As a result of analyzing more than 7000 photos in 21 categories of the online museum collection, it has been pointed out that the proportion of gray is gradually increasing in the world.

The most common color among the 7083 items targeted was 'charcoal gray'. Charcoal gray appeared in 80% of the photos surveyed, but in most cases it was in only a few pixels. Here is a diagram showing how the colors of various objects changed over time from 1800 to 2020. One of the points is that gray is increasing while brown and yellow are decreasing. thing. It also shows that highly saturated colors have been used since the 1960s. As an example, many of the 1844 telephones were made of mahogany, so there are a lot of brown and yellow ingredients. Presented as a 2008 phone, the iPhone is mostly gray and black. If you look at the color trends, you can see the trends of 'hidden colors' that you can't understand just by looking at them. Below are images of the 'pocket watch' in the dataset, with blue bands below each image to indicate that each photo contains a small amount of blue pixels. Depending on the watch, there are cases where the hands contain blue, but there are also examples where rust-resistant screws called bluing screws are used, and it is believed that blue was detected. .

After five years of offering unlimited free photo backups at “high quality,” Google Photos will start charging for storage once more than 15 gigs on the account have been used. The change will happen on June 1st, 2021, and it comes with other Google Drive policy changes like counting Google Workspace documents and spreadsheets against the same cap. Google is also introducing a new policy of deleting data from inactive accounts that haven’t been logged in to for at least two years.

All photos and documents uploaded before June 1st will not count against that 15GB cap, so you have plenty of time to decide whether to continue using Google Photos or switching to another cloud storage provider for your photos. Only photos uploaded after June 1st will begin counting against the cap.

Google already counts “original quality” photo uploads against a storage cap in Google Photos. However, taking away unlimited backup for “high quality” photos and video (which are automatically compressed for more efficient storage) also takes away one of the service’s biggest selling points. It was the photo service where you just didn’t have to worry about how much storage you had.

La police patrouille au delà du grand public : une des dernières tendances en marketing digital — avoir sa propre fonte — dépasse en effet le strict cadre du "B2C".

The announcement describes two new Intelligent Vision CMOS chip models, the Sony IMX500 and IMX501. From what I can tell these are the same base chip, except that the 500 is the bare chip product, whilst the 501 is a packaged product.

They are both 1/2.3” type chips with 12.3 effective megapixels. It seems clear that the one of the primary markets for the new chip is for security and system cameras. However having AI processes on the chip offers up some exciting new possibilities for future video cameras, particularly those mounted on drones or in action cameras like a GoPro or Insta 360.

One prominent ability of the new chip lies in functions such as object or person identification. This could be via tracking such objects, or in fact actually identifying them. Output from the new chip doesn’t have to be in image form either. Metadata can be output so that it can simply send a description of what it sees without the accompanying visual image. This can reduce the data storage requirement by up to 10,000 times.

For security or system camera purposes, a camera equipped with the new chip could count the number of people passing by it, or identifying low stock on a shop shelf. It could even be programmed to identify customer behaviour by way of heat maps.

For traditional cameras it could make autofocus systems better by being able to much more precisely identifying and tracking subjects. With AI systems like this, it could make autofocus systems more intelligent by identifying areas of a picture that you are likely to be focussing on. For example if you wanted to take a photograph of a flower, the AF system would know to focus on that rather than, say, the tree branch behind it. Facial recognition would also become much faster and more reliable.

Autofocus systems today are becoming incredibly good already, but if they were backed up by ultra fast on-chip object identification they could be even better. For 360 cameras, too, the ability to have more reliable object tracking metadata will help with post reframing.

As facial recognition technology gets cheaper, faster and more popular, people are looking for ways to subvert it. Reflectacles, a Chicago-based company, makes glasses that can thwart the technology.

“The announcement came totally out of the blue and I am very proud to accept the Turing Award,” said Hanrahan, who is the Canon Professor in the School of Engineering and a professor of computer science and of electrical engineering at Stanford University. “It is a great honor, but I must give credit to a generation of computer graphics researchers and practitioners whose work and ideas influenced me over the years.”

