Computer vision is poised to transform industries from manufacturing to medicine. But what exactly is computer vision and why does it matter now? This comprehensive guide has all the answers.
Defining Computer Vision
Computer vision refers to a field of artificial intelligence that trains computers to interpret and understand digital images, videos and other visual inputs — and then take action based on that information.
Put another way, the goal of computer vision is to teach machines to "see" and derive meaning from visual inputs in a humanlike manner. A computer vision system can identify objects, log defects in products, guide autonomous vehicles, detect medical conditions, track moving objects and more.
How Computer Vision Works
Computer vision systems comprise three key pieces:
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Image Acquisition: A camera, sensor or other device captures visual inputs like photos or video frames and converts them into digital images composed of pixel data.
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Image Processing: Software extracts useful numeric and symbolic data from raw pixel information to prepare it for analysis. This includes techniques like normalization, noise reduction, segmentation, etc.
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Analysis: High-level machine learning algorithms process prepared image data to perform tasks like classification, anomaly detection, similarity matching with trained models and providing image-based predictions. Popular techniques include convolutional neural networks (CNNs), recurrent neural networks (RNNs) and R-CNN models.
So in summary, computer vision leverages sensors and cameras to digitize visual data, prepares that data using image processing methods and then applies artificial intelligence to analyze images and derive higher-level insights.
Key Computer Vision Techniques
Let‘s explore some of the most important computer vision techniques powering modern systems:
Image Classification
Image classification refers to labeling images based on their visual content. For example, algorithms can be trained on thousands of labeled images of cats and dogs to classify unseen images as either cat or dog. CNNs are exceptionally good at this task due to their hierarchical structure mirroring visual cognition.
Object Detection
Object detection identifies all objects in an image and draws bounding boxes around them. This allows for localizing and classifying multiple objects like cars and pedestrians in an autonomous vehicle‘s camera feed. Single Shot MultiBox Detector (SSD) provides fast, real-time region proposals for object detection.
Semantic Segmentation
Semantic segmentation divides images into meaningful segments pixel-by-pixel to classify each pixel by the object it represents. Fully Convolutional Networks efficiently perform pixel-level segmentation using an encoder-decoder architecture.
Image Generation
Generative Adversarial Networks (GANs) can create realistic synthetic images and videos. This supports applications like automatically generating training data.
So in short, modern computer vision leverages convolutional and recurrent deep neural networks to analyze visual inputs and power capabilities like classification, object detection and content generation.
Differentiating Computer Vision and Machine Vision
Computer vision represents the overarching science of machines understanding images and video using AI and deep learning. Machine vision refers to the industrial application of computer vision, often for quality inspection, metrology, barcode reading and guidance in robotics or autonomous vehicles.
So machine vision is applied computer vision for automation and manufacturing processes. Machine vision systems are embedded in factory lines and processes for real-time image analysis.
The Growing Importance of Computer Vision
Though computer vision research dates back decades, its commercial potential is hitting an inflection point today. A few key factors underline its current momentum:
1. Expanding Applications Across Industries
Computer vision holds tremendous potential for delivering business value in nearly every sector:
- Manufacturing: Automated inspection, product defect detection
- Healthcare: Medical image analysis, robotic surgery and diagnostics
- Retail: Product identification, visual search engines, cashier-less payments
- Transportation: Autonomous vehicles, advanced driver assistance systems (ADAS), smart cities
- Security: Surveillance systems, face recognition
And many more fields like agriculture, marketing, entertainment, financial services, etc.
As this ubiquity increases, enhancing computer vision capabilities remains crucial.
2. Growing Data Volumes
Deep learning depends heavily on data volume and computer vision is no exception. The explosion of images and video from phones, drones, cameras, self-driving cars, satellites and medical imaging hardware provides incredible corpuses to train computer vision models.
Startups like Scale leverage thousands of human labelers to further enrich this data. Machine learning teams across sectors devote huge resources towards collecting industry-specific training datasets.
In healthcare for example, outstanding progress in segmenting organs and identifying anomalies relies on abundant medical imaging data.
3. Technological Improvements
Robust funding, improved datasets and fierce competition drive spectacular innovation in computer vision:
- Training CNNs and R-CNNs on specialized hardware like GPUs and TPUs drastically improves speed and accuracy
- Novel neural architectures squeeze out marginal performance gains year-over-year
- Techniques like transfer learning apply models trained on large datasets (e.g. ImageNet) to new domains with modest data
So hardware improvements and refined algorithms deliver exponential gains in computer vision efficacy.
Computer Vision Applications
Computer vision unlocks breakthrough use cases across global industries:
Manufacturing
Smart cameras instantly identify manufacturing or packaging defects throughout product lines, facilities and warehouses. For example, a bottling plant AI spots cracked glass, overflowing liquids or misaligned labels. These visual inspections substantially reduce waste and downtime. Market leader Cognex provides a suite of manufacturing computer vision software.
Retail
Brick and mortar retailers install computer vision software to analyze in-store activity. Video feeds capture granular data like customer counts, dwell times at shelves, demographics and queue lengths. Cloud vision APIs deliver this intel to improve layouts, staff planning and personalized offers. Additionally, e-commerce sites rely on computer vision behind visual search tools allowing queries based on images rather than keywords.
Healthcare
Computer vision assists doctors by automating analysis of medical images for faster, more accurate screening and diagnosis. As examples, algorithms identify diabetic retinopathy indicators in retinal scans or classify tissue types in MRIs far quicker than manual evaluation. PathAI and Zebra Medical are leading startups delivering healthcare computer vision software to augment clinicians.
So in short, computer vision unlocks automation, efficiency and innovation across nearly every industry vertical.
The Future of Computer Vision
Computer vision sits at the forefront of commercial AI and its future shines bright. With virtually infinite use cases across global markets, seemingly boundless data growth and rapid technological progress, computer vision will create tremendous value over the next decade.
IDC predicts the computer vision market to balloon from $10 billion in 2020 to over $40 billion in just 3 years. MarketsandMarkets forecasts even larger growth climbing to $60 billion by 2027. With no slowdown in sight, computer vision promises to transform business and society for years to come.