Can You Remembrandt Where This Is From?

August 5, 2020 by Sharon Lin 8 Comments

A group of researchers have built an algorithm for finding hidden connections in artwork.

The team, comprised of computer scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Microsoft, used paintings from the Metropolitan Museum of Art and Amsterdam’s Rijksmuseum to demonstrate these hidden connections, which link artwork that shares similar styles, such as Francisco de Zurbarán’s The Martyrdom of Saint Serapion (above left) and Jan Asselijn’s The Threatened Swan (above right). They were initially inspired by the “Rembrandt and Velazquez” exhibition in the Rijksmuseum, which demonstrated similarities between the artists’ work despite the former hailing from the Protestant Netherlands and the latter from Catholic Spain.

The algorithm, dubbed “MosAIc”, differs from probabilistic generative adversarial network (GAN)-based projects that generate artwork since it focuses on image retrieval instead. Rather than focusing solely on obvious factors such as color and style, the algorithm also tries to uncover meaning and theme. It does this by constructing a data structure called a conditional k-nearest neighbor (KNN) tree, which provides a tree-like structure where branches off a central image indicate similarity to the image. In order to query the data structure, these branches are followed until the closest match to an image in a dataset is found. In further iterations, it prunes unpromising branches in order to improve its time for new queries.

Some results from running the algorithm against museum collections were finding similarities between the Dutch Double Face Banyan and a Chinese ceramic figurine, traced to the flow of porcelain and iconography from the Chinese to the Dutch in the 16th to 20th centuries.

A surprising result of this study was discovering that the approach could also be applied to find problems with deep nerual networks, which are used for creating deepfakes. While GANs can often have blind spots in their models, struggling to recreate certain classes of photos, MosAIc was able to overcome these shortcomings and accurately reproduce realistic images.

While the team admits that their implementation isn’t the most optimized version of KNN, their main objective was to present a broad conditioning scheme that is simple but effective for applications. Their hope is to inspire related researchers to consider multi-disciplinary applications for algorithms.

Facial Detection With Pi + MATLAB

August 4, 2020 by Orlando Hoilett 2 Comments

[Monica] wanted to try a bit of facial detection with her Raspberry Pi and she found some pretty handy packages in MATLAB to help her do just that. The packages are based on the Viola-Jones algorithm which was the first real-time object detection framework for facial detection.

She had to download MATLAB’s Raspbian image to allow the Pi to interpret MATLAB commands over a custom server. That setup is mostly pretty easy and she does a good job walking you through the setup on her project page.

With that, now she can control the Pi in MATLAB: configure the camera, toggle GPIO, etc. The real fun comes with the facial detection program. In addition to opening up a live video feed of the Pi camera, the program outputs pixel data. [Monica] was mostly just testing the stock capabilities, but wants to try detecting other objects next. We’ll see what cool modifications she’s able to come up with.

If MATLAB doesn’t quite fit your taste, we have a slew of facial detection projects on Hackaday.

Art Generated From The Dubious Comments Section

July 30, 2020 by Orlando Hoilett 5 Comments

[8BitsAndAByte] are back, and this time they’re taking on the comments section with art. They wondered whether or not they can take something as dubious as the comments section and redeem it into something more appealing like art.

They started by using remo.tv, a tool they’ve used in other projects, to read comments from their video live feeds and extract random phrases. The phrases are then analyzed by text to speech, and a publicly available artificial intelligence algorithm that generates an image from a text description. They can then specify art styles like modern, abstract, cubism, etc to give their image a unique appeal. They then send the image back to the original commenter, crediting them for their comment, ensuring some level of transparency.

We were a bit surprised that the phrase dog with a funny hat generated an image of a cat, so I think it’s fair to say that their AI engine could use a bit of work. But really, we could probably say that about AI as a whole.

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Argos Book Of Horrors

July 26, 2020 by Bryan Cockfield 14 Comments

If you live outside the UK you may not be familiar with Argos, but it’s basically what Americans would have if Sears hadn’t become a complete disaster after the Internet became popular. While they operate many brick-and-mortar stores and are a formidable online retailer, they still have a large physical catalog that is surprisingly popular. It’s so large, in fact, that interesting (and creepy) things can be done with it using machine learning.

This project from [Chris Johnson] is called the Book of Horrors and was made by feeding all 16,000 pages of the Argos catalog into a machine learning algorithm. The computer takes all of the pages and generates a model which ties the pages together into a series of animations that blends the whole catalog into one flowing, ever-changing catalog. It borders on creepy, both in visuals and in the fact that we can’t know exactly what computers are “thinking” when they generate these kinds of images.

