Vision-Based Hand Gesture Recognition for Human-Computer Interaction

Zabulis, Xenophon; Baltzakis, Haris; Argyros, Antonis A.

doi:10.1201/9781420064995-c34

Cited by 108 publications

(56 citation statements)

References 194 publications

(205 reference statements)

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“…Fingertip positions can be detected using template matching, which examines the correlation between sections of a video image and a fingertip template, a detailed or simplified picture of a fingertip or finger (Zabulis et al, 2009). Alternatively, characteristic features of the finger may be used for detection.…”

Section: Introductionmentioning

confidence: 99%

Determining the bias and variance of a deterministic finger-tracking algorithm

Morash

Velden

2015

Behav Res

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Finger tracking has the potential to expand haptic research and applications, as eye tracking has done in vision research. In research applications, it is desirable to know the bias and variance associated with a finger-tracking method. However, assessing the bias and variance of a deterministic method is not straightforward. Multiple measurements of the same finger position data will not produce different results, implying zero variance. Here, we present a method of assessing deterministic finger-tracking variance and bias through comparison to a non-deterministic measure. A proof-of-concept is presented using a videobased finger-tracking algorithm developed for the specific purpose of tracking participant fingers during a psychological research study. The algorithm uses ridge detection on videos of the participant's hand, and estimates the location of the right index fingertip. The algorithm was evaluated using data from four participants, who explored tactile maps using only their right index finger and all right-hand fingers. The algorithm identified the index fingertip in 99.78 % of one-finger video frames and 97.55 % of five-finger video frames. Although the algorithm produced slightly biased Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands and more dispersed estimates relative to a human coder, these differences (x = 0.08 cm, y = 0.04 cm) and standard deviations (σ x = 0.16 cm, σ y = 0.21 cm) were small compared to the size of a fingertip (1.5-2.0 cm). Some example finger-tracking results are provided where corrections are made using the bias and variance estimates.

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Section: Introductionmentioning

confidence: 99%

Determining the bias and variance of a deterministic finger-tracking algorithm

Morash

Velden

2015

Behav Res

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“…To highlight the challenges of vision-based hand gesture segmentation, several approaches in the literature were adapted in terms of visual features such as colour, motion information, shape or a combination of these features (Zabulis et al 2009;Stergiopoulou et al 2014). Most hand gesture segmentation approaches tend to either segment hand region doing gesture or estimate the shape of hand (Zabulis et al 2009).…”

Section: Previous Work and Motivationmentioning

confidence: 99%

“…Most hand gesture segmentation approaches tend to either segment hand region doing gesture or estimate the shape of hand (Zabulis et al 2009). Bhuyan et al (2014) used the fusing of Cb and Cr, H and S chrominance components in YCbCr and HSV colour spaces, as well as the largest connected component method to get palm region of hand.…”

Section: Previous Work and Motivationmentioning

confidence: 99%

Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Thabet

Khalid

Sulaiman

et al. 2017

J Ambient Intell Human Comput

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“…Algorithms need to be robust, and preferably lowdimensional in nature, in order to do real-time processing in the fraction of a second. A great number of recognition techniques have been proposed [32,3,33], which mostly focus on the image features being extracted and/or the development of the statistical learning techniques involved in the recognition process. The aim is to illustrate how the performance of recognition algorithms can be enhanced, using minimal processing overhead, through improved pre-processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Gesture Recognition Performance through Improved Pre-Processing

Grosek¹,

Shi²,

Kutz³

2013

IJCA

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Gesture recognition is analyzed on a set of static hand gestures in the context of designing robust, real-time pre-processing techniques for applications in hand-held electronics. A comparative case study that uses various combinations of algorithms across the steps of the recognition process is made, revealing the fact that many method combinations can produce highly accurate results, even at low resolutions, given the right kind of pre-processing. The pre-processing includes the hand segmentation and normalization done before feature extraction. Indeed, pre-processing has by far the greatest effect on the overall accuracy, robustness, and speed of the gesture recognition process, significantly outweighing the influence of feature-extraction and classification. Even at image resolutions as low as 8×8 pixels, accuracies of 99% are achieved using a simple PCA feature selection scheme and a LDA classification method. These results suggest the priority and advantages of focusing on developing robust and efficient pre-processing methods.

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Vision-Based Hand Gesture Recognition for Human-Computer Interaction

Cited by 108 publications

References 194 publications

Determining the bias and variance of a deterministic finger-tracking algorithm

Determining the bias and variance of a deterministic finger-tracking algorithm

Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Enhanced Gesture Recognition Performance through Improved Pre-Processing

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