Computer Vision with Deep Learning and NLP

Table of Contents

Get an introduction to the interesting world of machine learning technology in this blog, which is a subfield of artificial intelligence (AI).

Introduction

Computer vision is a field of artificial intelligence training computers to understand the visual world of humans. Using digital images from videos and cameras and videos and deep learning models, machines can accurately identify and classify objects. They can then react to what they “see.” Learn more about how computer vision works and its applications in our everyday life in this article.

How does computer vision work?

Computer vision technology mimics the way the real human brain works. But how does a human brain perform visual object recognition? 

A popular hypothesis states that the human brain relies on patterns to decode individual objects. The same concept is used to create computer vision systems.

Computer vision algorithms are based on pattern recognition. Computers are trained on a huge amount of visual data. Computers process the images, label objects on them, and then find patterns in those objects. 

For instance, if we send a million flower images, the computer will analyze all of them and identify patterns similar to all flowers. At the end of this process, the computer will create a model named “flower.” Thus, the computer can accurately detect if a particular image is that of a flower every time we send them an image.

History of computer vision

  • 1959: The invention of the first digital image scanner took place by transforming images into grids of numbers.
  • 1963: The father of CV, Larry Roberts, described the process of deriving 3D information about solid objects from 2D photographs.
  • 1966: Marvin Minksy helped a graduate student to connect a camera with a computer and have it describe what it sees.
  • 1980: Kunihiko Fukushima built the ‘neocognitron’, which is the precursor of modern Convolutional Neural Networks.
  • 1991-1993: Introduction of multiplex recording devices along with cover video surveillance for ATM machines.
  • 2001: Two MIT researchers introduced the first face detection framework (Viola-Jones) working in real-time.
  • 2009: Google tested robot cars on roads.
  • 2010: Google released Goggles, which was an image recognition app for searches on pictures taken by mobile.
  • 2010: Facebook began using facial recognition to help tag photos.
  • 2011: Facial recognition helped confirm Osama bin Laden’s identity.
  • 2012: Google Brain’s neural network recognized cat images using a deep learning algorithm.
  • 2015: Open-source Machine learning system TensorFlow was launched by Google.
  • 2016: The DeepMind’s AlphaGo algorithm of Google beat the world Go champion.
  • 2017: Apple released the iPhone X by advertising face recognition as one of its main new features.
  • 2018: AI model of Alibaba scored better than humans in a reading and comprehension test by Stanford University. Rekognition, a real-time face recognition system by Amazon, was sold to police departments.
  • 2019: A facial recognition plan was announced by the Indian government. This plan allows police officers to search images through mobile apps. The US added four leading AI start-ups in China to a trade blacklist. It was ruled by the UK High Court that the automatic facial recognition technology used to search for people in crowds is lawful.
  • 2030: It is believed that by this time at least 60% of countries will be using AI surveillance technology.

Computer vision applications

Content organization

Computer vision systems help to organize content. Apple Photos is an interesting example here. The app has access to the user’s photo collections. It automatically adds tags to photos and lets its users browse a more structured collection of photographs. The app also creates a curated view of a user’s best moments for them.

Facial recognition

To match people’s faces and photos to their identities, facial recognition technology is used. This technology is also integrated into major products used in everyday life. For instance, Facebook uses computer vision to identify people in photos.

Facial recognition is an important technology for biometric authentication. Several mobile devices available today allow users to unlock their devices by showing their faces. Mobile devices process the user’s image and, based on this analysis, it can tell whether the person who is holding the mobile device is authorized on this device or not

Self-driving cars

Computer vision allows cars to make sense of their surroundings. A smart vehicle has cameras installed that capture videos from different angles. It then sends the videos as an input signal to the computer vision software. The system processes the video in real time. It detects objects such as road marking, traffic lights, objects near the car (like pedestrians or other vehicles), etc. Autopilot in Tesla cars is one of the most interesting examples of applications of this technology.

Health sector

Computer vision has been an essential part of advances in the health sector. Computer vision algorithms help to automate tasks like finding symptoms in x-ray and MRI scans or detecting cancerous moles in skin images.

Examples of computer vision

  • Drone monitoring of crops.
  • Smart systems for classifying and sorting crops.
  • Weather records.
  • Smart Farming.
  • Yield monitoring.
  • Automatic pesticide spraying.
  • Forest information.
  • Crop field security.

Future of computer vision

Computer vision is a fast-developing field. It has also gathered a lot of attention from various industries in a short span of time. With the amount of data that we are generating every single day, it’s only natural that machines use that data to craft solutions for better well-being.

With corporate giants such as Google, Microsoft, Facebook, and Apple investing in computer vision, it’s only a matter of time before this technology takes over the global market. 

If you need assistance to develop a computer vision project, contact appleute today.

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