Top 5 Computer Vision Applications Transforming Our Lives

Overview of Computer Vision

Briefly speaking, computer vision is a technology that allows computers to see and interpret people or objects in visual stimuli (e.g., images or videos). It works by using specialized software to analyze visual data collected from hardware like cameras and sensors. The technology also uses different techniques like image processing or statistical pattern classification to implement this process.

History & Growth of Computer Vision

Computer vision first appeared in the early 1970s as the visual perception component that was expected to imitate human vision and equip robots with intelligent behavior.

It differs from digital image processing which only manipulates and improves images through techniques like filtering or noise reduction. Computer vision, by contrast, aims at retrieving meaningful information from images, just like the human visual system can do. Particularly, its goal is to interpret the spatial arrangement of objects (“3D structure”) in a scene and understand the overall context of an image. Accordingly, it was expected to perform high-level tasks, like object detection or motion analysis.

The following period of the 1970s-1990s witnessed the evolution of computer vision with advancements in image analysis techniques and hardware (like graphics chips and many-core architectures). Studies during this period also introduced various foundational algorithms, such as line labeling, articulated body models, image pyramids, edge and contour detection, or image segmentation.

Computer vision algorithms — Image source

In the 2000s, the deepening interaction of computer graphics and computer vision transformed this realm significantly, with the introduction of recent CV algorithms like image-based rendering or feature-based recognition. Especially, the growing adoption of deep learning algorithms, typically Convolutional Neural Networks, has truly made CV outcomes more accurate.

Besides technological advancements, the higher demand for safety and productivity has recently encouraged computer vision to develop. Particularly when COVID-19 suddenly exploded in late 2019, various organizations were finding ways to control and prevent infection effectively. Computer vision, therefore, became a crucial tool with common applications like masked facial recognition, thermography, or bacterial screening.

Future of Computer Vision

Even when the pandemic ended, the global demand for this technology continued to soar. Researchers predicted this demand will grow by 27.3% annually from 2024 to 2032 with the following noticeable trends:

By Technology

The increasing adoption of 3D imaging and automation technologies will boost the growth of AI vision which involves using artificial intelligence to understand visual data.
Generative AI will be integrated to make computer vision systems more robust and generalized. It uses techniques like generative adversarial networks to create realistic images, enhance image synthesis, assist data augmentation, etc.

By Industry

The agriculture industry will see the growing use of AI vision in many tasks, like disease detection, plant monitoring, and yield prediction. Particularly, AI algorithms can analyze crop images, predicting the crop’s health and yield potential.
Manufacturing still dominates the computer vision market due to its huge applications in boosting efficiency, minimizing waste, and avoiding potential failures. The technology can inspect products, spot their defects (e.g., minor cracks), and detect early signs of machinery deterioration.
Healthcare will witness the highest growth in using computer vision. The technology can analyze X-rays, MRIs, and CT scans to recognize fine abnormalities that can be invisible to the human eye, hence enhancing diagnoses.

5 Key Applications of Computer Vision

Computer vision has wide applications across industries, bringing them a variety of transformative benefits. In this section, we’ll dig into five common applications by computer vision’s typical techniques:

1. Image Classification

Image classification is a basic, yet important computer vision application. Its main goal is to train machines how to classify images based on their content – be it a cat, a bike, or even more complex objects. This categorization task focuses on identifying shapes, textures, patterns, and colors in images to sort them into the right groups.

Computer vision systems leverage many techniques and algorithms for effective image classification. They involve feature extraction, Support Vector Machines (SVMs), Naive Bayes, Hidden Markov Models (HMMs), etc.

Today, these systems also integrate deep learning methods to categorize images with higher accuracy. One of the most common techniques is Convolutional Neural Networks (CNNs). CNNs layer numerous filters over images to extract and classify different features (e.g., shapes or edges).

Real-World Applications

Image classification has countless applications across industries. Below are some key areas where computer vision is utilized for this task:

Medical Image Analysis: In healthcare, computer vision systems can analyze thousands of medical images (like CT scans or X-rays) to detect early signs or abnormal growths of disease. This helps doctors make faster, yet still precise diagnoses.

Product Classification in eCommerce: Online retailers such as Amazon or Walmart can use CV systems to automatically organize thousands of products into groups like “office furniture,” “home decor,” or “customer electronics” based on their images alone. This helps them manage large inventories with ease.

