"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.
Please Note: I reserve the rights of all the media used in this blog -- photographs, animations, videos, etc. they are my work (except the 7 mentioned artworks by artists which were used as style images). GIFs might take a while to load, please be patient. If that is the case please open in browser instead. The world today doesn't make sense, so why should I paint pictures that do? -- Pablo Picasso Here are the results, some combinations produced astounding artwork. Here's an image of a bride & graffiti, combining them results in an output similar to doodle painting. Here, you can see the buildings being popped up in the background.
The government aims to put a facial recognition system into practical use to prevent new coronavirus infections at large-scale events including the Tokyo Olympics and Paralympics, it was learned Friday. The government also hopes to improve the national capacity to conduct saliva-based polymerase chain reaction tests to simultaneously detect cases of influenza and novel coronavirus infection, informed sources said. The proposals are included in a draft program for developing new technologies for preventing coronavirus infection. The government will unveil the program shortly and carry out demonstration tests at relevant ministries and agencies. According to the draft, the government is looking at using security cameras equipped with a facial recognition system to record the movements of visitors to the Tokyo Games, which were postponed to 2021, and other large-scale events, the sources said.
YI Technology (YI), the global provider of advanced, intelligent imaging technologies and products, announced the launch of the Kami Mini Indoor Camera, a tiny indoor security camera with a big brain, and the YI Dome Camera U, a complete coverage home security camera with extra privacy features. Both home security solutions are powered by YI Technology's proprietary, EDGE-based artificial intelligence (AI) technology which offers the most advanced feature set at the lowest possible price point to make smart home technology more accessible to the masses. YI has pioneered the development of a series of AI-enabled camera processing integrated circuits (ICs) which power Kami Mini and YI Dome Camera U. The sophisticated System-On-a-Chip (SoC) processing leverages Edge-based AI allowing for more advanced computing, fewer false alerts, and guaranteed access to AI features whether or not the user has a cloud subscription. Embedding the advanced AI chip in the camera also reduces detection latency because detection happens directly on the device rather than sending it to the cloud and waiting for a return signal. Recommended AI News: Cryptocurrency Prodigy, Joseph "PlugWalkJoe" O'Connor, Is Helping People Everywhere Master Crypto AI-Based Alerts & Face Detection: Equipped with the latest Edge Computing enabled chip, users can review all the faces that appear in their clips directly in the YI Home or Kami Home Apps.
Before we dive into the various CV techniques, let's explore the human body part that computer vision is trying to emulate in terms of functionality. Most humans don't give much thought to vision; it's a bodily function that automatically works with little to no deliberate influence. The human vision sensory system has developed over thousands of years to provide humans with the ability to extrapolate scenery meaning and context from the light that is reflected by objects in our 3-dimensional world, into our eyes. Our eyes and brain can infer an understanding of environments from reflected light. Our visual system equips us with the ability to determine the distance of objects, predict the texture of objects without directly touching, and identify all sort of patterns and events within our environment.
Computer vision (CV) is a major task for modern Artificial Intelligence (AI) and Machine Learning (ML) systems. It's accelerating nearly every domain in the tech industry enabling organizations to revolutionize the way machines and business systems work. Academically, it is a well-established area of computer science and many decades worth of research work have gone into this field. However, the use of deep neural networks has recently revolutionized the CV field and given it new oxygen. There is a diverse array of application areas for computer vision.
Artificial Intelligence is transforming the world at a rapid and accelerating pace, offering huge potential, but also posing social and economic challenges. Human beings are naturally fearful of machines – this is a constant. Technological advancements tend to outpace cultural shifts. It has taken the shock of a global pandemic to accelerate the uptake of many technologies that have been around for at least a decade. Unsurprisingly, much of the public discussion on AI has focused on recent controversies around facial recognition, automated decision-making and exam algorithms.
We all are moving towards an era of Artificial Intelligence. Earlier when face recognition was something to be amazed at it is now easily implemented using existing libraries and frameworks. Machine learning is now embedded into our lives and it is thickening its grasp with time. Earlier it was a buzzword but now it is a reality that is making our lives easier and better. So let's talk about some of the problems with Machine Learning.
OpenCV (Open source computer vision) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source BSD license. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc.
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