Ordering computers by hand gestures recognition based on wavelet networks

Bouchrika, Tahani; Zaied, Mourad; Jemai, Olfa; Amar, Chokri Ben

doi:10.1109/ccca.2012.6417911

Cited by 26 publications

(6 citation statements)

References 24 publications

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“…Since Beta wavelet [1] is a powerful tool in various domains such as image compression [2], face recognition [3,4], 3D face recognition [5], image classification [6,7], phoneme recognition [8], speech recognition [9] and in particular Arabic word recognition [10] and hand tracking and recognition [11]; this study used the Fast Beta Wavelet Network (FBWN) modeling to propose a new approach for CBIR.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Section: Introductionmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

Self Cite

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“…Gesture detection, an integral component of Human-Computer Interaction (HCI), has witnessed exponential growth, spurred by advancements in machine learning and computer vision technologies. This paradigm enables intuitive, nonverbal communication between humans and digital systems, transcending the traditional confines of keyboard and mouse interfaces [21].…”

Section: Cmentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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“…To make the hand gesture recognition more accurate and thus ensuring a more natural user experience interacting with the machine interface, Bouchrika et al [10], [11] applied a Wavelet Network Classifier (WNC) in a remote computer ordering application using hand gestures to place orders. Hands detection, tracking and gesture recognition techniques were applied.…”

Section: Literature Reviewmentioning

confidence: 99%