The more steps the model was trained on the creepier the images became, too. To see more of the project you can follow it on Twitter where new images are released from time to time. It also reminds us a little of some other machine learning projects that have been used recently to create short films with equally mesmerizing imagery. Continue reading “Argos Book Of Horrors” →

Detect COVID-19 Symptoms Using Wearable Device And AI

July 11, 2020 by Orlando Hoilett 9 Comments

A new study from West Virginia University (WVU) Rockefeller Neuroscience Institute (RNI) uses a wearable device and artificial intelligence (AI) to predict COVID-19 up to 3 days before symptoms occur. The study has been an impressive undertaking involving over 1000 health care workers and frontline workers in hospitals across New York, Philadelphia, Nashville, and other critical COVID-19 hotspots.

The implementation of the digital health platform uses a custom smartphone application coupled with an Ōura smart ring to monitor biometric signals such as respiration and temperature. The platform also assesses psychological, cognitive, and behavioral data through surveys administered through a smartphone application.

We know that wearables tend to suffer from a lack of accuracy, particularly during activity. However, the Ōura ring appears to take measurements while the user is very still, especially during sleep. This presents an advantage as the accuracy of wearable devices greatly improves when the user isn’t moving. RNI noted that the Ōura ring has been the most accurate device they have tested.

Given some of the early warning signals for COVID-19 are fever and respiratory distress, it would make sense that a device able to measure respiration and temperature could be used as an early detector of COVID-19. In fact, we’ve seen a few wearable device companies attempt much of what RNI is doing as well as a few DIY attempts. RNI’s study has probably been the most thorough work released so far, but we’re sure that many more are upcoming.

The initial phase of the study was deployed among healthcare and frontline workers but is now open to the general public. Meanwhile the National Basketball Association (NBA) is coordinating its re-opening efforts using Ōura’s technology.

We hope to see more results emerge from RNI’s very important work. Until then, stay safe Hackaday.

Automating The Disinfection Of Large Spaces With Robots

June 30, 2020 by Maya Posch 31 Comments

What do you do when you have to disinfect an entire warehouse? You could send a group of people through the place with UV-C lamps, but that would take a long time as said humans cannot be in the same area as the UV-C radiation, as much as they may like the smell of BBQ chicken. Constantly repositioning the lamps or installing countless lamps would get in the way during normal operation. The answer is to strap UV-C lights to a robot according to MIT’s CSAIL, and have it ride around the space.

As can be seen in the video (also embedded after the break), a CSAIL group has been working with telepresence robotics company Ava Robotics and the Greater Boston Food Bank (GBFB). Their goal was to create a robotic system that could autonomously disinfect a GBFB warehouse using UV-C without exposing any humans to the harmful radiation. While the robotics can be controlled remotely, they can also map the space and navigate between waypoints.

While testing the system, the team used a UV-C dosimeter to confirm the effectiveness of this setup. With the robot driving along at a leisurely 0.22 miles per hour (~0.35 kilometer per hour), it was able to cover approximately 4,000 square feet (~372 square meter) in about half an hour. They estimated that about 90% of viruses like SARS-CoV-2 could be neutralized this way.

During trial runs, they discovered the need to have the robot adapt to the constantly changing layout of the warehouse, including which aisles require which UV-C depending on how full they are. Having multiple of these robots in the same space coordinate with each other would also be a useful feature addition.

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Autonomous Sentry Gun Packs A Punch And A Ton Of Build Tips

June 10, 2020 by Dan Maloney 17 Comments

What has dual compressed-air cannons, 500 roll-on deodorant balls, and a machine-learning brain with a bad attitude? We didn’t know either, until [Leo Fernekes] dropped this video on his autonomous robot sentry gun and saw it in action for ourselves.

Now, we’ve seen tons of sentry guns on these pages before, shooting everything from water to various forms of Nerf. And plenty of those builds have used some form of machine vision to aim the gun onto the target. So while it might appear that [Leo]’s plowing old ground here, this build is chock full of interesting tips and tricks.

It started when [Leo] saw a video on TensorFlow basics from our friend [Edje Electronics], which gave him the boost needed to jump into an AI project. The controller he ended up with looks for humans in the scene and slews the turret onto target, where the air cannons can do their thing. The hefty ammo is propelled by compressed air, which is dumped into the chamber using a solenoid valve with an interesting driver that maximizes the speed at which it opens. Style points go to the bacteriophage T4-inspired design, and to the sequence starting at 1:34 which reminded us of the factory scene from RoboCop.

[Leo] really put a ton of work into this project, and the results show. He is hoping to get an art gallery or museum to show it as an interactive piece to comment on one possible robot-human future, presumably after getting guests to sign a release. Whatever happens to it, the robot looks great and [Leo] learned a lot from it, as did we.

Continue reading “Autonomous Sentry Gun Packs A Punch And A Ton Of Build Tips” →