Computer Vision

Computer vision technology is one of the more remarkable AI applications. It actually helped spark the current AI revolution in 2012, when researchers used a deep neural network to radically improve the ability to recognize and classify objects in images. Since then, neural nets have surpassed even human abilities in many areas.

Companies are now investing billions of dollars in computer vision research and product development. We’re seeing companies like IBM and Pinterest driving innovative use cases in computer vision applications. These include security cases, like detecting human faces with startling accuracy, even from lamp-posts as they speed down the highway in their cars; to ecommerce, where retail companies can let you search for a pair of jeans your favorite celebrity was wearing — simply by uploading an image.

Computer vision’s potential is suddenly clear across a broad swath of industries, making it one of the fastest-growing trends. You’ll find computer vision enhancing search engines, offline retail companies engaging with in-store shoppers, and even in use with smart fridges. And at Transform 2019 you’ll learn how facial recognition has been harnessed for drones and video surveillance, but also to make in-store advertising uncannily personalized, connected cars a reality, improve healthcare diagnostics, and more.

But safety and privacy are important trends too. Also, the deep learning behind computer vision is famous for its black-box decision making, so smart companies using “explainable AI” to makes sure the machines don’t run amok.

Join us as we dig in to how computer vision applications — and the five other big AI trends of 2019 — are changing the way businesses of every size are driving value, customer service, and unprecedented returns on investment, right here and now.

Source: Venture Beat

AI in computer vision market is Growing Vigorously at a CAGR of +51% to 2026 with Major Vendors: Apple, Alphabet, Microsoft, Facebook, Wikitude, Xilinx

Computer vision is a branch of automation and computer science that enables the observation of machine vision and improves methods for creating systems that gel information from images. The rising demand for computer vision systems in non-traditional and developing applications and rising demand for edge computing in mobile devices are among the factors that drive market growth. With the increase in labor cost in the security market and use of robotics in the health industry, AI in computer vision systems are being used for various applications, Such as controlling processes of industrial robots, in navigations for autonomous vehicles, detecting events, organization information, medical image analysis, automatic inspection in manufacturing processes and Human-computer interactions, etc.

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The recently released report by Report Consultant titled as Global AI in computer vision market is a detailed analogy that gives readers an insight into intricacies of various elements like growth rate, technological developments, and impact of socio-economic conditions that affect the market space. An in-depth study of these numerous components is essential as all these aspects need to blend-in seamlessly for businesses to achieve success in this industry.

AI in computer vision market research report offers deep-dive analysis on recent market trends and technological aggrandizement that impact growth in the Market, and the Market is thriving by Major Vendors are:

  • Qualcomm
  • Apple
  • Alphabet
  • Microsoft
  • Facebook
  • Wikitude
  • Xilinx
  • Basler
  • Teledyne Technologies
  • Cognex
  • General Electric
  • Avigilon
  • Intel

AI in computer vision market Segmentation by Vertical: Automotive, Sports and Entertainment, Consumer, Robotics and Machine Vision, Healthcare, Others

Geographically, a well-developed infrastructure of the global AI in computer vision market, its awareness, regulatory framework are some of the factors that are driving the North Americans, Europe, Asia-Pacific, Middle East & Africans, and Latin Americans global market.

Source: A Market Research Report

Leveling the bars of human vision, the interdisciplinary field of AI is making remarkable disruptions in the business world. Dealing with how the machine can be made to attain high-level understanding from digital visuals, computer vision actually aided the spark of AI revolution. If we observe from the perspective of engineering, the technology automates the task that human visual structure can do.

After the ignition of the AI revolution in 2012, researchers and scientists employed a deep neural network to radically improve the capability of machines to identify and categorize objects in an image. Post that neural networks have outshined even human capabilities in varied areas.

More and more organizations are making hefty investments in technology’s research and development work. Companies including IBM and Pinterest deploying innovative use cases in computer vision applications. These applications include security cases – detecting human face with accuracy from a distance even if the person is driving a car, in e-commerce sector where one can find products of their choice just uploading an image of it.

Computer Vision has surely become a fast-growing trend driving the potential growth of companies deploying it. The application of computer vision can be witnessed in several places – enhancing search engines, offline retail engaging with in-store buyers and even in smart fridges.

Computer Vision-driven facial recognition has paved ways for drones and video surveillance, personalized in-store advertising, connecting cars to reality, improvement in the medical sector and others.