Hanrahan splits the award and its $1 million prize with one-time mentor and colleague Edward “Ed” Catmull, former president of Pixar and Disney Animation Studios. The pair’s recognition marks only the second time that the award has been given for computer graphics.

“All of us at Stanford are tremendously proud of Pat and his accomplishments, and I am delighted that he and his colleague Ed Catmull are being recognized with the prestigious Turing Award,” said Stanford President Marc Tessier-Lavigne. “Pat has made pioneering contributions to the field of computer graphics. His work has had a profound impact on filmmaking and has created new artistic possibilities in film, video games, virtual reality and more.”

In the late-1980s, Hanrahan was chief architect at Pixar under Catmull, where he was principally responsible for creating the now-famous RenderMan Interface, the 3D animation application used to make such beloved films as Toy Story, Finding Nemo, Cars and many more. RenderMan is still widely used today. Hanrahan is particularly proud of his work on RenderMan’s Shading Language, which brought dramatic, photorealistic lighting to computer animation for the first time. He has won three Academy Awards for technical achievement.

“Pat Hanrahan’s work is an extraordinary example of the kind of creative research for which Stanford Engineering is known,” said Jennifer Widom, dean of the Stanford School of Engineering and a colleague of Hanrahan’s in the departments of computer science and of electrical engineering. “His work on computer graphics and data visualization have transformed industries and fields, and I am thrilled he is receiving this recognition, which he so richly deserves.”

In a statement, ACM said Hanrahan and Catmull “fundamentally influenced the field of computer graphics through conceptual innovation and contributions to both software and hardware. Their work has had a revolutionary impact on filmmaking, leading to a new genre of entirely computer-animated feature films.”

If you love landscape photography you'll be blown away by the winners of the 2019 Landscape Photographer of the Year contest.

Netflix now supports the AV1 video codec in its Android app, which it says is 20 percent more efficient than the VP9 codec it currently uses. The codec can be enabled now for "selected titles" by selecting the app’s "Save Data" option.

An argument could be made that Google has over-indulged in its creation of way too many messaging apps in years past. But today’s launch of a new messaging service — this time within the confines of Google Photos — is an integration that actually makes sense.

The company is rolling out a way to directly message photos and chat with another user or users within the Google Photos app. The addition will allow users to quickly and easily share those one-off photos or videos with another person, instead of taking additional steps to build a shared album.

The feature itself is simple to use. After selecting a photo and tapping share, you can now choose a new option, “Send in Google Photos.” You can then tap on the icon of your most frequent contacts or search for a user by name, phone number or email.

The recipient will need a Google account to receive the photos, however, because they’ll need to sign in to view the conversation. That may limit the feature to some extent, as not everyone is a Google user. But with now a billion-some Google Photos users out there, it’s likely that more of the people you want to share with will have an account, rather than not.

You also can use this feature to start a group chat by selecting “New group,” then adding recipients.

Once a chat has been started, you can return to it at any time from the “Sharing” tab in Google Photos. Here, you’ll be able to see the photos and videos you each shared, comments, text chats and likes. You also can save the photos you want to your phone or tap on the “All Photos” option to see just the photos themselves without the conversations surrounding them.

A photographer captures the paths that birds make across the sky.

This story appears in the January 2018 issue of National Geographic magazine.
If birds left tracks in the sky, what would they look like? For years Barcelona-based photographer Xavi Bou has been fascinated by this question. Just as a sinuous impression appears when a snake slides across sand, he imagined, so must a pattern form in the wake of a flying bird. But of course birds in flight leave no trace—at least none visible to the naked eye. Bou, now 38, spent the past five years trying to capture the elusive contours drawn by birds in motion, or, as he says, “to make visible the invisible.”

First he had to shed the role of mere observer. “Like a naturalist, I used to travel around the world looking at wildlife,” he says. He began exploring photographic techniques that would allow him to express his love of nature and show the beauty of birds in a way not seen before.

Ultimately he chose to work with a video camera, from which he extracts high-resolution photographs. After he films the birds in motion, Bou selects a section of the footage and layers the individual frames into one image. He finds the process similar to developing film: He can’t tell in advance what the final result will be. There’s one magical second, he says, when the image—chimerical and surreal—begins to emerge.

