Weighted ICP Algorithm for Alignment of Stars from Scanned Astronomical Photographic Plates

ACM Computing Classification System (1998): I.2.8, I.2.10, I.5.1, J.2.Given the coarse celestial coordinates of the centre of a plate scan and the field of view, we are looking for a mapping between the stars extracted from the image and the stars from a catalogue, where the stars from both sources are represented by their stellar magnitudes and coordinates, relatively to the image centre. In a previous work we demonstrated the application of Iterative Closest Point (ICP) algorithm for the alignment problem where stars were represented only by their geometrical coordinates. ICP leads to translation and rotation of the initial points - a correction required for one set of stars to fit over the other. This paper extends the previous work by demonstrating significant improvement of ICP by using the stellar magnitudes as point weights. The improvement consists of great decrease of the iteration count until convergence, which helps in the case of highly “misaligned” initial states. The essential aspects of the ICP method like noise tolerance of false or missing stars are still in charge.This work is partially supported by the following projects: (1) Creative Development Support of Doctoral Students, Post-Doctoral and Young Researches in the Field of Computer Science, BG 051-PO-001-3.3.04/13, European Social Fund 2007–2013, Operational programme “Human resources development”, and (2) Astroinformatics, grant DO-02-275/2008 of the National Science Fund of the Bulgarian Ministry of Education, Youth and Science

International Conference on Computer Systems and Technologies- CompSysTech’07 CBIR Approach to the Recognition of a Sign Language Alphabet (*)

Abstract: The task of recognizing letters from the sign language alphabet, by means of which the hearing impaired people finger-spell words and proper nouns, is interpreted as a CBIR (Content Based Image Retrieval) problem. An arbitrary input image of given sign (palm gesture) is treated as a sample for search within a database (DB), which contains a large enough set of images (i.e. projections from a sufficient number of view points) for each letter of the sign language alphabet. We assume that the gestures for recognition are static images, which have been appropriately extracted from the input video sequence. In addition, we have at our disposal a CBIR method for image DB access that is simultaneously fast enough and noise-tolerant. The paper describes both the methodology used for building up the DB of image samples and the experimental study for the noise-tolerance of the available CBIR method. The latter is used to acknowledge the applicability of the proposed approach. Key words: CBIR, sign language recognition, finger-spelling, image databases, image/video analysis. 1

Asen L. Dontchev (on the occasion of his 65th birthday) and Vladimir M. Veliov (on the occasion of his 60th birthday)

[Donchev Tzanko; Дончев Цанко]; [Krastanov Mikhail; Кръстанов Михаил]; [Ribarska Nadezhda; Рибарска Надежда]; [Tsachev Tsvetomir; Цачев Цветомир]; [Zlateva Nadia; Златева Надя