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What is Computer Vision?

Computer vision is the science and technology that enables machines to understand, analyze, and interpret visual information in the same way humans do. It empowers computers to process, comprehend, and extract meaningful insights from images, videos, and even real-time visual streams.

Just as we use our eyes to take in our surroundings and make decisions, computer vision trains machines to perform these same actions, albeit digitally. For example, with computer vision, a machine can be shown an image of a tree, and will be able to recognize it as such. And because a system trained in computer vision can analyze data points (such as images or videos) so quickly, a computer vision system can surpass human capabilities in short order.

This technology is rapidly expanding as well. According to Gartner, the market for enterprise computer vision software, hardware and services in key markets is expected to generate global revenue of $386 billion by 203, up from $126 billion in 2022.

The History of Computer Vision

Early History

The emergence of computer vision can be traced back to the late 1960s, when universities were exploring artificial intelligence. The goal was to create machines that could see and understand the world around them, much like humans. In 1966, it was believed this could be accomplished by attaching a camera to a computer and having it “describe what it saw.”

In the 1970s, researchers laid the foundation for many of the algorithms we still use today. They developed techniques to detect edges, label lines, model objects in different shapes, understand motion, and more.

As the field progressed, researchers delved into the mathematical aspects of computer vision. They explored concepts like scale-space, inferring shapes from shading, texture, and focus, and contour models called snakes. They discovered that these mathematical ideas could be optimized using already existing frameworks, such as regularization and Markov random fields.

By the 1990s, scientists made significant advancements in projective 3-D reconstructions, improving camera calibration and using optimization methods. They developed techniques for creating 3-D models of scenes using multiple images. Statistical learning techniques were applied to recognize faces in images, a groundbreaking achievement.

Toward the end of the 1990s, computer graphics and computer vision began to merge, opening up new possibilities. Researchers explored image-based rendering, image morphing, panoramic image stitching, and early light-field rendering. These advancements revolutionized how we perceive and interact with visual data.

Computer Vision Today

Thanks to advancements in using machine learning techniques, the adoption of computer vision has rapidly expanded. Several factors contribute to this growth:

  1. Continuous improvement in neural network architectures, models, and algorithms is enhancing the cost-effectiveness and performance of computer vision applications. The combination of CNNs (convolutional neural networks) and vision transformers is achieving impressive performance levels. Additionally, innovations like model compression and advancements in computer chips allow more complex tasks to be carried out on devices at the edge of networks.
  2. The widespread availability of cameras and sensors has led to a tremendous increase in image data. As a result, there’s a growing need for methods that can automatically analyze, manage, and extract valuable insights from this data.
  3. Frameworks that enable processing at the edge of networks, along with developer support and user-friendly products, are further expanding opportunities and making it possible for individuals without specialized expertise to create and deploy their own computer vision models.
  4. New types of businesses and applications are emerging as a result of these advancements. These range from simple uses like adding filters to smartphone photos to more complex applications like producing and distributing global video content, crucial medical image analysis, self-driving cars, security through video surveillance, and automation in fields like robotics and manufacturing.
  5. The increased reliability, affordability, performance, and functionality of computer vision technologies are generating significant business value and fueling its widespread adoption.

How does Computer Vision Work?

Computer vision encompasses a range of algorithms, techniques, and principles to enable machines to understand and interpret visual data. It involves a complex process of analyzing images and videos to extract meaningful information, often employing machine learning, pattern recognition, image processing, and specifically, deep learning and neural networks.

