Computer vision: what is it and how it works

Contents of article:

  1. What is computer vision?
  2. How computer vision works
  3. Why use proxies by Dexodata for computer vision development?

Business cases leveraging AI-driven technologies range from e-commerce to product lines’ optimization. Their adoption relies on processing terabytes of information, including obtained online in an ethical way. That is why entrepreneurs and corporations seek to buy residential and mobile proxies from Dexodata as a KYC/AML-compliant infrastructure. Our wide-ranged ecosystem covers 100+ countries, supports big data workflows, and provides geo targeted proxies suitable for ML-enhanced internet data collection.

Microsoft, Google, NVIDIA, Qualcomm Technologies, Inc. and other “blue chips” IT giants, meanwhile, are raising resources to a new field of AI development. This is computer vision (CV), one of the fastest growing ML-based methods, with a total market estimated at size from $15 to $22 billion. The technology holds potential across numerous sectors, from healthcare and autonomous driving to medical diagnosis and security. The description of computer vision mechanics and peculiarities are offered below.

What is computer vision?

Computer vision is an AI’s applied direction that empowers neural networks to manage graphical data and obtain substantial insights from it. CV works with digital pictures, videos or other visual elements. Its core algorithms focus on automated extraction, examination, and comprehension of valuable information from either a solitary graphical file or their sequences. The rising popularity of computer vision is explained but to the same benefits AI-based models bring to data extraction through residential and mobile proxies one buys. These are higher velocity, accuracy, and increased amounts of potentially manageable information.


How computer vision works


Computer vision employs convolutional neural networks (CNN), a unique technology rooted in deep learning. This is a multi-layered method of building analogies according to provided data sets and chosen targets. For example, municipalities deploy CV systems to detect pedestrians for traffic optimization, while self-driven cars use the same principle to avoid road obstacles.

CNN leverages multiple AI-based layers for pixel analysis becoming gradually complex with each subsequent level. From spotting simple forms and characteristics, neural networks come to patterns’ identification. The classification of visual objects is the main goal of the conclusive, “fully connected” layer. 

What is computer vision and why apply geo targeted proxies for CV

Intermediate “pooling” layer raises the accuracy with which AI-enhanced technologies recognize patterns and spot particular models in provided media. The reliability of input data impacts the precision specific to final results, and thus requires buying residential and mobile proxies at corporate level.  Proxy servers contribute in preparing correct and actual information for AI-based computer vision to:

  1. Train self-studying networks through their initial machine learning phase.
  2. Enrich gathered visuals with additional material for eliminating detection inaccuracies.

As a component of ethical and efficient web data harvesting, geo targeted proxies assist accessing publicly available images and reduce amounts of inapplicable graphic content. It took less than a decade for CV systems to double their average accuracy reaching the 99% indicator.

The main CNN techniques are:

  • Three-part organization of the initial databases to work with. It involves:
    • Data
    • Filters
    • Feature maps. 

The latter constitute kits of characteristics identified by filters in the processed pictures.

  • Shaping spatial hierarchies of features to assort media content metrics.
  • Matrix multiplication based on kernels acquiring information from every pixel.
  • Statistically-oriented schemes, including decision trees, linear regression, etc.
  • Backpropagation algorithm, which adjusts scales and minimizes number of bias-based errors.


Why use proxies by Dexodata for computer vision development?


The growing role of ML-enhanced systems was reflected in the governmental acts emphasizing artificial intelligence’s role. Apart from authority structures, business representatives are interested in developing computer vision as it serves for cost reduction and manufacturing optimization. The implementation of AI-based platforms has raised a trend on ethical functioning as more profitable. The Dexodata ecosystem provides geo targeted proxies suitable for computer vision’s development objectives in strict compliance with AML and KYC policies. Ask for a proxy free trial to experience web scraping with ethical status and enhance your artificial intelligence models with accurate, relevant internet insights.


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