Hand Gesture Recognition Using OpenCv and Python

Harini, V.; Prahelika, V.; Sneka, I.; Ebenezer, P. Adlene

doi:10.1007/978-3-030-41862-5_174

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The technique proposed by Viola and Jones is highlighted and, later, improved by Lienhart and Maydt, which is based on Haar”s wavelets [ 3 ], which perform a multiresolution analysis of the image. In addition, the OpenCV library [ 4 ] includes functions that allow finding the hand and face through AdaBoost-type parallel classifiers [ 5 ], which yield excellent results. Other authors use morphological techniques such as skeletonization to identify organs in the human body [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Human‐Computer Interaction Using Manual Hand Gestures in Real Time

Alsaffar

Alshammari

et al. 2021

Computational Intelligence and Neuroscience

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This paper describes the construction of an electronic system that can recognise twelve manual motions made by an interlocutor with one of their hands in a situation with regulated lighting and background in real time. Hand rotations, translations, and scale changes in the camera plane are all supported by the implemented system. The system requires an Analog Devices ADSP BF-533 Ez-Kit Lite evaluation card. As a last stage in the development process, displaying a letter associated with a recognized gesture is advised. However, a visual representation of the suggested algorithm may be found in the visual toolbox of a personal computer. Individuals who are deaf or hard of hearing will communicate with the general population thanks to new technology that connects them to computers. This technology is being used to create new applications.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Human‐Computer Interaction Using Manual Hand Gestures in Real Time

Alsaffar

Alshammari

et al. 2021

Computational Intelligence and Neuroscience

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“…is type of infrared shot is associated with night shots or the possibility of seeing in very dark situations [2].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Using Depth Cameras for Recognition and Segmentation of Hand Gestures

Alhamazani

Alshudukhi

Alharbi

et al. 2021

Advances in Materials Science and Engineering

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In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions.

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“…The Cv2.FindContours method [23] was used in OpenCV to select object contours on the frame. The method takes an image on which you want to find contours.…”

Section: Fig 3 Application Flowchartmentioning

confidence: 99%

Using OpenCV for real-time image recognition through augmented reality devices

Dinara,

Evgeniy,

Vlada

2023

Proceedings of International Conference on Artificial Life and Robotics

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This article describes the peculiarities of writing libraries and the rapid development of augmented reality applications on the Unity platform and the OpenCV open-source library. The application of OpenCV functions to work with augmented reality objects is discussed in detail. On their basis, algorithms and methods were developed and an open library was created that implements the stable recognition of objects in various conditions by means of binding to the marker and their visualization on the augmented reality devices. On the basis of the developed library, a complete application was created to illustrate the possibility of applying virtual reality technologies for optical control of the assembly of radio electronic boards and for personnel training.

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Hand Gesture Recognition Using OpenCv and Python

Cited by 6 publications

References 3 publications

[Retracted] Human‐Computer Interaction Using Manual Hand Gestures in Real Time

[Retracted] Human‐Computer Interaction Using Manual Hand Gestures in Real Time

[Retracted] Using Depth Cameras for Recognition and Segmentation of Hand Gestures

Using OpenCV for real-time image recognition through augmented reality devices

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