Complex Moment Invariants Research Articles

Quaternion moment and its invariants for color object classification

Moment invariants are important shape descriptors in computer vision. In this study, we propose new sets of quaternion moment descriptors for color image. They are constructed in the quaternion framework and are an extension of complex moment invariants for grayscale images. This is a useful tool in color image processing and color object recognition tasks that require the similarity invariance. The advantage of the proposed quaternion moment invariants is that they can not only process color image in a holistic manner but also grayscale one. In addition, the computational complexity of the proposed method is much lower than the quaternion Zernike moments defined in the polar coordinates. An example of using the quaternion moment invariants as pattern features for a color object classification application is given. Theoretical and experimental results show that the proposed descriptors perform better than the other competing moment-based methods.

Recognition of the blurred image by complex moment invariants

An important class of radiometric degradations we are faced with often in practice is image blurring. In this paper, special attention is paid to the recognition of the blurred image by complex moment invariant. Some important rules of complex moments for the blurred image are presented and proofed. Based on these rules, a useful subset of complex moment invariants is introduced, that are not affected by the blur, rotation, scale, and translation of the images. Another conclusion is that these complex moment invariants, which are not effected by image blurring, can all be expressed by a set of sub-polynomials, which are invariant to blurring. The graphs of these invariants under the effect of both blurring and image resolution are presented. The experiments have shown that these invariants can be successfully used in recognition of the blurred images.

A New Class of Shift-Invariant Operators

This letter proposes a class of operators with a shift invariance property. These operators are derived from two-dimensional (2-D) complex moment invariants based on the observation that there is a duality between rotation invariance and shift invariance. A general form of the shift invariants belonging to this class is presented, which shows that polyspectral invariants such as the power spectrum and the bispectrum are members of the class. Methods for computing shift invariants for one-dimensional (1-D) and 2-D signals are also presented. The examples given in the paper suggest that the higher order operators can preserve the original signal waveform better than autocorrelation.

Tactile array sensor for object identification using complex moments

AbstractThis article explores object identification by complex moment features obtained using a tactile array sensor. Some complex moment invariants have been derived and implemented eliminating the effects of lateral displacement and rotation from the tactile images. The generation of a decision tree and use of the complex moment features, allow easy identification of object shapes from the tactile array sensor.