"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
This week, host Karen Han talks to voice actor and performer Erika Ishii, whose very long resume includes video games, animated series, and live action projects. In the interview, Erika explains their process of bringing video game characters to life–characters like Valkyrie in the game Apex Legends. Then Erika discusses diversity among both characters and performers in the video game industry and the ability to say no to projects that aren't the right fit. After the interview, Karen and co-host Isaac Butler talk about diversity in entertainment and the progress that has yet to be made. In the exclusive Slate Plus segment, Erika lists some of the voice actors and performances that have inspired them over the years.
Image Classification is one of the most fundamental tasks in computer vision. It has revolutionized and propelled technological advancements in the most prominent fields, including the automobile industry, healthcare, manufacturing, and more. How does Image Classification work, and what are its benefits and limitations? Keep reading, and in the next few minutes, you'll learn the following: Image Classification (often referred to as Image Recognition) is the task of associating one (single-label classification) or more (multi-label classification) labels to a given image. Here's how it looks like in practice when classifying different birds-- images are tagged using V7. Image Classification is a solid task to benchmark modern architectures and methodologies in the domain of computer vision. Now let's briefly discuss two types of Image Classification, depending on the complexity of the classification task at hand. Single-label classification is the most common classification task in supervised Image Classification.
Instagram and Facebook users in Texas lost access to certain augmented reality filters Wednesday, following a lawsuit accusing parent company Meta of violating privacy laws. In February, Texas Attorney General Ken Paxton revealed he would sue Meta for using facial recognition in filters to collect data for commercial purposes without consent. Paxton claimed Meta was "storing millions of biometric identifiers" that included voiceprints, retina or iris scans, and hand and face geometry. Although Meta argued it does not use facial recognition technology, it has disabled its AR filters and avatars on Facebook and Instagram amid the litigation. The AR effects featured on Facebook, Messenger, Messenger Kids, and Portal will also be shut down for Texas users.
Image segmentation is an aspect of computer vision that deals with segmenting the contents of objects visualized by a computer into different categories for better analysis. The contributions of image segmentation in solving a lot of computer vision problems such as analysis of medical images, background editing, vision in self driving cars and analysis of satellite images make it an invaluable field in computer vision. One of the greatest challenges in computer vision is keeping the space between accuracy and speed performance for real time applications. In the field of computer vision there is this dilemma of a computer vision solution either being more accurate and slow or less accurate and faster. PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code.
Lidar is an acronym for light detection and ranging. Lidar is like radar, except that it uses light instead of radio waves. The light source is a laser. A lidar sends out light pulses and measures the time it takes for a reflection bouncing off a remote object to return to the device. As the speed of light is a known constant, the distance to the object can be calculated from the travel time of the light pulse (Figure 1).
For years, tech companies have relied on something called the Fitzpatrick scale to classify skin tones for their computer vision algorithms. Originally designed for dermatologists in the 1970s, the system comprises only six skin tones, a possible contributor to AI's well-documented failures in identifying people of color. Now Google is beginning to incorporate a 10-skin tone standard across its products, called the Monk Skin Tone (MST) scale, from Google Search Images to Google Photos and beyond. The development has the potential to reduce bias in data sets used to train AI in everything from health care to content moderation. Google first signaled plans to go beyond the Fitzpatrick scale last year; internally, the project dates back to a summer 2020 effort to make AI "work better for people of color," according to a Twitter thread from Xango Eyeé, a responsible AI product manager at the company.
Companies are building software that uses AI to monitor people's behavior and interpret their emotions and body language in real life, virtually and even in the metaverse. But to develop that AI, they need fake data, and startups are stepping in to supply it. Synthetic data companies are providing millions of images, videos and sometimes audio data samples that have been generated for the sole purpose of training or improving AI models that could become part of our everyday lives in controversial forms of AI such as facial recognition, emotion AI and other algorithmic systems used to keep track of people's behavior. While in the past companies building computer vision-based AI often relied on publicly available datasets, now AI developers are looking to customized synthetic data to "address more and more domain-specific problems that have zero data you can actually access," said Ofir Zuk, co-founder and CEO of synthetic data company Datagen. Synthetic data companies including Datagen, Mindtech and Synthesis AI represent a corner of an increasingly compartmentalized AI industry.
A new phone app which offers users a free digital avatar is taking facial-recognition quality photographs and sending them to Moscow, prompting major concerns within the cyber security community. Tens of thousands of people have already uploaded their photographs to the servers of the New Profile Pic app in return to the free avatar. However, many will be unaware that the company behind the app, Linerock Investments, is based in an apartment complex overlooking the Moscow River, beside Russia's Ministry of Defence and just three miles from Red Square. Jake Moore, Global Cybersecurity Advisor, ESET Internet Security told MailOnline that people have to be incredibly careful when uploading photographs or personal data to a brand new website. He said: 'This app is likely a way of capturing people's faces in high resolution and I would question any app wanting this amount of data, especially one which is largely unheard of and based in another country.'
Charlette Désiré N'Guessan comes from an intriguing family, where all the women share the same name: Charlette. It is confusing, and also a little ironic, since she is a software engineer who has invented a facial recognition app. In The Charlettes, by filmmaker Gauz, we see how this particular Charlette has made an impact in the tech world in Ivory Coast and Ghana, winning prizes and plaudits for her artificial intelligence (AI) identity invention. Gbaka-Brede Armand Patrick, known professionally as Gauz, is a multidisciplinary and self-proclaimed iconoclastic artist, based in Ivory Coast.