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ease
features
design
support

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Description

Alfi, Inc. specializes in crafting engaging interactive advertising experiences in public spaces. By leveraging artificial intelligence and advanced computer vision technology, Alfi enhances the delivery of advertisements tailored to individuals. Their unique AI algorithm is designed to interpret subtle facial expressions and perceptual nuances, identifying potential customers who may be particularly interested in specific products. Notably, this automation prioritizes user privacy by avoiding tracking, refraining from using cookies, and steering clear of any identifiable personal data. Advertising agencies benefit from access to real-time analytics that provide insights into interactive experiences, audience engagement, emotional responses, and click-through rates—data that has traditionally been elusive for outdoor advertisers. Additionally, Alfi harnesses the power of AI and machine learning to analyze consumer behavior, facilitating improved analytics and delivering more relevant content to enhance the overall consumer experience. This commitment to innovation positions Alfi at the forefront of the evolving advertising landscape.

Description

The VLFeat open source library offers a range of well-known algorithms focused on computer vision, particularly for tasks such as image comprehension and the extraction and matching of local features. Among its various algorithms are Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, the agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, and large scale SVM training, among many others. Developed in C to ensure high performance and broad compatibility, it also has MATLAB interfaces that enhance user accessibility, complemented by thorough documentation. This library is compatible with operating systems including Windows, Mac OS X, and Linux, making it widely usable across different platforms. Additionally, MatConvNet serves as a MATLAB toolbox designed specifically for implementing Convolutional Neural Networks (CNNs) tailored for various computer vision applications. Known for its simplicity and efficiency, MatConvNet is capable of running and training cutting-edge CNNs, with numerous pre-trained models available for tasks such as image classification, segmentation, face detection, and text recognition. The combination of these tools provides a robust framework for researchers and developers in the field of computer vision.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Alfi

Country

United States

Website

www.getalfi.com

Vendor Details

Company Name

VLFeat

Country

United States

Website

www.vlfeat.org/matconvnet/

Product Features

Computer Vision

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

Deep Learning

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

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Deep Learning

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

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