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Computer Vision in 2024: In-Depth Guide

Computer vision, is the process of using software to perform operations that the human brain and vision can perform, such as object recognition, flaw detection, or quality control. Various image processing and machine learning algorithms are used together to achieve computer vision.

What is Computer Vision?

A clear definition is suggested by IBM: “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.”

Computer vision aims for computers to see and understand the images as well as humans. Computers have better tools to see but less developed tools to understand like humans. High-quality cameras, sensors, radar, and thermal cameras enable computers to get more visual information from outside.  However, derivation of meaning from the visual input is a more complex issue and is an area of research under artificial intelligence.

Image of a street with different objects shaded and marked with different colors and labels.
Source: Techcrunch

Deep learning and neural network techniques make it easier for computer vision to make sense of what it sees, and computer vision gets closer to the human visual cognitive system. In fact, computer vision surpasses human vision in many applications, such as pattern recognition. For example, researchers suggest that AI gives better and faster results in identifying neurological illnesses from brain scan images.

What is the difference between computer vision and machine vision?

Machine vision is the technology used to see errors in a production line or products that need to be categorized. It is mainly used in industrial processes. Computer vision is the whole software and hardware tool used for image acquisition and processing. While computer vision can be used alone without being part of a large system, machine vision is part of a system.

Why is it important now?

Use cases of computer vision range from healthcare to automotive industries. Forbes expected the computer vision market to reach USD 49 billion by 2022.

Computer vision is one of the most important points in the autonomous vehicle race of the automotive industry. Computer vision enables cars to manage the relationship between the car and the environment.

Computer vision is highly dependent on the quality and quantity of the data, more data with better quality builds better deep learning models. Computer vision algorithms are fed by visual information flowing from smartphones every day. Therefore computer vision systems will be better and smarter in the future.

How does computer vision work?

There are three main components of computer vision:

  • Acquisition of an image
  • Image Processing
  • Analysis

Acquisition of an image: A digital camera or sensor captures the image or data and it is stored as binary numbers; ones and zeroes. This is called raw data.

Image Processing: this process includes the methods used to extract the basic geometric elements that may give information about the image. The image processing also includes the preprocessing step. Preprocessing is necessary to get a more accurate analysis by getting rid of unwanted elements such as noise.

For more information about how image processing works and how it is used in image recognition, feel free to explore our research on the topic.

Analysis: In this step, the processed image is analyzed by using high-level algorithms. Trained neural networks can be used to identify objects and make decisions.

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your computer vision project needs.

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What are the top computer vision techniques?

In computer vision, a number of methods are used to evaluate the inputs and obtain the outputs. Techniques such as image classification, object detection, object tracking, and image segmentation help create computer vision by combining them or separately.

Many different architectures have been created for Deep Learning, but the CNN architecture is most commonly used in the CV field. This technique has disadvantages such as large dataset requirements, the difficulty of optimization, and being a black-box. Despite these disadvantages, deep learning dominates the field with its ability to build complex models based on data. However, there are still some computer vision models hand-coded by developers. A company from Japan, Brain, uses this approach to identify different pastries for bakeries or cancer cells for doctors.

Image Classification:

Image classification aims to classify the content in the image according to its type. The most widely used deep learning technique is convolutional neural networks (CNN).

Pre-tagged images create a training data set. Each of the classes in which the images will be included has separate properties and these properties are represented by vectors. These vectors are trained using CNN and improvements are made with new data sets. If the quality of the classifier is not sufficient, more test sets or training sets can be added.

Object Detection:

The identification of the objects in an image has a different working principle than the image classification. In order to classify the objects in the image, those objects must be determined in the bounding boxes. In order to classify the objects in the image, those objects must be determined in the boxes. Although these boxes are of different sizes, they may contain images of the same class. Also, the detection of images containing a large number of objects also requires an increasing amount of computer power. Algorithms like R-CNN, Fast R-CNN, YOLO, Single Shot MultiBox Detector (SSD), and Region-Based Fully Convolutional Networks have been developed to find these occurrences fast.

Object Tracking:

Object tracking is the method that tracks the movement of the object in an image by finding the same object in the next image. Object Tracking techniques can be divided into three categories according to observation methods:

  • Generative techniques: In this technique, the tracking problem is formulated by searching for the image regions which are most similar to the target model. Principal component analysis(PCA), independent component analysis (ICA), and non-negative matrix factorization (NMF) are examples of generative models that try to find a suitable representation of the original data.
  • Discriminative techniques: In discriminative methods, monitoring is considered a binary classification problem, which aims to find a decision limit that best separates the target from the background. Unlike generative methods, both target and background information are used simultaneously. Examples of discriminative methods are stacked autoencoders (SAE), convolutional neural networks, and support vector machines(SVM).
  • Hybrid techniques: these two techniques are used jointly, and different techniques are adapted according to the problem.

Image Segmentation:

The process of dividing a digital image into image objects or pixel sets. The purpose of image segmentation is to simplify the representation of an image and to facilitate analysis.

How image segmentation is done with layers
Source: University of Toronto

Since there are many different approaches for image segmentation, Mask R-CNN and Fully Convolutional Networks (FCN) can be used for dense predictions without any fully connected layers.

What are its use cases in the industry?

Computer vision is used in many industries, from automotive to marketing, and health to security. Computer vision and image processing are concepts that do not separate from each other with certain lines. You can read this research to get detailed information about the usage areas in the industry.

If you have questions about how computer vision can help your business, feel free to ask us: 

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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1 Comments
Yura
Oct 07, 2020 at 20:57

I got a little deep into CV importance in the modern world because i like image processing and computer vision as well, so I’m making some research work on the computer vision field for my master thesis in computer science, but a few of my family always try to tell me that computer vision it’s not important anymore and nobody uses it now, because there are now many machine learning techniques improving many things, I’ve tried to explain that they both complement each other but at the end, I feel worried and head down about it, and asking myself if it is true or not.

Cem Dilmegani
Oct 11, 2020 at 12:52

Hi Yura! To be honest, we are not the best guides on which area to pursue as an academic, we are more focused on the business implications of AI.
Hope you figure it out,

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