Before Bou began this project, which he calls “Ornitografías,” he earned degrees in geology and photography in Barcelona, then worked as a lighting technician in the fashion industry and also co-owned a postproduction studio. This current work, he says, combines his passion and his profession. “It’s technical, challenging, artistic, and natural. It’s the connection between photography and nature that I was looking for.”

Are you reading this on a handheld device? There’s a good chance you are. Now imagine how’d you look if that device suddenly disappeared. Lonely? Slightly crazy? Perhaps next to a person being ignored? As we are sucked in ever more by the screens we carry around, even in the company of friends and family, the hunched pose of the phone-absorbed seems increasingly normal.

US photographer Eric Pickersgill has created “Removed,” a series of photos to remind us of how strange that pose actually is. In each portrait, electronic devices have been “edited out” (removed before the photo was taken, from people who’d been using them) so that people stare at their hands, or the empty space between their hands, often ignoring beautiful surroundings or opportunities for human connection. The results are a bit sad and eerie—and a reminder, perhaps, to put our phones away.

Micro-sized cameras have great potential to spot problems in the human body and enable sensing for super-small robots, but past approaches captured fuzzy, distorted images with limited fields of view.

Now, researchers at Princeton University and the University of Washington have overcome these obstacles with an ultracompact camera the size of a coarse grain of salt. The new system can produce crisp, full-color images on par with a conventional compound camera lens 500,000 times larger in volume, the researchers reported in a paper published Nov. 29 in Nature Communications.

Enabled by a joint design of the camera’s hardware and computational processing, the system could enable minimally invasive endoscopy with medical robots to diagnose and treat diseases, and improve imaging for other robots with size and weight constraints. Arrays of thousands of such cameras could be used for full-scene sensing, turning surfaces into cameras.

While a traditional camera uses a series of curved glass or plastic lenses to bend light rays into focus, the new optical system relies on a technology called a metasurface, which can be produced much like a computer chip. Just half a millimeter wide, the metasurface is studded with 1.6 million cylindrical posts, each roughly the size of the human immunodeficiency virus (HIV).

Each post has a unique geometry, and functions like an optical antenna. Varying the design of each post is necessary to correctly shape the entire optical wavefront. With the help of machine learning-based algorithms, the posts’ interactions with light combine to produce the highest-quality images and widest field of view for a full-color metasurface camera developed to date.

Description: https://twitter.com/physicsJ/status/1300748787011346432

Imagery from: NASA / Hubble / Cassini / JHUAPL / SwRI / Solar System Scope processing https://www.solarsystemscope.com/textures/ under CC BY 4.0
NASA ephemeris data: https://ssd.jpl.nasa.gov/horizons.cgi#top

Hybrid neural network can reconstruct Arabic or Japanese characters that it hasn’t seen before

Image analysis is ubiquitous in contemporary technology: from medical diagnostics to autonomous vehicles to facial recognition. Computers that use deep-learning convolutional neural networks—layers of algorithms that process images—have revolutionized computer vision.

But convolutional neural networks, or CNNs, classify images by learning from prior-trained data, often memorizing or developing stereotypes. They are also vulnerable to adversarial attacks that come in the form of small, almost-imperceptible distortions in the image that lead to bad decisions. These drawbacks limit the usefulness of CNNs. Additionally, there is growing awareness of the exorbitant carbon footprint associated with deep learning algorithms like CNNs.

One way to improve the energy efficiency and reliability of image processing algorithms involves combining conventional computer vision with optical preprocessors. Such hybrid systems work with minimal electronic hardware. Since light completes mathematical functions without dissipating energy in the preprocessing stage, significant time and energy savings can be achieved with hybrid computer vision systems. This emerging approach may overcome the shortcomings of deep learning and exploit the advantages of both optics and electronics.

In a recent paper published in Optica, UC Riverside mechanical engineering professor Luat Vuong and doctoral student Baurzhan Muminov demonstrate viability for hybrid computer vision systems via application of optical vortices, swirling waves of light with a dark central spot. Vortices can be likened to hydrodynamic whirlpools that are created when light travels around edges and corners.

