OpenCV using Python in Machine Learning

OpenCV using Python in Machine Learning

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Open Source Computer Vision (OpenCV) is a cross-platform, free library used to build real-time computer vision applications. It predominantly focuses on processing and analyzing images and video captures. Identifying similar images, face detection, and object detection are some of the commonly used features. It supports Machine Learning and Deep Learning frameworks. OpenCV runs on different platforms such as Windows, Linux, macOS, Android, and iOS. It supports programming languages such as C++, C, Python, Java, and so on.

OpenCV using Python

OpenCV-Python is a library set of Python bindings optimized to address computer vision needs. Leveraging the best features of the OpenCV, C++ API, and the Python language it serves as the Python API for OpenCV. OpenCV-Python extensively uses the NumPy Python package which is designed for numerical operations and scientific computing. It also uses python packages such as SciPy and Matplotlib that are compatible and easy to integrate.

OpenCV using Python in Machine Learning

Machine Learning (ML) enables computers, mobile devices, and other machines to access, learn, and interpret data. Machine Learning uses smart algorithms to learn and make data-driven decisions. It aids other technologies such as Artificial Intelligence, Robotics, and so on. NumerousPython Machine Learning libraries and modules are available.

Some of the popular OpenCV Python algorithms are:

  • Artificial Neural Networks (ANN):It is a collection of connected units called artificial neurons. A signal transmission can happen between artificial neurons. Multi-layer perceptrons (MLP) is a well-known neural networks type. One or more neurons in each MLP layer is directly connected with the neurons from the preceding and the subsequent layers. A classical random sequential back-propagation algorithm and a batch RPROP algorithm (default) are implemented to trainthe MLPs.
  • Support Vector Machines (SVM):It is a supervised classification algorithm that creates a dividing line between diverse categories of data. This technique is a representation of the examples as points in space. The points are plotted such that the examples of distinct categories are divided by a clear zone. Upcoming examples are then charted into the same space and identified to fit into a category based on which side of the zone they are present.
  • K-Nearest Neighbors (KNN):It is a supervised learning algorithm that analyzes all the training samples and predicts the response for a new sample. It makes predictions based on the analysis ofa K value of the nearest neighbors of the sample. KNN is also called as “Learning by Example” technique.
  • K-Means Clustering:It is an unsupervised learning algorithm used to address clustering problems. The algorithm initiates with randomly selected points and uses a distance formula to find the best grouping of data points to optimize the clusters.
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