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The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Cropping is the removal of unwanted outer areas from a photographic or illustrated image. The process usually consists of the removal of some of the peripheral areas of an image to remove extraneous visual data from the picture, improve its framing, change the aspect ratio, or accentuate or isolate the subject matter from its background.
GrabCut is an image segmentation method based on graph cuts.. Starting with a user-specified bounding box around the object to be segmented, the algorithm estimates the color distribution of the target object and that of the background using a Gaussian mixture model.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
OpenCV has Python bindings with a rich set of features for computer vision and image processing. [211] Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch, scikit-learn and the Logic language ProbLog.
Virtual Fixtures – first AR system, U.S. Air Force, Wright-Patterson Air Force Base (1992). Augmented reality (AR) is an interactive experience that combines the real world and computer-generated 3D content.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.