep learning.
If you want to dig into Computer Vision (CV) but have no idea where to start, this beginner guide is for you. Here we recommend some sources which will come in handy for learning and understanding both the computer vision and deep learning basics.
A century ago, the very thought of machines being able to think, make complicated calculations, and come up with effective solutions to pressing problems was more of a figment of science fiction writer’s fantasy rather than a foreseeable reality.
Deep learning (DL) and neural networks are extremely widespread in different computer vision (CV) applications. Indeed, many typical problems (like object recognition or semantic segmentation) are effectively solved by convolutional neural networks (CNNs). In this article, we are going to discuss how to utilize CNNs on embedded devices. Neural networks today are ubiquitous. In particular, it is hard to imagine computer vision without them.
Computer vision (CV) and machine learning (ML) algorithms solve a tremendous amount of problems. However many businesses often do not understand what hardware to choose for running your favorite neural net or some advanced image and video processing pipelines. With this blog post, we start a series of articles about embedded vision and specific practical things you need to know before making your choice.