Land Use Classification: Governments (like Queensland) and environmental organizations are using computer vision to analyze satellite imagery and automatically classify land use features (e.g., forests, water bodies, or urban areas). This helps them track land use changes and manage natural resources more efficiently.

Wildlife Monitoring: CV systems can categorize images of animals captured by cameras in wildlife areas, which is specifically useful in wildlife conservation. Accordingly, researchers can collect data on species distribution, interpret migration patterns, and track endangered animals.

2. Facial Recognition

Facial recognition is the sophisticated, yet most common application of computer vision. It uses both traditional and deep learning methods – for example, Principal Component Analysis (PCA), CNNs, and DeepFace – to identify specific facial characteristics. These features, like the distance between eyes or the jawline’s contour, form a “faceprint” to tell individuals apart.

Computer vision implements the facial recognition process through two key steps: face detection and face identification. First, a CV system detects and isolates a face in an image or a video frame. Then, the system will compare the detected face against a database of known faces to identify whether the face matches a particular individual. These capabilities make computer vision a valuable tool for surveillance and security, especially in tracing criminals or missing individuals.

Real-World Applications

Here are several impactful applications of vision-based facial recognition:

Access Control Systems: Many office buildings, government agencies, airports, and other facilities use computer vision to confirm people’s identities and block unauthorized access in access control systems. For instance, Delta Air Lines uses a vision-based kiosk at La Guardia Airport to take photos of travelers and compare them with the government database to identify their identities. This speeds up check-in and boarding processes while maintaining high security.

Law Enforcement: Vision systems can scan crowds in images or videos sent from security cameras and match faces against a government database. This helps police and security agents detect suspects, locate missing individuals, and secure public areas.

Social Media Platforms: Such social networking sites as Facebook rely on advanced CV algorithms like DeepFace for face verification and photo tag suggestions. By scanning uploaded images, these techs can identify and match familiar faces in previously posted photos. This allows you to automatically tag friends and organize your photo libraries.

FaceID on Smartphones: Many smartphones now integrate facial recognition technology to help you unlock devices securely. Take Apple’s iPhones as an example. These products use a TrueDepth camera system to scan your face and compare it to secure encrypted facial data stored on the devices. This technology also enables you to log in to apps securely or verify your identity for payments.

3. Object Detection and Tracking

In addition to humans, computer vision proves useful in detecting and tracking objects in visual data. Object detection focuses on identifying and locating each object in a single frame. Meanwhile, object tracking traces these identified objects across different frames in a video. This allows the vision system to inspect their movement over time.

Vision systems can leverage traditional methods like Sliding Window, Haar Cascades, or Template Matching to identify and track simple objects with basic movements. For more sophisticated objects, they often use deep learning algorithms like YOLO, Faster R-CNN, or SSD.

Particularly, YOLO (You Only Look Once) scans the whole visual data to detect the location of each object and what it is (e.g., a cat or dog) in one go, rather than breaking down the process into many steps. Faster R-CNNs (Region-based Convolutional Neural Networks) can directly identify potential areas (“region proposals) where an object could be instead of scanning every pixel. Meanwhile, SSD (Single Shot MultiBox Detector) uses various layers to draw estimated boxes around objects of all sizes to identify their location.

Real-World Applications

Various industries have increasingly adopted object detection systems to enhance automation and streamline workflows. Below are several significant real-world applications of computer vision in this task:

Self-Driving Vehicles: Many vehicles like Tesla’s Model Y use cameras and sensors to scan their surroundings, identifying objects (e.g., pedestrians, obstacles, or other vehicles) and tracking their movement. This allows them to navigate the roads, make smart decisions (e.g., slowing down), and ensure passenger safety.

Traffic Monitoring Systems: Many places like New York, Singapore, and Barcelona use vision-based systems to monitor and analyze traffic flow. These systems use cameras installed on roads to identify and track vehicles, traffic lights, and potential obstacles (e.g., stalled vehicles). This reduces traffic congestion and improves road safety.

Robotics: Through cameras embedded, robots can understand and interact with their surroundings. In manufacturing plants like Ford or Peterson Manufacturing, for instance, vision-based robots can detect and load parts to an assembly line. Meanwhile, retailers like Walmart also use shelf-scanning robots to inspect aisles, monitor products and prices, and check for problems. Computer vision also plays a key role in robotic surgery (like in Mayo Clinic) to foster the precision of procedures.

