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Lightweight Behavior Driven Development test framework MbUnit: Yes [394] Extensible, model-based nUnit compatible framework. Part of the Gallio Test Automation Platform. Moq: Yes [395] Moq is a .NET Framework library for creating mock objects. It leverages C# 3.0 lambda expressions, typically used in Test Driven Development. MSTest: No
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
This is a high performance, typesafe numerical array set of classes and functions for general math, FFT and linear algebra. The library, developed for .NET/Mono, aims to provide 32- and 64-bit script-like syntax in C#, 2D & 3D plot controls, and efficient memory management. It is released under GPLv3 or commercial license. [10]
C# (/ ˌ s iː ˈ ʃ ɑːr p / see SHARP) [b] is a general-purpose high-level programming language supporting multiple paradigms.C# encompasses static typing, [16]: 4 strong typing, lexically scoped, imperative, declarative, functional, generic, [16]: 22 object-oriented (class-based), and component-oriented programming disciplines.
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]
The flyweight pattern is useful when dealing with a large number of objects that share simple repeated elements which would use a large amount of memory if they were individually embedded. It is common to hold shared data in external data structures and pass it to the objects temporarily when they are used.
Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. [ 1 ]
This works better for planar surface recognition than 3D object recognition since the affine model is no longer accurate for 3D objects. In this journal, [25] authors proposed a new approach to use SIFT descriptors for multiple object detection purposes. The proposed multiple object detection approach is tested on aerial and satellite images.