What is Computer Vision

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As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies

Introduction

Computer Vision is a type of Artificial Intelligence (AI) that gives computers and machines the ability to interpret, comprehend, and analyze visual information from the physical environment, including the abilities to see and understand things around them.

The objective of Computer Vision is to simulate human vision by allowing machines to detect and classify objects, recognize patterns and produce results based upon visual input data. Specific to the area of Computer Vision are Machine Learning, Deep Learning, Image Processing and Mathematics techniques which are used collaboratively to derive valuable information from digital visual data.[1]

Core Components of Computer Vision

  • Image Acquisition: Creation of visual data from real-world life involves photographing with cameras or recording with imaging devices, including CCTV, medical devices for imaging, and Satellite cameras.
  • Image Preprocessing: Enhancement of images can occur by tuning brightness, contrast/resolution, and removing noise from the data to enhance its utility for analysis.
  • Feature Extraction: The identification of important aesthetic components presents in images, such as texture, shape, colour and edge, provide vital elements to correctly interpreting the images correctly.
  • Modelling and Learning: Application of various algorithms, as well as standard versions of machine learning/deep learning (e.g., convolutional neural networks), to identify/features of visual data, to identify patterns through analysis of features found in the data and to use visual data to create classification/prediction of results.[2]

Main Techniques and Methods

Deep Learning & CNNs

Identify the visual patterns of photographs and graphics and analyse these images for visual patterns in an efficient manner.

Object Detection

Locate and identify objects contained within photographs, video and motion pictures.

Image Segmentation

Break down photographs and video into meaningful segments based on some identifiable reference point.

Facial Recognition

Identify and locate the face(s) of people appearing in either video or motion pictures.

OCR

Capture the character(s) of written text from Photos, Scanned documents and other document types.[3]

Applications of Computer Vision

  • Healthcare: Assists in radiology, diagnostic imaging, and disease identification
  • Automotive: Makes possible self-driving vehicles, driving aid systems, and automated traffic reporting
  • Retail: Assists with the management of supplies, shelf surveillance, and analysis of consumer habits
  • Security & Surveillance: Provides services such as face identification, monitoring, and verification of identity.[4]

Challenges and Future Scope

  • Computer Vision continues to grow, although there are many challenges still facing the field including bad images, the training data that has been created with bias, and the large amount of computing power that is required to run the Algorithms used in Computer Vision.
  • Still, advancements in computer hardware and AI models provide increasingly accurate and efficient means of implementing Computer Vision Technology, making it an important tool for automation and intelligent systems in the future.[5]

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Reference

  1. Wu, Y. (2003). An introduction to computer vision. EECS 432–Advanced Computer Vision Notes Series 1. https://www.eecs.northwestern.edu/~yingwu/teaching/EECS432/Notes/Notes_2017/L1_intro.pdf
  2. Majumder, A., & Gopi, M. (2018). Introduction to visual computing: core concepts in computer vision, graphics, and image processing. CRC press. https://www.taylorfrancis.com/books/mono/10.1201/9781315372846/introduction-visual-computing-aditi-majumder-gopi
  3. Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A. M., & Wang, X. (2021). Computer vision techniques in construction: a critical review. Archives of Computational Methods in Engineering, 28(5). https://openurl.ebsco.com/EPDB%3Agcd%3A6%3A6092438/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A151737492&crl=c&link_origin=scholar.google.com
  4. Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature. https://books.google.co.in/books?hl=en&lr=&id=QptXEAAAQBAJ&oi=fnd&pg=PR9&dq=Applications+of+Computer+Vision+&ots=BNAhx-
  5. Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., … & Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20), 2470. https://www.mdpi.com/2079-9292/10/20/2470