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What is Physna?

Our state-of-the-art geometric deep learning solution forges a direct link between physical objects and digital coding. We are transforming the fields of engineering, industrial design, and procurement by empowering innovators and creators through the reimagining of every 3D model. Utilizing proprietary algorithms in conjunction with advanced geometric deep learning, Physna converts 3D models into rich data sets that can be understood by a multitude of software applications. This groundbreaking technology enables 3D models to be examined and manipulated like conventional code, effectively bridging the gap between the tangible world and the software-driven digital landscape. The platform analyzes CAD files and various 3D models, generating a structured representation termed the "Physna DNA" for each model. This digital format allows Physna to showcase subtle differences and resemblances across models, even those that may be incomplete or in differing formats. Moreover, it grants insight into all elements within complex assemblies, including components nested within others, thereby enhancing comprehension of intricate designs. In addition, this revolutionary approach not only fosters innovation but also leads to improved collaboration and productivity across diverse sectors, encouraging a more integrated future.

What is Amazon Rekognition?

Amazon Rekognition streamlines the process of incorporating image and video analysis into applications by leveraging robust, scalable deep learning technologies, which require no prior machine learning expertise from users. This advanced tool is capable of detecting a wide array of elements, including objects, people, text, scenes, and activities in both images and videos, as well as identifying inappropriate content. Additionally, it provides accurate facial analysis and search capabilities, making it suitable for various applications such as user authentication, crowd surveillance, and enhancing public safety measures. Furthermore, the Amazon Rekognition Custom Labels feature empowers businesses to identify specific objects and scenes in images that align with their unique operational needs. For example, a company could design a model to recognize distinct machine parts on an assembly line or monitor plant health effectively. One of the standout features of Amazon Rekognition Custom Labels is its ability to manage the intricacies of model development, allowing users with no machine learning background to successfully implement this technology. This accessibility broadens the potential for diverse industries to leverage the advantages of image analysis while avoiding the steep learning curve typically linked to machine learning processes. As a result, organizations can innovate and optimize their operations with greater ease and efficiency.

Media

Media

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Orange Logic OrangeDAM
Qrvey
Quickwork
Trendzact
Unremot
Visionati
n8n

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Orange Logic OrangeDAM
Qrvey
Quickwork
Trendzact
Unremot
Visionati
n8n

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Physna

Date Founded

2015

Company Location

United States

Company Website

physna.com

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/rekognition/

Categories and Features

3D Modeling

2D Drawing
Animation
Annotations
Bill of Materials
Character Modeling
Collaboration Tools
Component Library
Data Import / Export
For 3D Printing
For Architects
For Manufacturers
Reference Management
Simulation

Computer Vision

Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Categories and Features

Computer Vision

Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Content Moderation

Artificial Intelligence
Audio Moderation
Brand Moderation
Comment Moderation
Customizable Filters
Image Moderation
Moderation by Humans
Reporting / Analytics
Social Media Moderation
User-Generated Content (UGC) Moderation
Video Moderation

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Emotion Recognition

Facial Emotions
Facial Expression Analysis
Machine Learning
Photo Emotions
Speech Emotions
Video Emotions
Written Text Emotions

OCR

Batch Processing
Convert to PDF
ID Scanning
Image Pre-processing
Indexing
Metadata Extraction
Multi-Language
Multiple Output Formats
Text Editor
Zone Selection Tool

People Counting

API
Anonymous Counting
Benchmarking
Car Counting
Conversion Tracking
Data Export
Events Statistics
Heatmaps
Mood/Age/Gender Recognition
Motion Detection
Reporting / Analytics
Retail Counting
Staff Exclusion
WiFi Tracking
Zone / Area Monitoring

Session Replay

Eye Tracking
Form Analytics
Heatmaps
Mouse Tracking
Optimization Tools
Session Recording
Surveys
User Experience Analysis
User Feedback
Visitor Segmentation

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