Retail Analytics: Computer vision has wide applications in retail analytics. By identifying and following shopper movements, stores can gain insights into foot traffic patterns, shopping behavior, and popular products. Many retailers like Amazon Go integrate this technology to scan what customers pick up and automatically tally the bills, which is often known as “self-checkout systems.” This technology also detects and tracks in-store and outside objects (e.g., fire extinguishers, automatic doors, or vehicles parking in front of stores) to ensure compliance with internal policies, smooth operations, and no obstacles blocking exits.

4. Image Restoration

Computer vision makes profound impacts on image restoration. This application focuses on enhancing or restructuring low-quality images like old photos, noisy medical scans, or blurry surveillance footage. Its goal is to restore the original, clean version of the images to make it clearer or more helpful for analytics.

For effective image restoration, computer vision first captures and analyzes the local structure of the visual data. This involves looking at even the smallest regions of the image to determine features like edges, lines, or textures. The step helps a vision system interpret the image’s underlying characteristics and patterns.

Then, the system will use specialized algorithms and the analyzed information to process different types of damage. For example, deconvolution algorithms help reverse blurring effects, while such denoising algorithms as Wavelet Transform or Non-Local Means can eliminate unexpected noise. Deep learning algorithms, like CNNs and Generative Adversarial Networks (GANs), can recover degraded images with impressive accuracy.

Real-World Applications

Image restoration has some noticeable use cases as follows:

Medical Imaging: If medical images (e.g., X-rays or MRI scans) suffer from noise or blur, doctors hardly analyze images for accurate diagnosis. By enhancing image clarity, vision-based restoration can identify subtle patterns (like small tumors) that might be unnoticed.

Forensics & Law Enforcement: Vision-based image restoration techniques are extremely useful for forensic investigations and law enforcement. By recovering old photographs and low-quality images from CCTV surveillance footage or crime scenes, police can easily identify suspicious individuals or objects that can be important for an investigation.

Cultural Heritage Preservation: Various historical photos and documents suffer from damages or degradation issues over time (e.g., discoloration or fading). With advanced CV techniques, historians can bring out faded details and recover original colors.

Satellite and Aerial Imagery: Images collected from satellites or aircraft are sometimes distorted because of camera limitations, movement, or atmospheric conditions. By removing noise and improving sharpness, vision systems can address these issues, restoring images with clearly presented geographic and environmental features for later analysis.

5. Feature Matching

Feature matching is a crucial computer vision technique that detects identical features or parts between two or more images. It works by recognizing unique points in an image (“feature points”). These points, such as a building’s corners or textures, stand out from the rest of the scene. They have unique “fingerprints,” known as descriptors, to help computers identify and see whether there’s a feature match across multiple images.

Computer vision uses specialized algorithms to ensure reliable feature matches. Two of them include SURF (Speeded Up Robust Features) and SIFT (Scale-Invariant Feature Transform). The former captures unique features for simple scenes, while the latter can handle more challenging matching cases regardless of image scale.

Real-World Applications

With the mentioned capability, feature matching plays an important role in scenarios where you need to align, combine, and track images in real-time. Below are some of its real-world applications:

Image Stitching: Feature matching is widely adopted in image stitching, where various images are combined to form a larger panorama. Computer vision uses algorithms like image registration, calibration, and blending to identify overlapping regions and match the feature points. Image stitching is quite popular in tasks like document mosaicing, image stabilization, medical imaging, video stitching, and object insertion.

3D Reconstruction: Feature matching also proves helpful in creating a 3D representation of a scene or an object from different 2D images. By recognizing and matching features from multiple angles, computer vision systems can calculate the structure and depth of objects to build a realistic 3D model. This capability turns it into a valuable tool in sectors like architecture or archeology.

Augmented Reality (AR): Using your phone’s camera to capture pictures of your environment, AR applications like the IKEA Place App understand the scene, detect feature points in the real world (e.g., tables or floors), and anchor virtual objects to those features. This helps the apps place the virtual items accurately in your real-world space.

Document Scanning: Phone cameras and several applications (like Google Translate) allow you to convert images or scanned documents into editable text. This process is often known as Optical Character Recognition (OCR). It uses feature matching to identify unique feature points and align these documents for precise text recognition.

Conclusion

Now, you’re here, after a long journey of discovering common computer vision applications and its long-term growth. With impressive capabilities, computer vision has a wide application, from image classification and restoration to facial and object detection. In the future, advancements in modern technologies like GenAI will boost the growth of computer vision, giving it the ability to perform more complex tasks.

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