Computer Vision is a branch of science that combines science and technology to enable machines, such as computers, to possess capabilities similar to human vision.
In today’s digital era, computer vision has become one of the most vital branches of artificial intelligence (AI), revolutionizing various fields, from facial recognition to autonomous vehicles. This article will provide a detailed discussion on the definition of computer vision, its applications, and its benefits in various domains.
What Is Computer Vision?
Simply put, computer vision is a branch of science that is a combination of science and technology that enables a machine (computer) to “see,” “process,” and “understand” images or videos as humans do.
The goal of computer vision is to identify, process, and analyze images and other visual data to make decisions or provide useful insights.
Computer vision can be likened to our vision when looking at a scenic landscape. At that moment, we might think about several things, such as what the current weather is like, where a tree is located, and how far the tree is from where we are standing. This analogy represents how computer vision works to extract information from an image.
The primary focus of computer vision is the development of theories and principles to create artificial systems using algorithms and machine learning models capable of extracting information from images or videos.
Typically, the data sources used in computer vision come from video sequences, depth images, images from multiple camera angles, or data from medical devices such as CT scans or MRIs.
Components of Computer Vision
In computer vision, several components play a crucial role in the system’s operation:
- Computer (Machine): Functions as the brain of the system to process visual data using specific algorithms and software.
- Camera: Captures or acquires images or videos from the surrounding environment as visual data sources.
- Image: A digital image captured by the camera used for training and analysis by the system.
These three components work in an integrated manner to enable the machine to “see” and understand objects or environments like a human.
Key Points to Understand About Computer Vision
To deepen understanding of computer vision, here are several key aspects that form the foundation of this technology:
Computer vision is achieved using large image datasets. These datasets are used to train models in computer vision. The models are built using deep learning techniques where the images are labeled. Labeled images serve as the basis for teaching computers to recognize various patterns and objects to make specific decisions.
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Applications of Computer Vision AI
Today, computer vision technology has rapidly advanced to support various tasks in system security, research, and healthcare. The following are some applications of computer vision:
Image Classification
Image Classification MATLAB
Image Classification: The basic form of computer vision where models are trained with specifically labeled images. It recognizes the type of object but does not detect its specific location. Example: A model learns that an image contains a “fish” and when tested with a new image, it classifies it as a fish.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR)
Optical Character Recognition (OCR): A technology that converts scanned documents into digital text. Commonly used in scanners with OCR software. Example: License plate readers that convert image data into text for identification.
Face Detection
Face Detection in Computer Vision
Face Detection: Detects the presence of human faces in images or videos without identifying their identity.
Smile Detection
Face, Age and Emotion Detection
Smile Detection: An extension of face detection to recognize smiling expressions. Other developments include facial expression recognition, age detection, emotion detection, and gender detection.
Object Detection
Computer Vision Object Detection
Object Detection: Identifies the specific location of objects in images. It trains models to draw bounding boxes around recognized objects. Example: Self-checkout systems in modern stores like Amazon Go use this for automated product recognition.
Vision-based Biometrics
Iris Recognition Computer Vision
Vision-based Biometrics: Identity recognition using visual images of human body parts for authentication, such as face, iris, fingerprint, or gait recognition.
Vision in Space
Vision in Space: Used in space exploration for autonomous satellite navigation, visual monitoring of outer space, and object tracking in orbit. Example: Mars Rovers use stereo cameras for real-time obstacle detection and safe navigation.
Vision-based Interaction (and Games)
Vision-based Interaction (and Games): Allows humans to interact with computer systems through body movement or facial expressions. Common in AR games.
Industrial Robots
Industrial Robots: Use vision systems with cameras and computer vision algorithms to identify objects on production lines, perform quality control, and guide robotic arms.
Medical Imaging
Brain Tumor Detection Using Computer Vision
Medical Imaging: Applies computer vision in medicine to detect diseases or abnormalities in scan images like X-rays, CT scans, MRIs, and ultrasound. Examples: Early cancer detection through mammography, brain damage identification from MRI, and fetal growth monitoring from ultrasound images.
Semantic Segmentation
Semantic Segmentation: More advanced than object detection, semantic segmentation labels each pixel in an image. This allows for detailed image understanding. Example: Self-driving cars needing detailed environmental analysis.
Segmentation Semantic
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Integration of Computer Vision and Language Models
Recent AI developments allow the integration of computer vision with language models to create multi-modal models. These combine visual capabilities with generative AI to understand and generate content based on visual and textual input. Example: AI that can interpret an image and explain its contents in natural language.
Required Skills in Implementing Computer Vision
Implementing computer vision demands not only understanding visual concepts and algorithms but also technical skills. These include programming languages like C/C++, Java, or MATLAB, which are crucial for building and testing digital image processing systems.
Strong knowledge in linear algebra, basic calculus, probability, and statistics is also essential, as these are the mathematical foundations of machine learning and image processing algorithms. These skills are vital in designing object detection, classification, and segmentation models.
Why Is Computer Vision Important?
Computer vision plays a crucial role in various aspects of modern life due to its ability to process, understand, and extract information from visual data automatically. Here are the main reasons:
- Safety: Assists autonomous vehicles in recognizing signs, pedestrians, or potential hazards.
- Health: Used in medical image analysis to detect diseases early and accurately.
- Security: Detects and recognizes faces in public areas for criminal identification.
- Comfort: Automates daily tasks, such as smart homes recognizing users and habits.
- Fun: Used in motion-based or facial expression-based games for interactive experiences.
- Access: Helps users access information from images or environments (e.g., OCR for the visually impaired).
FAQ
What is Computer Vision and why is it important in today’s digital era?
Computer Vision is a branch of artificial intelligence (AI) that enables computers to “see,” process, and understand images or videos like humans. In the digital era, it’s crucial in sectors such as security, healthcare, industry, autonomous vehicles, and everyday apps like smartphone face recognition.
What are some real-life applications of Computer Vision?
Examples include face detection on phone cameras, OCR for digitizing documents, checkout automation like Amazon Go, medical diagnostics, industrial robot navigation, and surveillance systems.
What’s the difference between image classification, object detection, and semantic segmentation?
Image classification identifies the type of object. Object detection recognizes and locates specific objects using bounding boxes. Semantic segmentation labels every pixel in the image for a detailed understanding.
Conclusion
Computer vision technology is a highly important and promising field in future digital transformation. With abundant image data, this technology can classify, detect, and understand objects in images with high accuracy. The latest innovation of combining computer vision with language intelligence marks a new era of multi-modal AI capabilities.
Additionally, computer vision syndrome reminds us of the importance of eye health amid increased technology use.
With a deep understanding of what computer vision is and its applications, we can be better prepared to face and utilize the ever-evolving technological transformation.
Reference
Moin, Tanvir. (2023). Overview of Computer Vision. 10.13140/RG.2.2.13989.68327.
Seetharaman, Dr. K. & Ragupathy, R.. (2012). Iris Recognition based Image Authentication. International Journal of Computer Applications. 44. 1-8. 10.5120/6272-8434.
Jonsson, Ari & Morris, Robert & Pedersen, Liam. (2007). Autonomy in Space Exploration: Current Capabilities and Future Challenges. IEEE Aerospace Conference Proceedings. 1-12. 10.1109/AERO.2007.352852.
Penulis : Meilina Eka A
