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Computer Vision Advances and Challenges

Computer Vision is a field of computer science using the technology of artificial intelligence. A part of robotics as artificial visual systems automatically processes images and videos. AI training lets the computers understand, identify, classify and interpret the digital images. Response from the machines to the images relies on the understanding of computer vision. The purpose of this technology is to automate the tasks consisting of human visual aspects.

Machines obtain information from images with computer vision technology. The input data processed by the vision sensor enables it to perform actions using high-level information. Machines can gain an understanding of the situations. AI uses pattern recognition and machine learning techniques that ease decision-making.
Computer Vision technology is now accessible and affordable for industries to adopt changes and extract benefits.

History:

Experimentation on computer vision began in the1950s and by 1970s; it could distinguish handwritten and typed text with optical character recognition. In 1966, a summer vision project to build a system that can analyze the scene and identify objects commenced at MIT. Initially, the project looked simple but to be decoded. The computer vision market is all set to reach a valuation of $48.32 billion by 2023. The estimation of the computer vision AI market, in 2019 for the healthcare industry is about $1.6billion.

Reason of popularity:

  1. Creation of a huge amount of visual data
  2. Improvement in mobile technology and computing power add to image data
  3. Its ability to process massive datasets
  4. Recognizing visual inputs faster than humans
  5. Accurate interpretation of images and videos
  6. Quick processing and high demand in robots across industries
  7. Defect detection assists corrective actions
  8. Analyze images on different parameters
  9. Maintain quality and safety
  10. Increases reliability and accuracy
  11. AI Training for computer vision
  12. New hardware and algorithms brought precision
  13. Cost-effective technology compared to other systems prevailing
  14. Automation, quality control, scrutiny is introduced
  15. Eases complicated industrial tasks
  16. Rise in online analysis of images
  17. Industries that widely use computer vision are automotive, aerospace, defense, education, healthcare, pharmaceuticals, food and packaging, beverages, manufacturing, government applications, etc.
Computer Vision

How does it work?

Machines understand process and analyze images with the information it can access on the topic. With the neural networks, the iterative learning process can be set. If you are looking forward to identifying the forest area all over the globe, the datasets used by neural networks require images and videos of green patches and dry patches. Tagged images and metadata helps the machine to reply correctly. Different pieces of image are recognized using pattern recognition by the neural networks.

Mainly the system uses various components of the machine vision system such as lens, image sensors, lighting, vision processing, and communication devices. Computers assemble visual images in bits like a puzzle put together. The pieces assembled into an image makes filtering and processing speedy. In the above example of identifying forests, the machines are not trained to see different tree types and leaves instead they are trained to recognize the green patches on earth. The training lets it create an image of the forest and match it with the data.

Deep Learning learns from large amounts of data and its algorithms are inspired by a human brain to result most accurately. This subset of machine learning can identify objects, people, tag friends, translate photos, translate voice, and translates text in multiple languages. Deep learning has transformed computer vision with its high level of accuracy that is beyond human capacity.

Difference between Computer Vision and Machine Learning:

Machine learning helps the computer to understand what they see and computer vision determines how they see. Machine learning is where the systems teach themselves based on the continuously populating data. CV requires artificial intelligence to train the system in performing varied tasks. CV does not learn from the training data available but makes data patterns to find relations between data and understand it for a visual representation of a preset result.
Computer vision is progressing towards replacing human vision that assists in complicated tasks. This requires intelligent algorithms and robust systems.

Examples of Computer Vision Applications:

Applications of Computer Vision

Augmented Reality:

  1. Geo Travel: Augmented Reality Geo Travel can be your travel guide, GPS enabled application gives you information on your exact location. Plan a trip for you using your searched data on the city with the result of Wikipedia pages that you can save for easy travel. Find a car with a car finder that saves your parking position for you to get back to your car easily.
  2. Web: The Augmented Web combines HTML5, Web Audio, WebGL, and WebRTC to improve the user experience when they visit existing pages. Image search, Google photos use face recognition, object recognition, scene recognition, geolocalization, Facebook takes care of image captioning, Google maps use aerial imaging and YouTube does content categorization with help of computer vision.

Automotive: In this field can save millions of people from tragic traffic accidents. Human error is possible due to multitasking, overthinking, tension and negligence. Self-driving cars are loaded with multiple cameras, radar, ultrasonic sensors and technology that detect 360-degree movement, developed by Google Labs. Tesla car warns drivers to take control of the steering wheel. The error proofing, presence, and absence of objects, responsible control on the machine all is possible with computer vision. Technology takes control by detecting objects, marks lanes, catches signs and understands traffic signals for us to drive safely.