Vivo has started teasing its next flagship phone, the X50. A video posted to Weibo shows off the camera module, which includes a periscope telephoto, two normal-looking lenses, and one much larger module that is presumably for the primary camera. The lens rotates as the module is manipulated by a robotic gimbal, suggesting the key feature here is image stabilization.

One of the big inclusions in Vivo’s Apex 2020 concept phone, which we weren’t able to see in person due to the COVID-19 pandemic, was “gimbal-like” stabilization on a 48-megapixel camera. Vivo said the design was inspired by chameleons’ eyes and is 200 percent more effective than typical OIS, allowing for longer nighttime exposures and smoother video. It looks like the X50 will be the first commercial deployment of this idea; another teaser videotouts the camera’s low-light ability.

Two key Pixel execs, including the computer researcher who led the team that developed the computational photography powering the Pixel’s camera, have left Google in recent months, according to a new report from The Information. The executives who left are distinguished engineer Marc Levoy and former Pixel general manager Mario Queiroz.

Queiroz had apparently already moved off the Pixel team two months before the launch of the Pixel 4 into a role that reported directly to Google CEO Sundar Pichai. However, he left in January to join Palo Alto Networks, according to The Information and his LinkedIn. Levoy left Google in March, which is also reflected on his LinkedIn.

There is a certain beauty in the expanses of public space, now vacant amid the pandemic, these photos from around the world show. There is also a reminder: True beauty comes when the builders roam the built.

We have created a new AI-powered tool using convolutional neural nets that can turn virtually any standard 2D picture into a 3D image. No depth information required.

From the "author : "This video has recently been getting a lot of attention from the media (something of a shock to me). In some articles they are crediting me for having done something unique, but in my opinion this is unfair. Anyone can repeat this process with algorithms that are currently published on Github; all of them are in the video description. Credit should go to DIAN, Topaz AI, ESRGAN, Waifu2x, DeOldify, Anime 4K and other developers who are part of the worldwide ML-community and contributing to humanity by making these algorithms publicly available."

From my time researching this topic I have come to the realisation that a lot of people don’t actually understand it, or only understand it partly. Also, many tutorials do a bad job in explaining in “lay-mans” terms what exactly it is doing, or leave out certain steps that would otherwise clear up some confusion. So i’m going to explain from start to finish in the most simplest way possible.

There are many approaches to implement facial detection, and they can be separated into the following categories:

Knowledge Based

• Rule based (Ex: X must have eyes, x must have a nose)
• Too many rules and variables with this method

Feature Based

• Locate and extract structural features in the face
• Find a differential between facial and non facial regions in an image

Appearance Based

• Learn the characteristics of a face
• Example: CNN’s
• Accuracy depends on training data (which can be scarce)

Template

• Using predefined templates for edge detection
• Quick and easy
• A trade off for speed over accuracy

The approach we are going to be looking at is a mix between feature based and template based. One of the easiest and fastest ways of implementing facial detection is by using Viola Jones Algorithm.

A specialist medical camera that measures just 0.65 x 0.65 x 1.158mm has just entered the Guinness Book of Records. The size of the grain of sand, it is the camera's tiny sensor that is actually being entered into the record book. 

The OmniVision OV6948 is just 0.575 x 0.575 x 0.232mm and produces a 40,000-pixel color image using an RGB Bayer back-side-illuminated chip. Each photosite measures just 1.75 µm across.

The resolution may seem low, but the OVM6948-RALA camera is designed to fit down the smallest of veins in the human body giving surgeons views that will aid diagnosis and with surgical procedures. Previously the surgeon would carry out these operations blind, or use a much lower-resolution fibre optic feed. 

Manufactured by California-based OmniVision Technologies Inc, the sensor captures its imagery at 30fps, and its analog output can be transmitted over distances of up to 4m with minimal noise. The camera unit offers a 120° super-wide angle of view - so something like a 14mm on a full-frame camera. It gives a depth of field range from 3mm to 30mm.