Fast and Stable Bio-Signals Reconstruction Using Krawtchouk Moments

One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.

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A Static Hand Gesture Recognition Algorithm Based on Krawtchouk Moments

Communications in Computer and Information Science - Pattern Recognition ◽

10.1007/978-3-662-45643-9_34 ◽

2014 ◽

pp. 321-330 ◽

Cited By ~ 2

Author(s):

Shuping Liu ◽

Yu Liu ◽

Jun Yu ◽

Zengfu Wang

Keyword(s):

Gesture Recognition ◽

Recognition Algorithm ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Krawtchouk Moments

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A New Object Recognition Approach Using Combination of Texture, Color and Shape Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.761.111 ◽

2015 ◽

Vol 761 ◽

pp. 111-115

Author(s):

Abdul Kadir ◽

K.A.A. Aziz ◽

Irianto

Keyword(s):

Texture Features ◽

Shape Features ◽

Accuracy Rate ◽

New Approach ◽

Orthogonal Moments ◽

Krawtchouk Moments ◽

Image Histogram ◽

Color Features ◽

Tchebichef Moments ◽

Skewness And Kurtosis

This paper reports a new approach for recognizing objects by using combination of texture, color and shape features. Texture features were generated by applying statistical calculation on the image histogram. Color features were computed by using mean, standard deviation, skewness and kurtosis. Shape features were generated using combination of Shen features and basic shapes such as eccentricity and dispersion. The total features were used much less compared to approaches that involve orthogonal moments such as Krawtchouk moments, Zernike moments, or Tchebichef moments. Testing was done by using a dataset that contains 53 kinds of objects. All objects contained in the dataset were various things that can be found in supermarkets or produced by manufacturing. The result shows that the system gave 98.11% of accuracy rate.

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