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We, humans, observe, perceive, contextualize, and make sense of our surroundings through a pair of biological cameras: eyes. They may not be capable of recording, storing, and analyzing visual data, but they help us see objects, orient ourselves in different settings, and genuinely enjoy the beauty around us. Imagine having millions of eyes that we can place anywhere we want: buildings, cars, streets, drones, robots, satellites, literally anywhere. Imagine using them to get any information we need in a snap. How would these eyes ease our lives? In countless possible ways. Now, what can be more fascinating than the fact that those eyes exist and the scenario above is not a fantasy? With this in mind, let’s take a leap into the has-been miracle computer vision.

  • What is computer vision?
  • Why is it important?
  • History and milestones
  • Applications of computer vision
  • Common computer vision tasks
  • Computer vision tools
  • Reshaping the future with computer vision
  • Key insights

What is computer vision?

Computer vision is an interdisciplinary field that enables systems and computers to derive resourceful information from videos, digital images, and other forms of visual inputs. AI helps computers think, while computer vision helps them perceive and understand the environment. It imitates the human eye and is used to train models to perform various functions with the help of cameras, algorithms, and data rather than optic nerves, retinas, and visual cortex.

Why is it important?

The significance of computer vision goes hand in hand with its applications in the real world. There is an endless variety of industries that turned to automation and where vision is a fundamental component to streamline operations. All the hyperbolism aside, some of the most promising innovations greatly rely on the benefits of computer vision: Tesla autopilot? Computer vision. Updated Instagram story pack? Computer vision! Amazon StyleSnap? You’ve got that. The list goes on and on. We’ll tap into the applications of computer vision more profoundly in the coming sections. At this point, you may be wondering, but where did all of that come from?

History and milestones

For about 60 years, engineers and scientists have been working on developing systems that would enable machines to see while interpreting visual data. So, we came up with a trajectory to give you an idea of the first attempts in computer vision and what it eventually evolved into.

  • 1959—Most experiments started here when neurophysiologists showed an array of images to a cat in an attempt to correlate responses in its brain. Consequently, they found that it reacted first to the lines or hard edges, which made it clear that image processing starts with simple shapes, such as straight edges.
  • 1963—Computers were able to interpret the tridimensionality of a scene from a picture, and AI was already an academic field.
  • 1974—Optical character recognition (OCR) was introduced to help interpret texts printed in any typeface.
  • 1980—Dr. Kunihiko Fukushima, a neuroscientist from Japan, proposed Neocognitron, a hierarchical multilayered neural network capable of robust visual pattern recognition, including corner, curve, edge, and basic shape detection.
  • 2000-2001—Studies on object recognition increased, helping in the development of the first real-time face recognition application.
  • 2010—ImageNet data were made available containing millions of tagged images across various object classes that provided the foundation of CNNs and other deep learning models used today.
  • 2014COCO has also been developed to offer a dataset used in object detection and support future research.
what is computer vision

Applications of computer vision

Computer vision is being used in numerous fields and is expected to grow into a $48.6 billion industry by 2022. Today most organizations are still unable to finance individual computer vision labs to develop models to meet their product needs, which is where SuperAnnotate steps in with its end-to-end platform to annotate, train, and automate a computer vision pipeline. Where exactly is computer vision used, though? Here are some of the common applications:

Automotive: monitoring busy intersections for near-miss incidents, helping autonomous vehicles navigate across roads, detect and perceive objects. It also helps develop autopilot systems.

Manufacturing: counting and classifying items on a conveyor belt, conducting barcode analysis, assisting in inventory management, rotary and laser die-cutting, etc.

Retail: monitoring product quality or quantity on shelves, assisting in inventory management, tracking customer behavior, helping detect directional gaze.

Defense and security: assistance in detecting landmines, facial detection, weapon defect detection, unmanned military vehicles development.

Agriculture: drone-based crop monitoring and damaged crop detection, automated pesticide spraying, phenotyping, livestock count detection, smart systems for crop grading and sorting.

Healthcare: timely identification of diseases, precise diagnoses, blood loss measurement, medical imaging with greater accuracy, nuclear medicine, and so on.

