Exploring Computer Vision (CV): Meaning, Principles, Applications, and Research

Computer vision (CV) is a field of artificial intelligence (AI) that aims to enable computers to imitate the human visual system to better understand and interpret digital images and videos. content. This process mainly involves image acquisition, screening, analysis, recognition and information extraction. It can be said that AI gives computers the ability to think, while CV gives them the ability to observe and understand.

The value of computer vision

Computer vision systems are trained and optimized to analyze a large number of products or processes in real time to help identify problems. Its speed, objectivity, continuity, accuracy and scalability exceed human capabilities. It is able to inspect products, observe infrastructure or production processes, and perform real-time analysis. The application of this technology makes problem discovery more efficient and accurate.

The latest computer vision deep learning models have demonstrated superhuman accuracy and performance in real-world image recognition tasks. These models have achieved significant breakthroughs in facial recognition, object detection, and image classification. With the advancement of technology, computer vision has been widely used in various industries. It plays an important role in security and medical imaging, manufacturing, automotive, agriculture, construction, smart cities, transportation, and more. Moreover, with the continuous development of technology, computer vision has become more flexible and scalable, which also brings the possibility of more practical application cases.

According to relevant media estimates, the computer vision market will reach US$144 billion by 2028.

Computer vision working steps and principles

Let us first understand the basic working steps of computer vision:

Step 1, image acquisition, the camera or image sensor inputs digital images.

Step 2, preprocessing, the original image input needs to be preprocessed to optimize the performance of subsequent computer vision tasks. Preprocessing includes noise reduction, contrast enhancement, rescaling or image cropping.

Step 3, algorithm processing, computer vision algorithms perform object detection, image segmentation and classification on each image or video frame.

Step 4, rule processing, the output information needs to be processed according to the use case condition rules. This part performs automation based on information obtained from computer vision tasks.

Let’s take a look at the working principle of computer vision:

Modern computer vision systems combine image processing, machine learning and deep learning technology, relying on Pattern recognition and deep learning to self-train and understand visual data. Traditional computer vision uses machine learning, but now deep learning methods have evolved into better solutions in this field.

Many high-performance methods in modern computer vision applications are based on convolutional neural networks (CNN). This layered neural network allows computers to understand image data contextually. Given enough data, the computer learns how to differentiate between images. As the image data passes through the model, the computer applies a CNN to view the data. CNNs help deep learning models understand images by breaking them down into pixels, which are given labels to train specific features, so-called image annotations. The model performs convolutions using the labels and makes predictions about what it sees, and iteratively checks the accuracy of the predictions until the predictions are as expected. Deep learning relies on neural networks and uses examples to solve problems. It learns on its own by using labeled data to identify common use cases in examples.

Application fields of computer vision

Manufacturing industry: Industrial computer vision is used in the manufacturing industry for automated product inspection, object counting, and process automation. , and improve employee safety through PPE testing and mask testing.

Healthcare: Among the applications of computer vision in healthcare, a prominent example is automatic human fall detection to create fall risk scores and trigger alerts.

Security: In video surveillance and security, personnel detection is performed to achieve intelligent perimeter monitoring.

Agriculture: A use case for computational vision in agriculture is to automatically monitor animals and detect animal diseases and abnormalities early.

Smart Cities: Computer vision is used in smart cities for crowd analysis, traffic analysis, vehicle counting and infrastructure inspection.

Retail: Video from retail store surveillance cameras can be used to track customer movement patterns for people counting or traffic analysis.

Insurance: Computer Vision in Insurance leverages AI vision for automated risk management and assessment, claims management, and forward-looking analysis.

Logistics: Automation to save costs through reduced human error, predictive maintenance and accelerated operations across the supply chain.

Pharmaceutical: Computer vision in the pharmaceutical industry is used for packaging inspection, capsule identification, and visual inspection of equipment cleaning.

Computer Vision Research Direction

Object recognition: Determine whether image data contains one or more specified or learned objects or object classes.

Facial recognition: Recognize faces by matching them to a database.

Object detection: Analyze image data for specific conditions and locate semantic objects of a given class.

Pose estimation: Estimating the relative direction and position of a specific object.

Optical character recognition: Recognizes characters in images, often combined with text encoding.

Scene understanding: Parse images into meaningful segments for analysis.

Motion Analysis: Track the movement of points of interest or objects in an image sequence or video.

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