Agriculture: Computer vision can check the quality of grain, identify weeds, and take actions to save crops by sprinkling herbicides on weeds using AI technology. It helps in the packaging of agricultural produce and products.

Healthcare and Medical Imaging: The computer vision technology helps healthcare professionals inaccurate presentation of data, reports, and illness-related information. It can save patients from getting improper treatments, study their medical data, which is image-based such as X-Rays, CT scans, sonography, mammography, and other monitoring activities of patients. Augmented Reality assisted surgery ensures better results than surgeries with human surveillance.

Get assistance in surgery from the analysis of various images with computer vision technology. Gauss Surgical is a blood monitoring solution that closely watches blood loss in real-time. It can save patients’ life during critical operations, facilitate blood transfusions, and make out hemorrhage. The images captured with help of iPad or Triton, processed by cloud-based computer vision and it estimates blood loss through intelligent machine learning algorithms. Computer vision can improve diagnosis ad automate pathology.

Smartphones: These handy tools for perfect pictures and AI are transforming the arena of development in computer vision. It scans QR codes, has portrait and panorama modes of photography. The face and smile detection, anti-blur technology is computer vision.

Insurance: Computer vision will compare the images of patients, reports and insurance forms to settle claims of hospitalization. In case of car or property insurance, this technology can analyze the damage, inspect the property and process claims. Automation in the insurance sector can result in speedy resolution of queries and settlements.

Manufacturing: Computer vision can predict the equipment maintenance, quality issues of product, monitor the production line and product quality to reduce the defects in manufacturing.

Google Translate App: Need to learn a foreign language just to travel for pleasure and leisure is eliminated with the introduction of computer vision. Pointing to a text or sign translates the foreign language in the selected output language. The accurate recognition of any sign is possible due to optical character recognition and augmented reality for exact translation.

Challenges of Computer Vision:

Challenges of Computer Vision
  1. The human visual system is too good to be simulated. The capacity of the human eye and brain in coordination with each other can recognize things, people and places are better. Computer systems can fail to recognize the faces with a variety of expressions or variant lighting.
  2. Initial research for industry-specific tasks can be expensive. The technology is changing rapidly but the complexities of integrating computer vision systems are a higher-level challenge.
  3. Face recognition is an annoyance and breach of privacy and business ethics in the hospitality, finance and banking industry. Multiple and adverse uses of technology are a threat and San Francisco has banned facial recognition.
    The algorithms for each talk about a particular industry may not be accurate or updated and the results may not match the preordained results.
  4. The misuse of computer vision is the result of faulty inputs or intentionally tampered images to form flawed patterns that harm the learning models.
  5. Object classification is challenging as the label is assigned to the entire image for classification. Handwritten documents are difficult for computer vision, due to a variety of handwriting styles, curves and shapes formed while writing for each alphabet.
  6. Object Detection is more complicated than image classification as there can be multiple objects in an image and the request can be for single objects or combinations.

Insufficient visual data sets or image reconstruction used to fill in for the missing parts of the image damages or corrupts the versions of photos.

Supposition:

Computer vision technology of Artificial Intelligence (AI) is witnessing a global rise in market revenues from $1700 million in 2015 to $5500 in 2019.

Image processing a subset of computer vision that performs to imitate the human vision and goes beyond human accuracy. It can enhance images by processing and making them identifiable for future use. Defect-free manufacturing, automotive, pharmaceuticals, overall many industries, products, and services is achievable. Increased adoption of computer vision AI-based technology is facilitating market growth.

The future of computer vision is accelerating and the image, photo and video data are growing enormously. The data upload, download and access are opening new opportunities for computer vision-based solutions.

Scope to improve performance and create a better user experience is a source of innovation towards the problem-solving capabilities of systems. The food industry will demonstrate the highest growth rate by applying computer vision technology in manufacturing and packaging operations.

The relationship of images and users is changing and the equation of visual data and its processing is harmonizing.

Top 7 ai trends in 2019

Artificial Intelligence is a method for making a system, a computer-controlled robot. AI uses information science and algorithms to mechanize, advance and discover worth escaped from the human eye. Most of us are pondering about “what’s next for AI in 2019 paving the way to 2020?” How about we explore the latest trends in AI in 2019.

