Nowadays Machines are able to identify places, people, objects, and things in images with accuracy and high efficiency with the help of Computer vision on AWS i.e. Amazon Rekognition. Generally built with deep learning models, it can classify and understand useful information from the images. The image data can be in any form such as video and set of images.
Amazon offers a computer vision service i.e. Amazon Rekognition. It is simple and quick to integrate deep learning-based visual search and image analysis into our applications. In this blog post, we are going to cover everything about Amazon Rekognition (computer vision on AWS).
In this blog, we are going to cover:
- What Is Amazon Rekognition?
- Key Features Of Amazon Rekognition
- Computer Vision Benefits And Use Cases
- FAQs
What Is Amazon Rekognition?
- Amazon Rekognition is a service that makes it easy to add image and video analysis to our application using deep learning technology that requires no mastering in machine learning.
- With Amazon Rekognition, we can easily identify text, objects, scenes, and activities in images and videos.
- It provides facial analysis and facial search capabilities with high accuracy. We can easily detect and compare faces user verification, people counting, and human safety use cases.
- It can identify the objects and scenes in images that are exact to your business needs.
Key Features Of Amazon Rekognition
1) Labels
Amazon Rekognition can identify hundreds or thousands of objects like cars, bikes, mobile phones, buildings, and so many objects. It is also capable of scenes like parking lots, beach, city. When you analyzing video, you can easily identify different activities such as “delivering a package” or “playing soccer”.
2) Custom Labels
Amazon Rekognition Custom Labels can find the objects and scenes in images that are exact to your business needs. For example, you can identify your logo in social media posts, find your products on store shelves, segregate machine parts in an assembly line, figure out healthy and infected plants, or spot animated characters in videos.
3) Content Moderation
Amazon Rekognition can easily catch content that is inappropriate, offensive, or unwanted. With Rekognition moderation APIs in broadcast media, social media, and e-commerce situations to make a safer user experience. Amazon Rekognition accurately controls what you want to allow based on your needs.
4) Text Detection
Amazon Rekognition can easily detect text in videos and images. Then it converted into the detected text to machine-readable text. You can use this text to implement solutions such as:
- Content insights
- Visual search
- Navigation
- Filtering
5) Face Analysis And Detection
With Amazon Rekognition, you can quickly and simply detect when faces appear in images and videos and get characteristics such as gender, age range, eyes open, glasses, facial hair for each. In the video, you can also find out how these face characteristics change over time, such as constructing a timeline of the emotions expressed by an artist.
6) Face Verification And Search
Amazon Rekognition brings fast and exact face search, allowing you to find a person in a photo or video using your own repository of face images. You can also authenticate identity by analyzing a face image against images you have saved for comparison.
7) Celebrity Recognition
Amazon Rekognition can quickly find out well-known people in your image and video libraries to catalog footage and photos for advertising, marketing, and media industry use cases.
8) Workplace Safety
With Amazon Rekognition, you can figure out images from your on-premises system devices (IoT sensors, cameras) at scale to automatically detect if persons in images are wearing Personal Protective Equipment (PPE) such as hand covers (gloves), face covers (face masks), and headcovers (helmets) and whether the protective equipment covers the corresponding body part (nose for face covers, head for head covers, and hands for hand covers).
Computer Vision Benefits And Use Cases
1) Home Security And Public Safety
Computer vision with image and facial recognition helps instantly identify unlawful entries or persons of interest, resulting in safer communities and a more efficient way of deterring crimes.
2) Autonomous Driving
With computer vision technologies. Auto manufacturers can provide upgraded and safer self-driving car navigation realizing the aim of developing autonomous driving a reality and a reliable transportation option.
3) Enhanced And Authentication Computer-human Interaction
Enhanced human-computer interaction enhances customer satisfaction such as present products based on customer sentiment analysis in retail outlets or faster banking services with rapid authentication based on customer identity and preferences.
4) Manufacturing Process Control
Well-trained computer vision integrated into robotics improves quality support and operational efficiencies in manufacturing applications, resulting in more reliable and cost-effective products.
5) Medical Imaging
Medical image analysis with computer vision can immeasurably enhance the accuracy and speed of a patient’s medical diagnosis, resulting in better cure outcomes and life expectancy.
6) Content Analysis And Management
With millions of images uploaded every day to media and social channels. The use of computer vision technologies such as metadata extraction and image analysis immeasurably enhance efficiency and earning opportunities.
FAQs
Q: What is deep learning?
Answer: Deep learning is a sub-set of ML and a significant branch of AI. Its goal to infer high-level abstractions from unprocessed data by using a deep graph with multiple processing layers composed of multiple linear and non-linear transformations. Deep learning is generally based on models of information converting and communication in the brain. Deep learning takes over handcrafted features with ones learned from very large amounts of annotated data.
Q: Do I need any deep learning proficiency to use Amazon Rekognition?
Answer: No, With Amazon Rekognition, you don’t have to create, maintain, or upgrade deep learning pipelines.
Q: What is a label?
Answer: A label is an object, scene, or concept found in an image based on its contents. For example, a picture of people on a tropical beach may contain labels such as ‘Water’, ‘Person’, ‘Palm Tree’, ‘Sand’, and ‘Swimwear’ (objects), ‘Beach’ (scene), and ‘Outdoors’ (concept).
Related References
- AWS Certified Machine Learning Specialty: All You Need To Know
- AWS Database Services – Amazon RDS, Aurora, DynamoDB, ElastiCache
- Multi-Account Management Using AWS Organizations
- AWS Certified Solutions Architect: Roles & Responsibilities
- Amazon Kinesis Overview, Features And Benefits
- AWS Route 53 Introduction
- Create And Connect To Amazon AWS Windows EC2 Instance
- Introduction To Amazon SageMaker Built-in Algorithms
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