With all this greatness also comes the drawback of such marvelous technologies – Computer vision underpinned with deep learning has fame for making black box decision. This compels organizations to employ explainable AI to make sure that machines do not execute uncontrollably. After all safety and privacy is also an important aspect to bring into consideration.

Source: Analytics Insight

Even though early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s, today the applications for computer vision have grown exponentially. By 2022, the computer vision and hardware market is expected to reach $48.6 billion. It is such a part of everyday life you likely experience computer vision regularly even if you don't always recognize when and where the technology is deployed. Here is what computer vision is, how it works and seven amazing examples in practice today. 

7 Amazing Examples Of Computer And Machine Vision In Practice

7 Amazing Examples Of Computer And Machine Vision In Practice


What is Computer Vision (CV)? 

Computer vision is a form of artificial intelligence where computers can “see” the world, analyze visual data and then make decisions from it or gain understanding about the environment and situation. One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better. Our world has countless images and videos from the built-in cameras of our mobile devices alone. But while images can include photos and videos, it can also mean data from thermal or infrared sensors and other sources. Along with a tremendous amount of visual data (more than 3 billion images are shared online every day), the computing power required to analyze the data is now accessible and more affordable. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification. In less than a decade, today’s systems have reached 99 percent accuracy from 50 percent making them more accurate than humans at quickly reacting to visual inputs. 

How Does Computer Vision Work? 

One of the critical components to realizing all the capabilities of artificial intelligence is to give machines the power of vision. To emulate human sight, machines need to acquire, process and analyze and understand images. The tremendous growth in achieving this milestone was made thanks to the iterative learning process made possible with neural networks. It starts with a curated dataset with information that helps the machine learn a specific topic. If the goal is to identify videos of cats as it was for Google in 2012, the dataset used by the neural networks needs to have images and videos with cats as well as examples without cats. Each image needs to be tagged with metadata that indicates the correct answer. When a neural network runs through data and signals it's found an image with a cat; it's the feedback that is received regarding if it was correct or not that helps it improve. Neural networks are using pattern recognition to distinguish many different pieces of an image. Instead of a programmer defining the attributes that make a cat such as having a tail and whiskers, the machines learn from the millions of images uploaded. 

7 Amazing Examples of Computer Vision 

Imagine all the things human sight allows and you can start to realize the nearly endless applications for computer vision. Here are some of the most exciting examples of computer vision in practice today: 

  1.      Autonomous vehicles

Computer vision is necessary to enable self-driving cars. Manufacturers such as Tesla, BMW, Volvo, and Audi use multiple cameras, lidar, radar, and ultrasonic sensors to acquire images from the environment so that their self-driving cars can detect objects, lane markings, signs and traffic signals to safely drive.   

  1.       Google Translate app

All you need to do to read signs in a foreign language is to point your phone’s camera at the words and let the Google Translate app tell you what it means in your preferred language almost instantly. By using optical character recognition to see the image and augmented reality to overlay an accurate translation, this is a convenient tool that uses computer vision. 

  1.       Facial recognition

China is definitely on the cutting edge of using facial recognition technology, and they use it for police work, payment portals, security checkpoints at the airport and even to dispense toilet paper and prevent theft of the paper at Tiantan Park in Beijing, among many other applications. 

  1.       Healthcare

Since 90 percent of all medical data is image based there is a plethora of uses for computer vision in medicine. From enabling new medical diagnostic methods to analyze X-rays, mammography and other scans to monitoring patients to identify problems earlier and assist with surgery, expect that our medical institutions and professionals and patients will benefit from computer vision today and even more in the future as its rolled out in healthcare

  1.       Real-time sports tracking

Ball and puck tracking on televised sports has been common for a while now, but computer vision is also helping play and strategy analysis, player performance and ratings, as well as to track the brand sponsorship visibility in sports broadcasts

  1.       Agriculture

At CES 2019, John Deere featured a semi-autonomous combine harvester that uses artificial intelligence and computer vision to analyze grain quality as it gets harvested and to find the optimal route through the crops. There’s also great potential for computer vision to identify weeds so that herbicides can be sprayed directly on them instead of on the crops. This is expected to reduce the amount of herbicides needed by 90 percent

  1.       Manufacturing

Computer vision is helping manufacturers run more safely, intelligently and effectively in a variety of ways. Predictive maintenance is just one example where equipment is monitored with computer vision to intervene before a breakdown would cause expensive downtime.  Packaging and product quality are monitored, and defective products are also reduced with computer vision.

