Prototype Program Hand Gesture Recognize Using the Convex Hull Method and Convexity Defect on Android

Anshary, Muhammad Adi Khairul; Hidayat, Eka Wahyu; Amalia, Tiara

doi:10.15575/join.v5i2.594

Cited by 2 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convex hulls and convexity defects are used to recognize the hand as an input to the system. Additionally, The functionality of the technology was verified by testing it in three scenarios involving variable lighting, background color, and indoor or outdoor conditions [26]. The convex hull and convexity defect algorithms have been successfully put into Android phones, the research shows.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

Zhang

Zhou

Tao

et al. 2023

IFS

View full text Add to dashboard Cite

The modern world contains a significant number of applications based on computer vision, in which human-computer interaction plays a crucial role, pose estimation of the hand is a crucial approach in the field of human-computer interaction. However, previous approaches suffer from the inability to accurately measure position in real-world scenes, difficulty in obtaining targets of different sizes, the structure of complex network, and the lack of applications. In recent years, deep learning techniques have produced state-of-the-art outcomes but there are still challenges that need to be overcome to fully exploit this technology. In this research, a fish skeleton CNN (FS-HandNet) is proposed for hand posture estimation from a monocular RGB image. To obtain hand pose information, a fish skeleton network structure is used for the first time. Particularly, bidirectional pyramid structures (BiPS) can effectively reduce the loss of feature information during downsampling and can be used to extract features from targets of different sizes. It is more effective at solving problems of different sizes. Then a distribution-aware coordinate representation is employed to adjust the position information of the hand, and finally, a convex hull algorithm and hand pose information are applied to recognize multiple gestures. Extensive studies on three publicly available hand position benchmarks demonstrate that our method performs nearly as well as the state-of-the-art in hand pose estimation. Additionally, we have implemented hand pose estimation for the application of gesture recognition.

show abstract

Section: Related Workmentioning

confidence: 99%

“…[24,25,28] implements image classification with SVM. [26,28] used convexity defects and convexity hulls to extract features, while [26] implemented a variety of scenarios, and [28] segmented data before feature extraction. [27,29] only implements the recognition of specific gestures.…”

Section: Related Workmentioning

confidence: 99%

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

Zhang

Zhou

Tao

et al. 2023

IFS

View full text Add to dashboard Cite

show abstract

“…To recognize hand motions, the system use the convex hull method and convexity defects. The background color and light intensity in this system are adjustable in order to determine the background color and light intensity that work best for identifying hands [3].…”

Section: Introductionmentioning

confidence: 99%

Quality of Service Analysis in Hand Gesture Identification Application Based on Convex Hull Algorithm to Control a Learning Media

Setiawan

Hidayati

Saptono

2023

Jartel

View full text Add to dashboard Cite

The technique of human interaction with computers has developed very rapidly. Body movement is the easiest and most expressive way and hand movements are flexible. We can use gestures as a simple identification command. This research discusses the application of hand gesture reading as a remote control for learning media and analyzes its quality of service using Wireshark software. This system uses a Raspberry Pi as a computing center. Raspberry Pi reads hand gestures using the Convex Hull algorithm, which can read the number of fingers raised on the hand by taking the outermost point in the contour scan of the hand. Each gesture in the form of the number of fingers was allocated to the keyboard commands used to select answers on the learning media. On the learning media side, a quiz system was created that uses keyboard commands to select the answers. Then the Raspberry Pi is connected to the laptop using a third-party application called VNC. Based on QoS measurement results, the throughput result is 62 kbps, which is included in the very good category. Packet Loss of 0%, which is also included in the very good category. The delay of 52.2 ms, which is included in the very good category. Thus, the overall quality of the network can be categorized as very good.

show abstract

Prototype Program Hand Gesture Recognize Using the Convex Hull Method and Convexity Defect on Android

Cited by 2 publications

References 9 publications

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

Quality of Service Analysis in Hand Gesture Identification Application Based on Convex Hull Algorithm to Control a Learning Media

Contact Info

Product

Resources

About