MatConvNetVLFeat
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Related Products
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About
The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local features extraction and matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs. Many pre-trained CNNs for image classification, segmentation, face recognition, and text detection are available.
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About
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, and stitch images together to produce a high-resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery, etc.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
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Audience
Anyone in need of a deep learning software
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Audience
Developers in search of an open-source software library providing the infrastructure to create computer vision applications and
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Supported
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Company InformationVLFeat
United States
www.vlfeat.org/matconvnet/
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Company InformationOpenCV
United States
opencv.org/about/
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Alternatives |
Alternatives |
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Categories |
Categories |
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Deep Learning Features
Convolutional Neural Networks
Supported
Document Classification
Supported
Image Segmentation
Supported
ML Algorithm Library
Not Supported
Model Training
Not Supported
Neural Network Modeling
Not Supported
Self-Learning
Not Supported
Visualization
Not Supported
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Integrations
Akira AI
Not Supported
C++
Not Supported
Dash
Not Supported
Intel Open Edge Platform
Not Supported
Java
Not Supported
MATLAB
Not Supported
OculiX
Not Supported
Python
Not Supported
Thunder Compute
Not Supported
Weasis
Not Supported
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Integrations
Akira AI
Supported
C++
Supported
Dash
Supported
Intel Open Edge Platform
Supported
Java
Supported
MATLAB
Supported
OculiX
Supported
Python
Supported
Thunder Compute
Supported
Weasis
Supported
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