 There is already a tremendous amount of real-world applications for computer vision, and the technology is still young. As humans and machines continue to partner, the human workforce will be freed up to focus on higher-value tasks because the machines will automate processes that rely on image recognition. 

Source: Forbes

Japanese startup’s software detects suspicious behavior

Technology can also be used for security, suicide prevention

It’s watching, and knows a crime is about to take place before it happens.

Vaak, a Japanese startup, has developed artificial intelligence software that hunts for potential shoplifters, using footage from security cameras for fidgeting, restlessness and other potentially suspicious body language.
While AI is usually envisioned as a smart personal assistant or self-driving car, it turns out the technology is pretty good at spotting nefarious behavior. Like a scene out of the movie “Minority Report,” algorithms analyze security-camera footage and alert staff about potential thieves via a smartphone app. The goal is prevention; if the target is approached and asked if they need help, there’s a good chance the theft never happens.
Vaak made headlines last year when it helped to nab a shoplifter at a convenience store in Yokohama. Vaak had set up its software in the shop as a test case, which picked up on previously undetected shoplifting activity. The perpetrator was arrested a few days later.
“I thought then, ‘Ah, at last!’” said Vaak founder Ryo Tanaka, 30. “We took an important step closer to a society where crime can be prevented with AI.”

Shoplifting cost the global retail industry about $34 billion in lost sales in 2017 — the biggest source of shrinkage, according to a report from Tyco Retail Solutions. While that amounts to approximately 2 percent of revenue, it can make a huge difference in an industry known for razor-thin margins.

The opportunity is huge. Retailers are projected to invest $200 billion in new technology this year, according to Gartner Inc., as they become more open to embracing technology to meet consumer needs, as well as improve bottom lines.

“If we go into many retailers whether in the U.S. or U.K., there are very often going to be CCTV cameras or some form of cameras within the store operation,” said Thomas O’Connor, a retail analyst at Gartner. “That’s being leveraged by linking it to an analytics tool, which can then do the actual analysis in a more efficient and effective way.”

Because it involves security, retailers have asked AI-software suppliers such as Vaak and London-based Third Eye not to disclose their use of the anti-shoplifting systems. It’s safe to assume, however, that several big-name store chains in Japan have deployed the technology in some form or another. Vaak has met with or been approached by the biggest publicly traded convenience-store and drugstore chains in Japan, according to Tanaka.

Vaak's AI Software That Unmasks Shoplifters

Ryo Tanaka

Photographer: Akio Kon/Bloomberg

Big retailers have already been adopting AI technology to help them do business. Apart from inventory management, delivery optimization and other enterprise needs, AI algorithms run customer-support chatbots on websites. Image and video analysis is also being deployed, such as Inc.’s Echo Look, which gives users fashion advice.

“We’re still just discovering all the market potential,” Tanaka said. “We want to keep expanding the scope of the company.”

Founded in 2017, Vaak is currently testing in a few dozen stores in the Tokyo area. The company began selling a market-ready version of its shoplifting-detection software this month, and is aiming to be in 100,000 stores across Japan in three years. It has 50 million yen ($450,000) in funding from SoftBank Group Corp.’s AI fund, and is in the middle of its series A round, seeking to raise 1 billion yen.

What makes AI-based shoplifting detection a straightforward proposition is the fact that most of the hardware — security cameras — is usually already in place.

“Essentially this is using something that’s been underutilized for decades,” said Vera Merkatz, business development manager at Third Eye. Founded in 2016, the startup offers services similar to Vaak in the U.K. market, where it has a deal with a major grocery chain. Third Eye is looking to expand into Europe.

The ability to detect and analyze unusual human behavior also has other applications. Vaak is developing a video-based self-checkout system, and wants to use the videos to collect information on how consumers interact with items in the store to help shops display products more effectively. Beyond retail, Tanaka envisions using the video software in public spaces and train platforms to detect suspicious behavior or suicide jumpers. At Third Eye, Merkatz said she’s been approached by security management companies looking to leverage their AI technology.

“The potential is broad since it can be applied outside of shoplifting prevention and outside of retail — such as with manufacturing or other types of marketing,” said Hiroaki Ando, a retail consultant at Ernst & Young Advisory & Consulting Co. in Tokyo.

Source: Bloomberg

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