Robotics: monitoring and imitating movement, studying the environment, detecting the speed of a moving object, learning specific manipulations in robotics.

Insurance: analyzing assets, calculating amounts of damage, analyzing paperwork data and reducing fraud, minimizing or eliminating insurance disputes, and helping provide correct assessments of filed claims.

Fashion: developing try-before-you-buy solutions, image-based outfit analysis, forecasting fashion trends, identifying designer brands, providing customized fashion recommendations.

Media: filtering and classifying media, identifying fake news, monitoring brand exposure, analyzing the effectiveness of advertising placement, and measuring eyes-on-screen attention.

Common computer vision tasks

We’ll dedicate a separate article to each computer vision task. For now, let’s briefly cover a few things computer vision can do:

Object detection

It is the ability to recognize, classify and accurately identify the spatial position of objects of interest in an image. The position, as well as the extension of an object, is commonly captured by means of bounding boxes (or other shapes, c.f., polygons, or ellipsoids).

Image classification

Refers to the process of labeling a frame. Unlike object detection, image classification aims at tagging the image as a whole and not its individual components. This one identifies the class the object belongs to.

image classification

Visual relationship detection

Another computer vision task detects the relationship between two or more objects in order to semantically describe the image. In the image below, the two objects share the relationship “cycles.”

visual relationship detection

Face recognition

As the name suggests, recognizing faces and attributing them to a certain individual is one of the common computer vision tasks. If trained properly, a model is just as capable of face recognition as we humans are.

Semantic segmentation

Semantic segmentation is the process of identifying similar objects belonging to the same class at the pixel level. For instance, in the image below, the model has tried to identify similar objects, in this case, people, and color-coded them with a single color to express their belonging to the same class.

semantic segmentation

Computer vision tools

The tasks above wouldn’t have been possible without computer vision algorithms, libraries, and tools, the most common of which are:

  • OpenCV: a real-time computer vision and machine learning software library. Provides infrastructure for computer vision applications that assist in face detection, recognition, 3D models extractions, and motion tracking.
  • TensorFlow: a free and open-source software library. Can be used across multiple tasks but has a special focus on training and inference of deep neural networks.
  • YOLO: an algorithm that provides real-time object detection with increased speed and accuracy. Can be used to detect traffic signals, people, parking meters, animals, etc.
  • MATLAB: a programming platform with a computer vision toolbox that supports diverse applications in image, signal, and video processing, deep learning, and machine learning.

Reshaping the future with computer vision

Computer vision has improved drastically ever since the 1960s and extended its applications over industries we didn’t imagine it would. Analogous growth and success promise a greater variety of computer vision usage and a wider spectrum of benefits in the decades to come. As anticipated, computer vision will drive the future in two ways, at the minimum:

1) Massive shift towards vision-driven technology and solutions: with the availability of automated tasks, more companies will be prone to opt for streamlined solutions. This will be the direct consequence of the need to save resources, time and reduce extra costs.

2) Improved performance of the earlier deployed solutions: the projected success will mostly be bound to industry-specific computer vision applications.

  • Reduced product shrinkage: by using security cameras along with point-of-sale system data, computer vision will allow monitoring self-checkouts and issue real-time alerts if any anomalies are caught.
  • Enhanced customer experience: the monitoring of checkout lines with the help of cameras will allow for alerting the store manager to open up another checkout counter as queues grow.

In short, retailers of all sizes and disciplines will take on and scale this technology to drive efficiencies as they grow their retail business. If the examples above are only a tiny glance into the expected headway, try to picture how many more industries will capitalize on computer vision.

Key insights

Computer vision is gaining a stronger foothold, transforming our lives almost rapidly. The potential of a similar technology evolves beyond our imagination. Today computer vision is incorporated in an enormous amount of industries, beckoning us to further invest in its applications and methodologies, for a good reason. The evolution of deep learning models enables us to handle complex tasks with ease. Given these achievements, the future is more promising than ever. Each of us can stand beyond the next ground-breaking AI. This article was your first step. Feel free to reach out for more.

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