AI-Enabled Chips

Companies over the globe are accommodating Artificial Intelligence in their frameworks however the procedure of cognification is a noteworthy concern they are confronting. Hypothetically, everything is getting more astute and cannier, yet the current PC chips are not good enough and are hindering the procedure.

In contrast to other programming technologies, AI vigorously depends on specific processors that supplement the CPU. Indeed, even the quickest and most progressive CPU may not be capable to improve the speed of training an AI model. The model would require additional equipment to perform scientific estimations for complex undertakings like identifying objects or items and facial recognition.

In 2019, Leading chip makers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will improve the execution speed of AI-based applications. Cutting edge applications from the social insurance and vehicle ventures will depend on these chips for conveying knowledge to end-users.

Augmented Reality

Augmented reality AI trend in 2019

Augmented reality (AR) is one of the greatest innovation patterns at this moment, and it’s just going to become greater as AR cell phones and different gadgets become increasingly available around the globe. The best examples could be Pokémon Go and Snapchat.

Objects generated from computers coexist together and communicate with this present reality in a solitary, vivid scene. This is made conceivable by melding information from numerous sensors such as cameras, gyroscopes, accelerometers, GPS, and so forth to shape a computerized portrayal of the world that can be overlaid over the physical one.

AR and AI are distinct advancements in the field of technology; however, they can be utilized together to make one of a kind encounters in 2019. Augmented reality (AR) and Artificial Intelligence (AI) advances are progressively relevant to organizations that desire to pick up a focused edge later on the work environment. In AR, a 3D portrayal of the world must be developed to enable computerized objects to exist close by physical ones. With companies such as Apple, Google, Facebook and so on offering devices and tools to make the advancement of AR-based applications simpler, 2019 will see an upsurge in the quantity of AR applications being discharged.

Neural Networks

A neural network is an arrangement of equipment as well as programming designed after the activity of neurons in the human cerebrum. Neural networks – most commonly called artificial neural networks are an assortment of profound learning innovation, which likewise falls under the umbrella of AI.

Neural networks can adjust to evolving input; so, the system produces the most ideal outcome without expecting to overhaul the yield criteria. The idea of neural networks, which has its foundations in AI, is quickly picking up prominence in the improvement of exchanging frameworks. ANN emulate the human brain. The current neural network advances will be enhanced in 2019. This would empower AI to turn out to be progressively modern as better preparing strategies and system models are created. Areas of artificial intelligence where the neural network was successfully applied include Image Recognition, Natural Language Processing, Chatbots, Sentiment Analysis, and Real-time Transcription.

The convergence of AI and IoT

IoT & AI trends in 2019

The most significant job AI will play in the business world is expanding client commitment, as indicated by an ongoing report issued by Microsoft. The Internet of Things is reshaping life as we probably are aware of it from the home to the workplace and past. IoT items award us expanded control over machines, lights, and door locks.

Organizational IoT applications would get higher exactness and expanded functionalities by the use of AI. In actuality, self-driving cars is certifiably not a commonsense plausibility without IoT working intimately with AI. The sensors utilized by a car to gather continuous information is empowered by the IoT.

Artificial intelligence and IoT will progressively combine at edge computing. Most Cloud-based models will be put at the edge layer. 2019 would see more instances of the intermingling of AI with IoT and AI with Blockchain. IoT is good to go to turn into the greatest driver of AI in the undertaking. Edge devices will be furnished with the unique AI chips dependent on FPGAs and ASICs.

Computer Vision

Computer Vision is the procedure of systems and robots reacting to visual data sources — most normally pictures and recordings. To place it in a basic way, computer vision progresses the info (yield) steps by reading (revealing) data at a similar visual level as an individual and along these lines evacuating the requirement for interpretation into machine language (the other way around). Normally, computer vision methods have the potential for a more elevated amount of comprehension and application in the human world.

While computer vision systems have been around since the 1960s, it wasn’t until recently that they grabbed the pace to turn out to be useful assets. Advancements in Machine Learning, just as the progressively skilled capacity and computational devices have empowered the ascent in the stock of Computer Vision techniques. What follows is also an explanation of how Artificial Intelligence is born. Computer vision, as a region of AI examines, has entered a far cry in a previous couple of years.