The process of computer vision typically involves the following steps:

  1. Image Acquisition: The first step in computer vision is acquiring the visual data, which can be in the form of images or videos. This data can come from various sources, such as cameras, sensors, or pre-existing image databases.
  2. Preprocessing: Before analysis can begin, the images often undergo preprocessing. This involves cleaning the data, removing noise, correcting for distortions, and adjusting brightness or contrast to enhance the quality of the images. Preprocessing aims to ensure that the resulting algorithms receive accurate and reliable visual input.
  3. Feature Extraction: Feature extraction involves identifying and capturing distinctive patterns or features within the images. These features could be edges, corners, textures, shapes, or color distributions. This helps to simplify the data and extract relevant information that can be used for analysis and classification.
  4. Object Classification: Once the features are extracted and the deep learning model is trained, it can identify and classify objects by comparing the extracted features with the patterns it has learned during training. This process enables computers to distinguish between different classes of objects, such as identifying whether an image contains a cat or a dog.
  5. Object Identification: Object identification goes beyond classification and aims to precisely identify specific instances of objects within an image. It involves localizing and recognizing individual objects or instances within a scene.
  6. Object Tracking: Object tracking is the process of following the movement of an object over time across a sequence of images or video frames. It involves locating and identifying the object in each frame and maintaining its continuity as it moves. Tracking algorithms utilize various techniques to ensure accurate tracking of objects, even under challenging conditions.

By combining the power of deep learning, machine learning, pattern recognition, and image processing, computer vision systems can perform an array of tasks ranging from basic image understanding to complex visual analysis. The advancements in these technologies have significantly expanded the scope and capabilities of computer vision, leading to its widespread adoption in diverse industries and applications.

What is the difference between Computer Vision and Machine learning?

While computer vision heavily relies on machine learning, machine learning is not limited to computer vision. Machine learning techniques, particularly deep learning, have played a major role in advancing computer vision capabilities.

The primary difference between the two is that computer vision focuses on the analysis and interpretation of visual data, while machine learning provides the tools and techniques for learning from data and making predictions or taking actions.

What is the difference between Computer Vision and Artificial Intelligence?

Computer vision can be seen as a sub-category of AI that specifically deals with enabling machines to perceive and understand visual data, such as images and videos. Its primary goal is to replicate human visual processing and interpretation capabilities using algorithms and techniques.

Artificial intelligence, on the other hand, focuses on creating algorithms and models that enable machines to learn, reason, and make decisions autonomously. It encompasses various subfields, including machine learning, natural language processing, expert systems, and robotics. AI aims to develop intelligent systems that can perceive, understand, learn, and interact with the environment in a way that simulates or exceeds human intelligence.

What is the relationship between Computer Vision and Intelligent Document Processing?

Computer vision and intelligent document processing (IDP) are two distinct but interconnected fields within the domain of AI.

Typically, intelligent document processing (IDP) systems use techniques like optical character recognition (OCR), natural language processing, and machine learning to extract data, categorize documents, and derive insights from textual content.

The relationship between CV and IDP comes into play when processing documents that contain visual elements. While IDP focuses on the textual content of documents, computer vision can assist in the analysis and understanding of visual elements within those documents.

Take image extraction, for example. Computer vision can be used in IDP systems to extract images or visual elements embedded within documents. This can include extracting product images from invoices, capturing signatures from contracts, or retrieving logos or stamps.

Another good example is document classification. Computer vision algorithms can assist in these tasks by analyzing the visual characteristics of documents such as logos, templates, or page layouts.

By combining computer vision with intelligent document processing, organizations can enhance the automation and efficiency of document management processes. Computer techniques provide additional context and analysis of visual components, complementing the text-based extraction and analysis performed by IDP systems. This integrated approach enables a more comprehensive understanding and processing of documents, leading to improved accuracy, reduced manual effort, and enhanced information extraction capabilities.

How is Computer Vision Used?

Computer vision can be used in many applications across many industries to transform day-to-day processes? Here are a few industry-specific ways computer vision is solving real-world problems.


Computer vision has revolutionized medical imaging, aiding in disease detection, diagnosis, and treatment. This technology has enabled automated analysis of medical images such as X-rays, MRIs, and CT scans, assisting radiologists in identifying abnormalities or tumors. It has also enabled the development of surgical robots with enhanced precision, improving surgical outcomes.

Financial Services

Computer vision is increasingly being applied in the finance industry, offering innovative solutions to various tasks and challenges.

One way computer vision helps financial institutions is through the automation of document processing tasks, such as invoice processing, document verification, and data extraction. Computer vision can be used to extract relevant information from documents, eliminating the need for manual data entry. It can also verify document authenticity by analyzing visual features, such as watermarks, logos, or security holograms.

