Siraj Raval has designed a free curriculum to help anyone learn Computer Vision in the most efficient way possible.

My curriculum starts off with low level vision techniques and progressively increases in difficulty until we get to high level analysis techniques i.e deep learning. Don’t worry if you’ve never coded before, i’ve included links to help you learn Python as well. Now is the time to build computer vision solutions, the world needs these menial tasks automated to help liberate humans from drudgery. The tools needed are python, OpenCV, and Tensorflow, all of which have their place and I’ll explain all the details of how it fits together in this video. Enjoy!

Here’s a great article on R-CNN, object detection, and the ins and outs of computer vision.

After exploring CNN for a while, I decided to try another crucial area in Computer Vision, object detection. There are several methods popular in this area, including Faster R-CNN, RetinaNet, YOLOv3, SSD and etc. I tried Faster R-CNN in this article. Here, I want to summarise what I have learned and maybe give you a little inspiration if you are interested in this topic.

The Raspberry Pi 4 could not have come at a better time and now is the moment for new developers to start experimenting with the technology. This powerful, yet tiny, computer can be used for a variety of functions, but our focus today will be on using the Pi 4 for image processing in a small package and low power setting.

The computing power of the Raspberry Pi 4 is higher compared to previous generations. This means that it can perform inference fairly quickly. It can be used for various types of applications. These include a rock-paper-scissors detection machine, home surveillance through motion detection, object detection for authorized entry (pet vs. animal) or even to give vision to a robot.

Here’s an interesting computer vision / IoT project you can make at home.

The JeVois machine vision sensor can recognize a wide variety of objects and symbols. My own project, Hedley the Robotic Skull , uses one to track me as I walk around in his field of view. The sensor communicates with an Arduino microcontroller, which moves the pan servo to […]

Here’s a unique (and unexpected) use of computer vision.

People take their tabletop games very, very seriously. [Andrew Lauritzen], though, has gone far above and beyond in pursuit of a fair game. The game in question is Star War: X-Wing , a strategy wargame where miniature pieces are moved according to rolls of the dice. [Andrew] suspected that […]

While readers of this blog may think that computer vision and neural networks have a long history together, the fact is: they don’t. Machine vision encompasses far more than Hot Dog or Not a Hot Dog. Here’s an interesting look at how deep learning has changed machine vision forever.

Underneath this hyperbole, however, describing the underlying science behind such concepts is more simple. In traditional machine vision systems, for example, it may be necessary to read a barcode on a part, judge its dimensions, or inspect it for flaws. To do so, systems integrators often use off-the-shelf softwarethat offers standard tools that can be deployed to determine a data matrix code, for example, or caliper tools set using graphical user interfaces to measure part dimensions.