Division Algorithms and Implementations
IEEE Transactions on Computers
FPGA Implementation of Median Filter
VLSID '97 Proceedings of the Tenth International Conference on VLSI Design: VLSI in Multimedia Applications
Optimized Component Labeling Algorithm for Using in Medium Sized FPGAs
PDCAT '08 Proceedings of the 2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies
Fast connected-component labeling
Pattern Recognition
A parallel hardware architecture for connected component labeling based on fast label merging
ASAP '08 Proceedings of the 2008 International Conference on Application-Specific Systems, Architectures and Processors
Research on Image Median Filtering Algorithm and Its FPGA Implementation
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03
A VLSI architecture and algorithm for Lucas-Kanade-based optical flow computation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays
Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays
Efficient hardware implementation of 8 × 8 integer cosine transforms for multiple video codecs
Journal of Real-Time Image Processing
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Nowadays, hardware implementation of image and video processing algorithms on application specific integrated circuit (ASIC) has become a viable target in many applications. Star tracking algorithm is commonly used in space missions to recover the attitude of the satellite or spaceship. The algorithm matches stars of the satellite camera with the stars in a catalog to calculate the camera orientation (attitude). The number of stars in the catalog has the major impact on the accuracy of the star tracking algorithm. However, the higher number of stars in the catalog increases the computation burden and decreases the update rate of the algorithm. Hardware implementation of the star tracking algorithm using parallel and pipelined architecture is a proper solution to ensure higher accuracy as well as higher update rate. Noise filtering and also the detection of stars and their centroids in the camera image are the main stages in most of the star tracking algorithms. In this paper, we propose a new hardware architecture for star detection and centroid calculation in star tracking applications. The method contains several stages, including noise smoothing with fast Gaussian and median filters, connected component labeling, and centroid calculation. We introduce a new and fast algorithm for star labeling and centroid calculation that needs only one scan of the input image.