Fixed camera setup for object localization and measurement
A common task in Computer Vision is to use a camera for localize and measure certain objects in the scene. In the industry is common to use images of objects on a high contrast background and use Computer Vision algorithms to extract useful information. There's a lot of literature about the computer vision algorithm that we can use to extract the information, but something that's usually neglected is how to correctly setup the camera in order to correctly address the problem. This post aim is to shed light on this subject.
Tensorflow 2.0: models migration and new design
Tensorflow 2.0 will be a major milestone for the most popular machine learning framework: lots of changes are coming, and all with the aim of making ML accessible to everyone. These changes, however, require for the old users to completely re-learn how to use the framework: this article describes all the (known) differences between the 1.x and 2.x version, focusing on the change of mindset required and highlighting the pros and cons of the new implementation.
Understanding Tensorflow's tensors shape: static and dynamic
Describing computational graphs is just a matter connecting nodes correctly. Connecting nodes seems a trivial operation, but it hides some difficulties related to the shape of tensors. This article will guide you through the concept of tensor's shape in both its variants: static and dynamic.
Camera calibration guidelines
The process of geometric camera calibration (camera resectioning) is a fundamental step for machine vision and robotics applications. Unfortunately, the result of the calibration process can vary a lot depending on various factors. There are a lot of empirical guidelines that have to be followed in order to achieve good results: this post will drive you through them.