Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability.

Source: OpenAI's DALL·E 2 with prompt "a hyperrealistic picture of a robot reading the news on a laptop at a coffee shop" Welcome to the inaugural edition of Robo-Insight, a biweekly robotics news update! In this post, we are thrilled to present a range of remarkable advancements in the field, highlighting robotics progress in terrain traversability, shape morphing, object avoidance, mechanical memory, physics-based AI techniques, and new home robotics kits. Recently, researchers from the University of California San Diego have given four-legged robots forward-facing depth cameras to enable them to clearly analyze the environment around and below them. This data can also be compared with past images to estimate possible 3D transformation. Furthermore, their system is also self-checking, as it fuses information to give it a sort of short-term memory. Although the model does not guide the robot to a specific location, it enables the robot to traverse challenging terrain.

I think your personal vision comes through. When I was a kid, I used to play video games that felt so real and watch movies that depicted machinery and technology coming to life as if they were living in the real world. I wondered if it would ever actually happen in reality or if it was simply the graphics I was seeing at the time, which he found to be incredible…But when that boy himself chose to be a part of this field, his questions were answered. Computer vision is a blend of magic and science to create illusions of reality. I believe that if a machine is capable of looking at the surroundings and understanding the situation, it can respond to it in the same way that a human can.

He is an experienced engineer, programmer, and entrepreneur specializing in the integration of machine vision, robotics, and other automation technologies, with an extensive career in the industry. He was the founder, owner, and principal engineer for two successful vision systems integration firms. Prior to joining Landing AI he was Principal Vision Systems Architect with Integro Technologies, responsible for application evaluation and design of complex automated imaging solutions for inspection, metrology, and robotic guidance. Previously, he had served as Staff Engineer for Intelligent Robotics/Machine Vision at FANUC America Corporation. Mr. Dechow is a recipient of the A3 Automated Imaging Achievement Award honoring industry leaders for outstanding career contributions in industrial and/or scientific imaging.

Intelligent automation drives the best ROI for repetitive, standardized, and attention-demanding workflows. Claims management is a great example of such. Largely paper-based and rarely end-to-end digitized, the claims management process can eat up to 50%-80% of premiums' revenues. Being primarily manual, claims processing is also prone to errors and inefficiencies, which further drive up the insurers' operating costs. As McKinsey stated at the beginning of 2019, larger insurance carriers haven't quite addressed the costs of services delivery: In particular, the increase in connectivity--telematics and onboard computers in cars, smart home assistants, fitness trackers, healthcare wearables, and other types of IoT devices--now allows insurers to automatically collect more comprehensive data from customers.

LLC, a developer of customizable imaging and video technology, entered the race to develop the next generation of artificial intelligence vision technologies. The Xfuse proprietary high-performance Image Signal Processing (ISP) technology supports multiple different sensor types simultaneously to fuse data-rich HDR video in real-time with minimal latency. These location-aware data streaming technologies from the Xfuse Phoenix HDR ISP offer both in-house and independent engineers complete control over the critical imaging pipeline necessary to rapidly advance self-aware robotics, autonomous guided vehicles, machine vision, and more. To make the advancements necessary for full-autonomous vehicles to safely navigate roads, developers require higher levels of accuracy and reduction of errors in the complex data streaming from multiple sensors. The Xfuse team has more than seven years of expertise in developing the real-time ISPs for Multi-Processor System on Chip (MPSoC) devices, multi-sensor guidance modules, and their necessary software tools.

The purpose of computer vision is to realize "human-like vision" on a computer.

In today's changing energy landscape, business leaders recognize that innovation, new technology, and automation are fundamental to remain competitive. The electric power industry is continuing to move towards a cleaner, more reliable, and resilient grid. Computer Vision is one of the most mature AI technologies with a highly disruptive impact on the power and utilities industry. This article explores how the next-generation AI vision technology can help pave the way to increase operational efficiency, safety, and reliability in the electric power industry. The most popular applications include AI vision inspection and monitoring, foreign object detection, abnormal situation detection, and intelligent control of field personnel and operation behavior.

Businesses have had several transformation breakthroughs in the last two years as a result of the epidemic that was expected to happen in the following five years. The rate of technological adoption will continue to rise. It mostly comprises artificial intelligence (AI) and intelligent industrial automation. Even though businesses are still learning how to use various AI technologies, computer vision will continue to offer up new technical vistas for the hyper-digital dawn. Video intelligence, one of the most talked-about computer vision technologies, has a lot of practical applications.

One thing that humans are trying to get from technology is the human-level of intelligence. Whether it is computer vision or chatbots, they want machines to see and speak like humans. When the outlook seems pretty simple, the inner mechanism of computer vision technology is complex, especially, when we are aiming to make them see like humans. Researchers are working to make the gap between computer vision and human vision disappear. Machines have the ability to learn from datasets.