Another way the financial sector uses computer vision is for security and compliance purposes. Facial recognition algorithms can be employed for identity verification in mobile banking apps or at ATMs, enhancing security and reducing fraud. Additionally, computer systems can monitor surveillance footage to ensure compliance with regulations, such as detecting unauthorized access to secure areas or monitoring compliance with privacy guidelines.


Computer vision technology is employed by government agencies to improve operational efficiency, enhance security measures, and support decision-making processes.

It’s widely used in government agencies for surveillance and security purposes. This involves monitoring public spaces, borders, critical infrastructure, and government facilities using video analytics. Computer vision algorithms can detect and track objects of interest, identify suspicious activities, and assist in the prevention and response to security threats.

Government agencies also rely on computer vision for document authentication and verification. Computer vision techniques, such as OCR, are employed to extract information from identity documents, passports, visas, or other official records. Computer vision algorithms can analyze visual features, security holograms, or watermarks to verify document authenticity and detect fraudulent activities.

Furthermore, Government agencies use computer vision to improve public service delivery. For example, computer vision-powered systems can analyze video feeds from public transport systems to monitor passenger safety, detect suspicious activities, or manage crowd control. CV algorithms can also be employed to analyze citizen sentiment through social media or feedback analysis, helping shape government policies and services.

Retail and E-commerce

Computer vision has transformed the retail industry by enhancing customer experiences and optimizing operational efficiency. It enables personalized shopping recommendations based on image analysis of customer preferences, and computer vision-powered systems can perform inventory management tasks by automatically tracking stock levels and identifying out-of-stock items.

Additionally, self-checkout systems that use computer vision algorithms for product recognition have streamlined the shopping process. For instance, Amazon Go stores use computer vision to allow customers to shop without cashiers, tracking their selections and charging them automatically.

What are the Challenges of Computer Vision?

Though computer vision offers many benefits, there are also challenges associated with it that can be barriers to more widespread adoption.

Variability and complexity of visual data is one such barrier, where this data exhibits a high degree of variation due to differences in lighting conditions, viewpoints, backgrounds, and object appearances. Handling this variability and developing robust algorithms that can generalize well across diverse visual data remains a significant challenge.

Another challenge is the limited availability of labeled data. Developing accurate and reliable computer vision models often requires large labeled datasets for training. However, manually labeling vast amounts of data can be time-consuming, expensive, and may introduce biases. Acquiring and annotating large-scale datasets with diverse variations remains a challenge for many applications.

A final obstacle faced when implementing computer vision technologies is the ethical and privacy concerns. Ensuring the responsible and transparent use of computer vision algorithms, addressing biases, and safeguarding privacy while leveraging the benefits of the technology are crucial challenges that currently aren’t well defined or regulated.

Addressing these challenges requires ongoing research and advancements in computer vision algorithms, data collection and annotation techniques, model architectures, and ethical frameworks. As the field progresses, tackling these challenges will contribute to the further development and deployment of reliable, accurate, and responsible computer vision systems.

What Does the Future of Computer Vision Look Like?

The path forward for computer vision is paved with exciting possibilities that are both practical and transformative.

In the coming years, we can expect computer vision technologies to become more accessible, scalable, and adaptable for businesses. Ongoing research and innovation are set to play a key role in driving this progress. This means that industries across the board will have the chance to benefit from these advancements.

The future of computer vision will be shaped by a range of factors, including the development of new neural network models such as vision transformers. These models promise to bring fresh insights and approaches to the field. Additionally, the fusion of computer vision with other sensor streams like audio, text, and more will open up new avenues for applications and problem-solving.

Moreover, improvements in processing performance and algorithms will further enhance the capabilities of computer vision systems. This translates to more accurate and efficient processing of visual data.

It’s clear that computer vision is not just a technological trend but a fundamental shift that will impact various industries. From healthcare to manufacturing, from retail to entertainment, the future of computer vision holds the potential to reshape the way we interact with the world and the way businesses operate.

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