TECHNOLOGY
Computer Vision
Unlock the potential of your visual data to personalise experiences, streamline and automate operations, and accelerate growth and efficiency.
What is Computer Vision?
Computer vision is a subset of artificial intelligence (AI) that enables systems to interpret and make decisions based on visual data. It's like teaching a computer to see and understand the world similarly to how humans do.
For instance, just as you recognise a friend’s face in a crowd, computer vision algorithms can identify objects in images, analyse medical scans, or enable autonomous vehicles to navigate safely.
How does it work?
01
Data collection
Gathering relevant data from various sources.
02
Data preparation
Cleaning and organizing data to make it suitable for analysis
03
Model training
Using algorithms to train a model on the prepared data
04
Model evaluation
Gathering relevant data from various sources.
05
Model deployment
Gathering relevant data from various sources.
06
Monitoring and maintenance
Gathering relevant data from various sources.
How businesses are using
Computer Vision
From enhancing safety to providing personalised experiences,
computer vision is transforming various industries.
Healthcare
Computer vision can analyse medical images for disease detection and treatment planning. It significantly improves diagnostic accuracy and speeds up the diagnosis process, leading to better patient outcomes.
Case Study: IBM Watson Health uses computer vision to help doctors diagnose and treat patients more accurately. This technology assists in analyzing complex medical images to detect conditions such as cancer and cardiovascular diseases early.
Use cases:
- Disease detection from medical imaging.
- Surgical assistance with real-time image analysis.
- Patient monitoring and anomaly detection.
- Automated analysis of pathology results
Healthcare
Computer vision can analyse medical images for disease detection and treatment planning. It significantly improves diagnostic accuracy and speeds up the diagnosis process, leading to better patient outcomes.
Case Study: IBM Watson Health uses computer vision to help doctors diagnose and treat patients more accurately. This technology assists in analyzing complex medical images to detect conditions such as cancer and cardiovascular diseases early.
Use cases:
- Disease detection from medical imaging.
- Surgical assistance with real-time image analysis.
- Patient monitoring and anomaly detection.
- Automated analysis of pathology results
Finance
Financial institutions use computer vision for facial recognition to enhance security and automate processes like cheque deposit via mobile apps. This technology also helps in preventing fraud and ensuring compliance with regulations.
Case Study: HSBC uses facial recognition technology to enhance the security of its mobile banking services. Customers can log in to their accounts and authorise transactions using facial recognition, providing a seamless and secure banking experience.
Use cases:
- Facial recognition for secure customer authentication.
- Automated processing of financial documents.
- Detection of fraudulent activities.
- Enhanced compliance with KYC (Know Your Customer) regulations.
Retail
Retailers leverage computer vision for personalised marketing and enhancing customer experiences. This technology is used to analyze customer behavior, manage inventory, and even create cashier-less stores.
Case Study: Amazon Go stores use computer vision to create a cashier-less shopping experience. Customers can pick up items and walk out, with their purchases automatically charged to their Amazon account.
Use cases:
- Customer behavior analysis for personalized marketing.
- Automated checkout systems.
- Real-time inventory management.
- In-store security and theft prevention.
Manufacturing
In manufacturing, computer vision helps with quality control and predictive maintenance. It ensures products meet quality standards and helps in maintaining equipment by predicting failures before they occur.
Case Study: Siemens uses computer vision to predict equipment failures and optimise production processes. This technology helps in maintaining high-quality standards and reducing downtime.
Use cases:
- Automated quality control inspections.
- Predictive maintenance of equipment.
- Monitoring production lines for efficiency.
- Safety compliance and hazard detection.
The Core Capabilities of
Computer Vision
Image Recognition
Identifying objects and features in images. It helps in applications like photo tagging, medical image analysis, and surveillance.
Practical Use Cases:
01
Automatically identify and label people and objects in photos (e.g., Facebook photo tagging).
02
Detect anomalies in medical images for early diagnosis of diseases (e.g., identifying tumors in MRI scans).
03
Recognise objects for autonomous driving (e.g., Tesla’s self-driving cars).
04
Inspect products on assembly lines to detect defects (e.g., quality control in manufacturing).
Object Detection
Locating and identifying objects within an image or video. It’s used in security systems, autonomous vehicles, and industrial inspection.
Practical Use Cases:
01
Enhance security systems by detecting intruders in real-time (e.g., surveillance cameras in smart cities).
02
Enable autonomous vehicles to detect and respond to obstacles (e.g., Waymo's self-driving cars).
03
Perform industrial inspection by identifying defects or issues in equipment (e.g., detecting cracks in pipelines).
04
Aid in retail analytics by tracking customer movements and behaviours in stores (e.g., heatmaps in stores for product placement).
Facial Recognition
Detecting and recognising human faces in images and videos. It’s widely used for authentication, security, and personalised user experiences.
Practical Use Cases:
01
Authenticate users through facial recognition for secure access (e.g., Face ID on iPhones).
02
Enhance security by identifying individuals in public spaces (e.g., airport security systems).
03
Provide personalised customer experiences in retail (e.g., VIP customer recognition in stores).
04
Monitor attendance in educational institutions and workplaces (e.g., automatic attendance systems).
Video Analysis
Analysing video content to detect and track objects or activities. It’s crucial for surveillance, activity recognition, and event detection.
