Currently, new techniques have been implemented to provide data security, confidentiality, integrity and authentication. Orthogonal moments can be used for the watermark in small binary images. In this work we use this principle to encrypt a grayscale image in a video sequence. To validate our approach, we present a comparative analysis using different families of discrete orthogonal moments in terms of accuracy. Finally, results and conclusions are presented.
Measuring large curvature radii of convex surfaces with high precision is a challenge because the spherometer’s focus must be positioned at the apex of the surface and at the center of curvature of the surface by moving the surface or the spherometer. If the radius of curvature is larger than the back focus of spherometer, then measurement is not possible. In this work, we propose to use the FOCOIVA system1 to move the focus of the spherometer in longitudinal way without modifying the f number by moving two lenses inside it, with this mechanism it is possible to measure radii of curvature of several meters in length. The curves of movement of the lenses and the optical parameters of the lenses that compose the spherometer are presented.
A detailed analysis of the quaternion generic Jacobi-Fourier moments (QGJFMs) for color image description is presented. In order to reach numerical stability, a recursive approach is used during the computation of the generic Jacobi radial polynomials. Moreover, a search criterion is performed to establish the best values for the parameters α and β of the radial Jacobi polynomial families. Additionally, a polar pixel approach is taken into account to increase the numerical accuracy in the calculation of the QGJFMs. To prove the mathematical theory, some color images from optical microscopy and human retina are used. Experiments and results about color image reconstruction are presented.
The optical flow associated with a set of digital images of a moving individual is analyzed in order to extract
a gait signature. For this, invariant Hu moments are obtained for image description. A Hu Moment History
(HMH) is obtained from K frames to describe the gait signature of individuals in a video. The gait descriptors
are subsequences of the HMH of variable width. Each subsequence is generated by means of genetic algorithms
and used for classification in a neuronal network. The database for algorithm evaluation is MoBo, and the gait
classification results are above 90% for the cases of slow and fast walking and 100% for the cases of walking with
a ball and inclined walking. An optical processor is also implemented in order to obtain the descriptors of the
In this work, we reconstruct discrete image functions by means Bessel-Fourier polynomials. To measure the
image reconstruction we use the Normalized image reconstruction error between the input and reconstructed
images. We show that, a good reconstruction performance is found to be available for gray-level images. The
reconstruction algorithm is implemented using the first forty zeros of the Bessel functions of the first kind.
Experimental results are presented.