Most of us carry a spectacularly powerful computer in our pocket, which we rarely use for much more than web browsing, social media, and maybe the occasional phone call. Our mobile phones are technological miracles, but their potential sometimes seems wasted.
It’s always a pleasure to see something that makes use of a mobile phone to drive some nuts-and-bolts hardware. [Jose Julio]’s project does just that, using the phone as the brains behind a robotic air hockey table.
Readers with long memories will remember previous air hockey tables from [Jose], using 3D printer components controlled by an Arduino Mega with a webcam suspended above the field of play. This version transfers camera, machine vision, and game strategy to an Android app, leaving the Arduino to control the hardware under wireless network command from above.
The result you can see in the video below the break is an extremely fast-paced game, with the robot looking unbeatable. If you want to build your own there are full instructions and code on GitHub, or if you follow the link from the page linked above, he sells the project as a kit.
Continue reading “Smartphone Will Destroy You at Air Hockey”
It’s getting easier and easier to add machine intelligence to your hacks, even to the point where you sometimes don’t have to install any special software. In this case [Dexter Industries] has added the ability to read human emotions to their EmpathyBot robot by making use of Google Cloud Vision.
Press a button on the robot and it moves forward until it’s a certain distance from an object. It then takes a picture and sends it off to Google Cloud Vision along with a request to do face detection. The response that Google returns is in JSON format and, if it finds a face, includes the likelihood of the face being happy, sad, sorrowful or surprised. The robot parses that response and gives an appropriate canned speech using the text-to-speech software, eSpeak e.g. “You seem happy! Tell me why you are so happy!”.
[Dexter] has made the source code available on github. It’s written in python and is easy to read by anyone with even just a little programming experience. The video after the break gives a number of demonstrations, including some with non-human subjects.
Continue reading “Raspberry Pi Robot That Reads Your Emotions”
If you have been off trick-or-treating and returned home with an embarassment of candy, what on earth can you do to mange the problem and sort it by brand?
Yes, it’s an issue that so many of us have had to face at this time of year. So much a challenge, that the folks at [Dexter Industries] have made a robotic candy-sorter to automate the task.
OK, there’s something of the tongue-in-cheek about the application. But the technology they’ve used is interesting, and worth a second look. Hardware wise it’s a Lego Mindstorms conveyor and hopper controlled by a Raspberry Pi through the BrickPi interface. All very well, but it’s in the software that the interest lies. They use the Raspberry Pi’s camera to take a picture to send off to Google Cloud Vision, which they then query to return a guess at the brand of the candy in question. The value returned is then compared to a list of brands to keep or donate to another family member, and the hopper tips the bar into the respective pile. They provide full build details and code, as well as the video we’ve put below the break. So simple a child can explain it, sort of.
Continue reading “Sort Your Candy With A Raspberry Pi And Google Cloud Vision”
If a picture is worth a thousand words, a video must be worth millions. However, computers still aren’t very good at analyzing video. Machine vision software like OpenCV can do certain tasks like facial recognition quite well. But current software isn’t good at determining the physical nature of the objects being filmed. [Abe Davis, Justin G. Chen, and Fredo Durand] are members of the MIT Computer Science and Artificial Intelligence Laboratory. They’re working toward a method of determining the structure of an object based upon the object’s motion in a video.
The technique relies on vibrations which can be captured by a typical 30 or 60 Frames Per Second (fps) camera. Here’s how it works: A locked down camera is used to image an object. The object is moved due to wind, or someone banging on it, or any other mechanical means. This movement is captured on video. The team’s software then analyzes the video to see exactly where the object moved, and how much it moved. Complex objects can have many vibration modes. The wire frame figure used in the video is a great example. The hands of the figure will vibrate more than the figure’s feet. The software uses this information to construct a rudimentary model of the object being filmed. It then allows the user to interact with the object by clicking and dragging with a mouse. Dragging the hands will produce more movement than dragging the feet.
The results aren’t perfect – they remind us of computer animated objects from just a few years ago. However, this is very promising. These aren’t textured wire frames created in 3D modeling software. The models and skeletons were created automatically using software analysis. The team’s research paper (PDF link) contains all the details of their research. Check it out, and check out the video after the break.
Continue reading “Interactive Dynamic Video”
I’ve developed or have been involved with a number of imaging technologies, everything from DIY synthetic aperture radar, the MIT thru-wall radar, to the next generation of ultrasound imaging devices. Imagery is cool, but what the end-user often wants is some way by which to get an answer as opposed to viewing a reconstruction. So let’s figure that out.
We’re kicking-off a discussion on how to apply deep learning to more than just beating Jeopardy champions at their own game. We’d like to apply deep learning to hard data, to imagery. Is it possible to get the computer to accurately provide the diagnosis?
I helped to organize a seminar series/discussion panel in New York City on November 13th (you know, for those readers who are closer to New York than to Munich). This discussion panel includes David Ferrucci (the guy who lead the IBM Watson program), MIT Astrophysicist Max Tagmark, and the person who created genetic sequencing on a chip: Jonathan Rothberg. As the vanguard of creativity and enthusiasm in everything technical we’d like the Hackaday community to join the conversation.
Continue reading “Next Week in NYC: How the Age of Machine Consciousness is Transforming Our Lives”
[Gustaf] has been playing around with machine vision for a while and sent in his latest project in on our tip line. It’s a video based car radar system that can detect cars in a camera’s field of vision while cruising down the highway.
Like [Gustaf]’s previous experiments with machine vision where he got a computer to recognize and count yellow cylinders and green rectangles, the radar build uses ADABoost and the AForge AI/Machine Vision C# framework. [Gustef] used an evolutionary algorithm to detect the presence of a car in a video frame, first by selecting 150 images of cars from a pre-recorded video, and the another 1,850 images were selected by a computer and confirmed as a car by a human eye.
With 2000 images of cars in its database, [Gustaf]’s machine vision algorithm is able to detect a car in real-time as he drove down a beautiful Swedish highway. In addition to overlaying a rectangle underneath each car in a video frame and an awesome Terminator-style HUD in the upper right corner, [Gustaf] also a distance display above the hood of his car.
It’s an awesome build that makes us wonder if [Gustef] is building an autonomous car. Even if he’s not, it really makes us want to install a video HUD in our whip, just to see this in action.
It’s neat how a project from 2004 can still be relevant if it’s done really well. This is the case with AVRcam. It uses an Atmel AVR mega8 and can do some pretty impressive things, like track up to eight objects at 30fps. The hardware and software is also open source, so it should be possible to build one yourself. There are many projects like it on the internet, though often they require much beefier hardware. Although, these days you can fit a computer inside a match box, so we see more and more projects just throwing a full USB camera on a robot to do simple things like line following. It’s debatable which solution is more elegant, but maybe not which one is more impressive.