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What is Overview?

Reliable and adaptable computer vision systems designed for various manufacturing environments are at your disposal. We expertly incorporate AI and image capture into every stage of the production workflow. Overview’s inspection systems utilize cutting-edge deep learning techniques, allowing for more consistent error detection across diverse situations. Our solutions enhance traceability and offer remote access and support, creating a detailed visual record for each item manufactured. This capability facilitates quick identification of production issues and quality problems. Whether you are starting the digitization of your inspection processes or looking to upgrade an underperforming vision system, Overview provides solutions aimed at reducing waste within your manufacturing operations. By experiencing the Snap platform, you can see how we can boost your factory's operational efficiency. Our automated inspection solutions, powered by deep learning, greatly improve defect detection rates, resulting in higher yields, enhanced traceability, and an easy setup process, all supported by outstanding assistance. Ultimately, our dedication to innovation guarantees that your manufacturing processes remain cutting-edge and effective. Join us in transforming your production capabilities today.

What is MatConvNet?

The open source library VLFeat provides an extensive selection of renowned algorithms aimed at computer vision, excelling in tasks like image understanding and the matching and extraction of local features. Its diverse set of algorithms includes 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 others. Written in C for optimal performance and compatibility, it features MATLAB interfaces that improve user accessibility and is supported by detailed documentation. This library works seamlessly across various operating systems such as Windows, Mac OS X, and Linux, which enhances its usability across multiple platforms. Furthermore, the MatConvNet toolbox is specifically crafted for MATLAB, focusing on the implementation of Convolutional Neural Networks (CNNs) for a range of computer vision tasks. Renowned for its user-friendliness and efficiency, MatConvNet allows for the execution and training of advanced CNNs, offering numerous pre-trained models suited for applications like image classification, segmentation, face detection, and text recognition. The synergistic use of these powerful tools delivers a comprehensive framework that supports researchers and developers in advancing their projects in computer vision, ensuring they are equipped with cutting-edge resources and capabilities. This combination fosters innovation within the field by enabling seamless experimentation and development.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

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

Overview

Date Founded

2022

Company Location

United States

Company Website

overview.ai/

Company Facts

Organization Name

VLFeat

Company Location

United States

Company Website

www.vlfeat.org/matconvnet/

Categories and 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

Categories and 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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