2007
DOI: 10.1049/iet-cvi:20060198
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Consumer electronics control system based on hand gesture moment invariants

Abstract: Almost all consumer electronic equipment today uses remote controls for user interfaces. However, the variety of physical shapes and functional commands that each remote control features also raises numerous problems: the difficulties in locating the required remote control, the confusion with the button layout, the replacement issue and so on. The consumer electronics control system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for dom… Show more

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Cited by 75 publications
(57 citation statements)
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“…A commonly used method to detect fingers and fingertips is the mathematical morphology and related solutions [18][19][20]. Some approaches use Hu invariant moments [21][22], others k-curvature algorithm [23], combined with template matching [11], [19]. Noise, scale, and hand direction have a significant impact on finding fingers and fingertips using skeleton [24] or dominant points [12] principles.…”
Section: Related Workmentioning
confidence: 99%
“…A commonly used method to detect fingers and fingertips is the mathematical morphology and related solutions [18][19][20]. Some approaches use Hu invariant moments [21][22], others k-curvature algorithm [23], combined with template matching [11], [19]. Noise, scale, and hand direction have a significant impact on finding fingers and fingertips using skeleton [24] or dominant points [12] principles.…”
Section: Related Workmentioning
confidence: 99%
“…If a metric is produced using this structural information, it will truly capture the structural information and will be a valid measure to evaluate the structural integrity thereby making comparing images more meaningful. Moment Invariants have been used extensively in identifying shapes or outlay of objects for many years [5,6]. An image reduced to 16x16 or larger using Wavelet decomposition can be used to generate moment invariants to identify the structural makeup of an image.…”
Section: Structural Similarity Measure (Ssim)mentioning
confidence: 99%
“…An image is normalized (divided by its own standard deviation) such that the two images being compared have unit standard deviation. An image reduced to an approximation level (usually larger than 16x16) and then edge detected using 'Canny' operator and first moment invariant (φ 1) is calculated for the entire approximation [5]. Then the approximation level is divided into four quadrants and the first and second moments (φ i1, φ i2) are calculated for each …”
Section: Structural Similarity Measure (Ssim)mentioning
confidence: 99%
“…The azimuth system uses a microphone-pair mounted in front of the users. The localization is based on Faller and Merinaa [7] which has been modified and adjusted to the thinking head setup.…”
Section: A Auditory Localisationmentioning
confidence: 99%