Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe, and understand.

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong with an image.

Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data, and algorithms rather than retinas, optic nerves, and the visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

How does it work?

Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognizes images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

Computer vision tasks?

  • Image classification sees an image and can classify it (a dog, an apple, a person’s face). More precisely, it can accurately predict that a given image belongs to a certain class. For example, a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users.
  • Object detection can use image classification to identify a certain class of images and then detect and tabulate their appearance in an image or video. Examples include detecting damages on an assembly line or identifying machinery that requires maintenance.
  • Object tracking follows or tracks an object once it is detected. This task is often executed with images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to not only classify and detect objects such as pedestrians, other cars, and road infrastructure, but they need to track them in motion to avoid collisions and obey traffic laws.
  • Content-based image retrieval uses computer vision to browse, search, and retrieve images from large data stores, based on the content of the images rather than metadata tags associated with them. This task can incorporate automatic image annotation that replaces manual image tagging. These tasks can be used for digital asset management systems and can increase the accuracy of search and retrieval.

Many companies work in computer vision like

Google, Microsoft, IBM, Amazon, Tesla, Intel, and more…,

Pixel Extraction

OpenCV (Open Source Computer Vision), a cross-platform and free-to-use library of functions is based on real-time Computer Vision which supports Deep Learning frameworks that aid in image and video processing. In Computer Vision, the principal element is to extract the pixels from the image to study the objects and thus understand what it contains. Below are a few key aspects that Computer Vision seeks to recognize in the photographs:

  • Object Detection: The location of the object.
  • Object Recognition: The objects in the image, and their positions.
  • Object Classification: The broad category that the object lies in.
  • Object Segmentation: The pixels belonging to that object.

Applications and Future

Computer Vision covers a huge ground as its applications know no bounds. It often escapes our minds as we fail to notice the role Computer Vision plays in the gadgets, we use day in and day out.

  • Smartphones and Web: Google Lens, QR Codes, Snapchat filters (face tracking), Night Sight, Face and Expression Detection, Lens Blur, Portrait mode, Google Photos (Face, Object, and scene recognition), Google Maps (Image Stitching).
  • Medical Imaging: CAT/MRI
  • Insurance: Property Inspection and Damage analysis
  • Optical Character Recognition (OCR)
  • 3D Model Building (Photogrammetry)
  • Merging CGI with live actors in movies

Computer Vision is an ever-evolving area of study, with specialized custom tasks and techniques to target application domains. I visualize its market value growing as fast as its capabilities. With our intelligence and interest, we will soon be able to blend our abilities with Computer Vision and achieve new heights.

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