Practical Use Cases:
01
Enhance surveillance by automatically detecting suspicious activities (e.g., identifying loitering or abandoned objects in public areas).
02
Track and analyse sports performance by monitoring players and movements (e.g., performance analytics in football).
03
Monitor traffic and detect incidents in real-time (e.g., smart traffic management systems).
04
Analyse customer behaviour in retail environments (e.g., tracking customer paths in stores)
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Advanced Computer Vision Techniques
The table below dives deeper into advanced computer vision techniques. These techniques require significant computational resources and expertise for implementation.
Criteria
Convolutional Neural Networks (CNNs)
Generative Adversarial Networks (GANs)
Transfer Learning
Definition
A class of deep neural networks, most commonly applied to analysing visual imagery
A class of machine learning frameworks where two neural networks contest with each other to create new, synthetic instances of data
Utilising a pre-trained model on a new, related problem
Goal
Automatically and accurately recognise patterns in images
Generate new, realistic images by learning the distribution of the original dataset.
Leverage existing models to reduce training time and improve performance on new tasks.
Algorithms
Convolutional layers, pooling layers, fully connected layers.
Discriminator and generator network
Fine-tuning pre-trained models, domain adaptation.
Data Requirement
Requires large amounts of labelled image data.
Requires substantial data for both networks to learn the data distribution
Requires less data than training a model from scratch, using pre-trained models.
Advantages
High accuracy in image classification tasks, ability to capture spatial hierarchies in images.
Capable of generating high-quality synthetic images, useful for data augmentation.
Significantly reduces training time and resources, improves performance with less data.
Applications
Image classification, object detection, facial recognition, medical image analysis.
Image generation, data augmentation, image-to-image translation.
Custom image classification, object detection, semantic segmentation.
Techniques
Backpropagation, activation functions (ReLU), dropout regularisation.
Adversarial training, optimisation of generator and discriminator.
Model fine-tuning, transfer learning architectures like VGG, ResNet.
How to choose the right AI platform
for Computer Vision
When selecting an AI platform for computer vision, consider the following aspects to ensure it meets your specific needs:
Data Storage and Management: Ensure the platform can handle large volumes of visual data efficiently. Examples: Google Cloud Storage, AWS S3.
Data Annotation Tools: Look for integrated tools for labelling and annotating images and videos. Examples: Labelbox, Amazon SageMaker Ground Truth.
Data Storage and Management: Ensure the platform can handle large volumes of visual data efficiently. Examples: Google Cloud Storage, AWS S3.
Data Annotation Tools: Look for integrated tools for labelling and annotating images and videos. Examples: Labelbox, Amazon SageMaker Ground Truth.
Pre-trained Models: Check if the platform provides access to pre-trained models to accelerate development. Examples: TensorFlow Hub, PyTorch Hub.
Custom Model Training: Ensure the platform supports custom model training for specific use cases. Examples: Google AI Platform, Azure Machine Learning.
Scalability: The platform should scale computational resources dynamically to handle intensive training tasks. Examples: Google Kubernetes Engine (GKE), Amazon EC2
Model Deployment: Look for seamless deployment capabilities to integrate models into your existing systems. Examples: AWS SageMaker, TensorFlow Serving.
Edge Deployment: If your application requires edge computing, ensure the platform supports deploying models on edge devices. Examples: AWS IoT Greengrass, Google Cloud IoT.
Real-time Monitoring: The platform should offer tools to monitor model performance and accuracy in real-time. Examples: Azure Monitor, Google Stackdriver.
Model Updating and Retraining: Ensure the platform supports continuous integration and deployment (CI/CD) pipelines for updating and retraining models. Examples: Jenkins, GitLab CI/CD.
Data Security: The platform should comply with industry standards for data security and privacy. Examples: AWS Shield, Google Cloud Security.
Compliance: Ensure the platform adheres to regulatory requirements relevant to your industry. Examples: GDPR compliance, HIPAA compliance.
Technical Support: Look for platforms that offer robust technical support and documentation. Examples: AWS Support, Google Cloud Support.
Community and Ecosystem: A strong user community and ecosystem can provide valuable resources and third-party integrations. Examples: TensorFlow Community, PyTorch Community.
Top Trends Shaping the Future
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Questions & answers
What is Computer Vision?
Computer vision is a branch of artificial intelligence that enables systems to interpret visual data and make decisions based on it. With computer vision algorithms, systems can identify patterns and make decisions based on historical visual data.
Why is Computer Vision important for my business?
Computer vision technology can streamline your business since it automates processes, improves decision-making, and uncovers insights from your visual data. Implementing computer vision leads to increased efficiency and cost savings.
Can Computer Vision be customised for my industry?
Absolutely. Computer vision is highly adaptable and can be tailored to meet the unique needs of various industries including healthcare, finance, retail, and manufacturing.
What support do you offer for Computer Vision projects?
We provide comprehensive support throughout the entire lifecycle of your computer vision project: initial consultation, data collection and preparation, algorithm selection, model training, deployment, and ongoing maintenance. Our goal is to ensure the success of your computer vision initiatives.
Do I need a lot of data to use Computer Vision?
The amount of data required depends on the specific project and chosen algorithms. We can help you assess your data readiness and explore strategies for maximising its value.
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