Facial Recognition

Facial recognition AI trends in 2019

Facial recognition is a type of AI application that aides in recognizing an individual utilizing their digital picture or patterns of their facial highlights. A framework utilized to perform facial recognition utilizes biometrics to outline highlights from the photograph or video. It contrasts this data and a huge database of recorded countenances to find the right match. 2019 would see an expansion in the use of this innovation with higher exactness and dependability.

In spite of having a lot of negative press lately, facial recognition is viewed as the Artificial Intelligence applications future because of its gigantic prominence. It guarantees a gigantic development in 2019. The year 2019 will observe development in the utilization of facial recognition with greater unwavering quality and upgraded precision.

Open-Source AI

Open Source AI would be the following stage in the growth of AI. Most of the Cloud-based advancements that we use today have their beginning in open source ventures. Artificial intelligence is relied upon to pursue a similar direction as an ever-increasing number of organizations are taking a gander at a joint effort and information sharing.

Open Source AI would be the following stage in the advancement of AI. Numerous organizations would begin publicly releasing their AI stacks for structuring a more extensive encouraging group of people of AI communities. This would prompt the improvement of a definitive AI open source stack.

Conclusion

Numerous innovation specialists propose that the eventual fate of AI and ML is sure. It is the place where the world is headed. In 2019 and beyond these advancements are going to support as more organizations come to understand the advantages. However, the worries encompassing the dependability and cybersecurity will keep on being fervently discussed. The ML and AI trends for 2019 and beyond hold guarantees to enhance business development while definitely contracting the dangers.

Drone Revolution | Blog | Bridged.co

It’s a bird, it’s a plane… Oh, wait it’s a Drone!

Also known as Unmanned Aerial Vehicles (UAVs), drones have no human pilot onboard and are controlled by either a person with a remote control/smartphone on the ground or autonomously via a computer program.

These devices are already popular in various industries like Defense, Film making and Photography and are gaining popularity in fields like Farming, Atmospheric research, and Disaster relief. But even after so much innovation and experimentation, we have not explored the full capacity of data gained from drones.

We at Bridged AI are aware of this fact and are contributing to this revolution by helping the drone companies in perfecting their models by providing them with curated training data.

Impact of Drones

Drones inspecting power lines

Drones are being used by companies like GE to inspect their infrastructure, including power lines and pipelines. They can be used by companies and service organizations to provide instant surveillance in multiple locations instantly.

Surveillance by drones

They can be used for tasks like patrolling borders, tracking storms, and monitoring security. Drones are already being used by some defense services.

Border patrolling
Drones surveying farms

In agriculture, drones are used by farmers to analyze their farms for keeping a check on yield, unwanted plants or any other significant changes the crops go through.

Drones at their best

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones use sensors such as electro-optical, stereo-optical, and LiDAR to perceive and absorb the environment or objects in some way.

Computer Vision

Computer Vision is concerned with the automatic extraction, analysis, and understanding of useful information from one or more drone images.

Deep Learning

Deep learning is a specialized method of information processing and a subset of machine learning that uses neural networks and huge amounts of data for decision-making.

DJI’s Drone

Drones with Artificial Intelligence

The term Artificial intelligence is now routinely used in the Drone industry.

The goal of drones and artificial intelligence is to make efficient use of large data sets as automated and seamless as possible.

A large amount of data nowadays is collected by drones in different forms.

This amount of data is very difficult to handle, and proper tools and techniques are required to turn the data to a usable form.

Combination of drones with AI has turned out to be very astounding and indispensable.

AI describes the capability of machines that can perform sophisticated tasks which have characteristics of human intelligence and includes things like reasoning, problem-solving, planning and learning.

Future with Drones and AI

In just a few years, drones have influenced and redefined a variety of industries.

When on the one hand the business tycoons believe that automated drones are the future, on the other hand, many people are threatened by the possibility of this technology becoming wayward. This belief is inspired by many sci-fi movies like The Terminator, Blade Runner and recently Avengers: The Age of Ultron.

What happens when a robot develops a brain of its own? What happens if they realize their ascendancy? What happens if they start thinking of humans as an inferior race? What if they take up arms?!

“We do not have long to act,” Elon Musk, Stephen Hawking, and 114 other specialists wrote. “Once this Pandora’s box is opened, it will be hard to close.”

Having said that, it is the inherent nature of humans to explore and invent. The possibilities that AI-powered drones bring along are too charming and exciting to let go.

At Bridged AI we are not only working on the goal of utilising AI-powered drone data but also helping other AI companies by creating curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.