Can OpenCV use for Machin learning? OpenCV – An Introduction to OpenCV.
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Can OpenCV use for Machin learning? OpenCV – An Introduction to OpenCV.
OpenCV is an open-source package/library which is aimed at real-time computer vision. The library is cross-platform – it can support Python, Java, c++, etc. It was originally developed by intel. It’s free for use under the open-source BSD license. The OpenCV library is one of the most widely used packages for implementing video detection, motion detection, video recognition image recognition, and even deep learning face recognition applications.
Few use cases for OpenCV are
2d and the 3d featured toolkits.
The facial recognition application is one of the most widely used applications of computer vision.
Gesture recognition.
Human-computer interaction
Motion understanding
Object Detection
Segmentation and Recognition
Motion Tracking.
The following are the key research areas in computer vision and image analytics:
- Deep neural networks for biometric evaluation
- Facial sentiment analysis and emotion recognition
- Real-time video analysis and recognition of key features
- Gesture recognition
- Visual representation learning
- Face smile detection
- Vein investigation and training for biometrics
- Multi-resolution approaches
- Radiomics analytics for medical data sets
- Forgery detection in digital images
- Content-based image retrieval
- Automatic enhancement of images
- Identification and classification of objects in real-time
- Defects prediction in manufacturing lines using live images of machines
- Industrial robots with a real-time vision for disaster management
- Image reconstruction and restoration
- Computational photography
- Morphological image processing
- Animate vision
- Photogrammetry
Which Companies use OpenCV?
OpenCV has more than 47 thousand people of user communities and an estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups, and governmental bodies. A lot of Top companies and many startups use OpenCV companies like Google, Yahoo, Microsoft, Intel, Sony, Honda, Toyota that employ the library. Startup companies such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV is also used in Artificial Intelligence for detecting objects.
Prominent tools for OpenCV and image analytics
There are a number of tools and libraries available to implement computer vision and image analytics.
Tools
OpenCV – Click Here
BoofCV – Click Here
NASA Vision Workbench – Click Here
SimpleCV – Click Here
Tesseract – Click Here
SLIC Superpixel – Click Here
OpenMVG – Click Here
LIBVISO – Click Here
VisualSFM – Click Here
MeshLab – Click Here
Bundler – Click Here
Vid.stab – Click Here
ViSP – Click Here
Training Data
In machine learning algorithms there is the notion of training data. Training data includes several components:
- A set of training samples. Each training sample is a vector of values (in Computer Vision it’s sometimes referred to as a feature vector). Usually, all the vectors have the same number of components (features); the OpenCV ml module assumes that. Each feature can be ordered (i.e. its values are floating-point numbers that can be compared with each other and strictly ordered, i.e. sorted) or categorical (i.e. its value belongs to a fixed set of values that can be integers, strings, etc.).
- An optional set of responses corresponding to the samples. Training data with no responses are used in unsupervised learning algorithms that learn the structure of the supplied data based on distances between different samples. Training data with responses are used in supervised learning algorithms, which learn the function mapping samples to responses. Usually, the responses are scalar values, ordered (when we deal with regression problem) or categorical (when we deal with classification problem; in this case, the responses are often called “labels”). Some algorithms, most noticeably Neural networks, can handle not only scalar but also multi-dimensional or vector responses.
- Another optional component is the mask of missing measurements. Most algorithms require all the components in all the training samples to be valid, but some other algorithms, such as decision trees, can handle the cases of missing measurements.
- In the case of a classification problem, user may want to give different weights to different classes. This is useful, for example, when:
- the user wants to shift prediction accuracy towards a lower false-alarm rate or higher hit rate.
- the user wants to compensate for significantly different amounts of training samples from different classes.
- In addition to that, each training sample may be given a weight, if user wants the algorithm to pay special attention to certain training samples and adjust the training model accordingly.
- Also, users may wish not to use the whole training data at once, but rather use parts of it, e.g. to do parameter optimization via cross-validation procedure.
As you can see, training data can have a rather complex structure; besides, it may be very big and/or not entirely available, so there is a need to make abstraction for this concept. In OpenCV ml there is cv::ml::TrainData class for that.
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