Computer Vision
Overview
Computer vision is a field of computer science that focuses on enabling computers to identify and understand objects and people in images and videos. Like other types of AI, computer vision seeks to perform and automate tasks that replicate human capabilities. In this case, computer vision seeks to replicate both the way humans see, and the way humans make sense of what they see.
The range of practical applications for computer vision technology makes it a central component of many modern innovations and solutions. Computer vision can be run in the cloud or on premises.
Object detection and recognition.
The talk that might change the way you think about the gratitude for yourself and others
Image and video analysis.
Everyone needs a time just for itself – learn the importance of your free time
Facial recognition and biometric solutions.
If you’re feeling scattered, overwhelmed or are preparing for a major life change, I can help.
How does it work?
( Hint - it's easy! )
Computer vision applications use input from sensing devices, artificial intelligence, machine learning, and deep learning to replicate the way the human vision system works. Computer vision applications run on algorithms that are trained on massive amounts of visual data or images in the cloud. They recognize patterns in this visual data and use those patterns to determine the content of other images.
How an image is analyzed with computer vision
A sensing device, such as a camera or medical imaging device, captures an image. This image is sent to an interpreting device, which uses pattern recognition to analyze and compare the image against a library of known patterns, identifying matches based on general features or unique identifiers. When a user requests information about the image, the interpreting device provides the analysis results.
Underlying Technologies
- Algorithms: Deep learning, convolutional neural networks (CNNs), and image processing techniques.
- Frameworks: TensorFlow, PyTorch, OpenCV, and Keras.
- Hardware: GPUs for accelerated processing, specialized AI chips.
Image Processing Capabilities
- Object Detection: Identifying and classifying objects within images.
- Image Segmentation: Dividing an image into meaningful segments.
- Facial Recognition: Detecting and verifying faces with high accuracy.
Integration Capabilities
- APIs: RESTful APIs for seamless integration with existing systems.
- Compatibility: Works with various data sources and formats (images, videos, real-time feeds).
- Cloud & On-Premises: Flexible deployment options to suit different infrastructure needs.
Security Measures
- Data Privacy: Adherence to data protection regulations (e.g., GDPR, CCPA).
- Encryption: Secure data transmission and storage using advanced encryption methods.
- Access Control: Role-based access and authentication to safeguard sensitive information.
Performance Metrics
- Accuracy: High precision in detecting and recognizing objects and patterns.
- Speed: Real-time processing capabilities for live video feeds and rapid image analysis.
- Scalability: Capable of handling large volumes of data and scaling as per demand.
Support and Maintenance
- Ongoing Updates: Regular updates to algorithms and models to improve accuracy and performance.
- Customer Support: Dedicated support team for troubleshooting and assistance.
- Training & Documentation: Comprehensive resources for easy implementation and use.
Computer vision applications use input from sensing devices, artificial intelligence, machine learning, and deep learning to replicate the way the human vision system works. Computer vision applications run on algorithms that are trained on massive amounts of visual data or images in the cloud. They recognize patterns in this visual data and use those patterns to determine the content of other images.
START CREATING TODAY
Empower your business with cutting-edge AI solutions from Drifko
Contact us today!