Hand gesture recognition based on free-form contours and probabilistic inference

Kasprzak, Włodzimierz; Wilkowski, Artur; Czapnik, Karol

doi:10.2478/v10006-012-0033-6

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…Researchers have proposed skin detection using various color models and fusion of color models [20][21][22][23]. Thus, the color space technique is widely used and preferred technique for detecting skin region.…”

Section: Good Results Under Different Light Conditions 5 No False Skimentioning

confidence: 99%

Hand Gesture Interface based on Skin Detection Technique for Automotive Infotainment System

Buddhikot¹,

Commerce²,

Kulkarni³

et al. 2018

IJIGSP

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The infotainment systems are acquiring wide popularity in automotive domain. These systems are manually operated and require physical contact for interaction. In the present scenario the consumers are demanding a smart phone like experience from the vehicle's console unit. Thus, there is a wide scope for enhancing the mode of interaction and introducing a touch less interface system. The gesture interface approach is a new possibility in this domain. In this method the skin detection plays an important role in segmenting hand region. There are various approaches for hand detection based on skin region identification. The fundamental challenge in skin detection lies in various factors such as illumination, background, camera characteristics, and ethnicity. The gesture detection in automotive environment is further challenging task due to significant impact of wide variation in light, continuous changing background and hindrance caused by vehicle movement. In the present work, design of hand gesture interface for rear seat passenger is discussed. The interface is developed to interact with media player application of infotainment system based on efficient skin detection technique. The objectives of work include study of various skin color modeling, analysis of combination of color spaces, study of hand feature extraction and recognition techniques, design of lab setup for experimentation, implementing gesture interface to access media player application of an infotainment system. The developed prototype lab set up is used for analyzing the skin classifiers and designing a Hi-Vi skin classifier. Further, a user friendly interface is developed using Hi-Vi algorithm with multimode interface features. The evaluation of developed system shows high TPR and low FPR.

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Section: Good Results Under Different Light Conditions 5 No False Skimentioning

confidence: 99%

Hand Gesture Interface based on Skin Detection Technique for Automotive Infotainment System

Buddhikot¹,

Commerce²,

Kulkarni³

et al. 2018

IJIGSP

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show abstract

“…The background subtraction with reference image is proposed in [16]. Many researches also use the contours of the hand gestures to extract features [17], [18], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition

Zhang

et al. 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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This paper presents a novel approach for hand gesture recognition based on wavelet enhanced image preprocessing and supervised Artificial Neural Networks (ANNs).Six different hand gestures are tested. The image preprocessing handles the hand gesture contour segmentation. This research includes three contributions: (1) it provides two dimensional hand gesture contour images to one dimensional signal conversion using reference points; (2) it implements wavelet decomposition for the 1D signals converted from 2D hand gesture contour images; and (3) it extracts 4 statistical features of the wavelet coefficients. The experimental results are provided to validate our proposed framework. The ANN is built to classify different hand gestures. There are totally 1240 images used for training and 240 images are used for testing. By using the proposed framework, our approach can provide classification accuracy of 97% and is fast in feature extraction and computation.

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“…Na potrzeby interakcji z komputerem człowiek może wykonywać gesty różnymi częściami ciała -dając znaki ręką [22], ruchem głowy lub całym ciałem [35] lub mimiką twarzy [56].…”

Section: Rozpoznawanie Gestówunclassified

Agent Structure of Multimodal User Interface to the National Cybersecurity Platform – Part 2

Kasprzak

Szynkiewicz

Stefańczyk

et al. 2019

PAR

Self Cite

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Zezwala się na korzystanie z artykułu na warunkach licencji Creative Commons Uznanie autorstwa 3.0 1. Wprowadzenie 1.1. Wielomodalny interfejs człowiek-komputer Prace badawcze poświęcone wielomodalnym interfejsom człowiek-komputer są prowadzone od ponad 40 lat [37]. Celem tych badań jest opracowanie metod i technik interakcji ludzi z komputerem w pełni wykorzystujących sposoby naturalnej komunikacji i interakcji człowieka z otoczeniem. Interfejsy wielomodalne charakteryzują się dwiema podstawowymi cechami: łączą wiele typów danych oraz przetwarzają te dane w czasie rzeczywistym przy określonych ograniczeniach czasowych [10]. System "Put-That-There" [3] opracowany w MIT (USA) jest powszechnie uważany za pierwszy praktyczny pokaz możliwości, jakie daje wielomodalny interfejs. W systemie tym były łączone dwa rodzaje wejść: głosowe oraz gesty, które umożliwiały użytkownikowi siedzącemu na krześle naturalną

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Hand gesture recognition based on free-form contours and probabilistic inference

Cited by 17 publications

References 13 publications

Hand Gesture Interface based on Skin Detection Technique for Automotive Infotainment System

Hand Gesture Interface based on Skin Detection Technique for Automotive Infotainment System

Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition

Agent Structure of Multimodal User Interface to the National Cybersecurity Platform – Part 2

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