Object Identification by Signal Gain and Correlation

In this study, “ring rotation invariant transform” techniques are used to add more salient feature to the original images. The “ring rotation invariant transform” can solve image rotation problem, which transfers a ring signal to several signal vectors in the complex domain, whereby to generate invariant magnitude. Matrix correlation is employed to combine these magnitudes to generate the various discriminators, by which to identify objects. For managing image-shifting problem, one pixel in sample image is compared with surrounding pixels of unknown image. The comparison approaching in this study is by the basis of pixel-to-pixel-comparisons.

GRAPHIC: The Geneva Reduction and Analysis Pipeline for High-contrast Imaging of planetary Companions

We present a new analysis and reduction pipeline for the detection of planetary companions using Angular Differential Imaging. The pipeline uses Fourier transforms for image shifting and rotation in order to achieve very low signal loss. Furthermore it is parallelised in order to run on computer clusters of up to 1024 cores. The pipeline was developed in Geneva for the ongoing direct imaging campaign for stars with radial velocity drifts in the HARPS and CORALIE radial-velocity planet-search surveys. In addition to that, a disk mode has been implemented in the context of observations of the protoplanetary disk around HD142527.

FACE RECOGNITION BY RING ROTATION INVARIANT TRANSFORM AND MANAGING IMAGE ROTATION AND SHIFTING PROBLEMS

Generally, for face recognition, image shift and rotation problems must be addressed. The "ring rotation invariant transform" technique is used to transfer geometrical features of face image to other more salient ones; by which one can identify whether a sample or unknown image is the identical image. It also can solve image rotation problem. To deal with the image-shifting problem, this study uses one pixel inside a sample image to compare with the corresponding pixels in the unknown image to locate the closest matching point. In this study, three different kinds of extracted ring signals are generated, which are (1) ring-radius-31, (2) ring-radius-22, and (3) ring-radius-13. These signals are used to generate the rotation invariant magnitudes and several magnitudes are combined as one entity and, subsequently, saved inside one specific corresponding pixel in the BMP file. By this approach, one pixel will possess more geometrical-features of the face images; one entity in sample image is compared with entities inside the corresponding radius-6-cake area of the unknown image to locate the closest matching point.