Computer Vision Based Human-Computer Interaction Using Color Detection Techniques

Dhule, Chetan; Nagrare, Trupti

doi:10.1109/csnt.2014.192

Cited by 15 publications

(3 citation statements)

References 8 publications

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“…For HCI, ref. [ 34 ] proposed a hand gesture detection system based on vision techniques. The authors developed a real-time application that uses a color-based detection method instead of ANN training to restrict mouse movement inside windows.…”

Section: Significant Research Work On Hand Gesture Recognitionmentioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

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Section: Significant Research Work On Hand Gesture Recognitionmentioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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“…Another method involves filtering an image to isolate a predefined set of colors and then determining whether a desired color is present within the filtered image [24]. In the context of gesture-based human-computer interaction, real-time tracking of hand and finger motion can be achieved by calculating changes in pixel values of RGB colors from a video, without the need for artificial neural network training [25]. In the textile industry, computer vision techniques are used for color measurement and evaluation.…”

Section: B Color Detection Techniques In Computer Visionmentioning

confidence: 99%

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

Mishra,

Trung,

Ngoc

et al. 2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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This paper presents an innovative approach for immersive virtual painting using real-time computer vision techniques. A meticulously crafted color detection algorithm implemented in C++ andOpenCV achieves up to 97.4% accuracy in identifying specified hues from live video feeds. The detected colors are seamlessly translated into vibrant brush strokes rendered on a digital canvas in real-time. The algorithms exhibit remarkable speed, analyzing each frame within 15ms, enabling ultra-low latency painting interactions. Optimization strategies involving parallel processing and code optimizations provide further performance gains. Comparative analysis reveals 3-4x faster execution using C++ over Python for color detection. The platform delivers an intuitive, natural, and uninterrupted painting experience, as validated through user studies. By automating color detection and digital rendering, this research transforms virtual painting from a passive activity to an immersive form of human-computer co-creativity. The fusion of computer vision, rendering algorithms, and optimization techniques establishes new frontiers in interactive digital art platforms and reshapes human-computer collaboration. Highlights:• This research achieves exceptional accuracy in real-time color detection, with up to 97.4% precision in identifying specified hues across thousands of video frames.• The integrated system enables seamless user interaction for natural virtual painting expressions, eliminating disruptive color selection interruptions.• Comparative analysis reveals significant 3-4x performance gains by implementing the algorithms in C++ instead of Python, underscoring the efficiency benefits of C++ for realtime computer vision applications.• User studies validate the immersive experience delivered by the platform, with users highlighting the responsiveness, precision, and intuitive interaction unmatched by traditional virtual painting tools.• The proposed techniques establish a new paradigm in realtime computer vision, pushing the boundaries of virtual creativity platforms and reshaping human-computer collaboration in the arts.

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“…ANN training is comparatively slow. D h u l e and N a g r a r e [29] have developed a real-time motion control of mouse using the change in pixel values of RGB colours in the input video. L i n and D i n g [30] use histograms of oriented gradients features and information of motion trajectory in a natural environment.…”

Section: Hand Gesture Recognitionmentioning

confidence: 99%

Face Authenticated Hand Gesture Based Human Computer Interaction for Desktops

Mukthineni

Sharma

et al. 2020

Cybernetics and Information Technologies

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Hand gesture detection and recognition is a cutting-edge technology that is getting progressively applicable in several applications, including the recent trends namely Virtual Reality and Augmented Reality. It is a key part of Human-Computer Interaction which gives an approach to two-way interaction between the computer and the user. Currently, this technology is limited to expensive and highly specialized equipment and gadgets such as Kinect and the Oculus Rift. In this paper, various technologies and methodologies of implementing a gesture detection and recognition system are discussed. The paper also includes the implementation of a face recognition module using the Viola-Jones Algorithm for authentication of the system followed by hand gesture recognition using CNN to perform basic operations on the laptop. Any type of user can use gesture control as an alternative and interesting way to control their laptop. Furthermore, this can be used as a prototype for future implementations in the field of virtual reality as well as augmented reality.

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Computer Vision Based Human-Computer Interaction Using Color Detection Techniques

Cited by 15 publications

References 8 publications

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

Face Authenticated Hand Gesture Based Human Computer Interaction for Desktops

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