β Scribed by Stephen B. Balakirsky; Rama Chellappa
No coin nor oath required. For personal study only.
his paper compares three image stabilization algorithms when used as preprocessors for a target tracking application. These algorithms vary in computational complexity, accuracy, and ability.
TAl gorithm 1 is capable of only pixel-level realignment of imagery, while Algorithms 2 and 3 are capable of full subpixel stabilization with respect to translation, rotation, and scale. The algorithms are evaluated on their performance in the stabilization of one synthetic forward looking infrared (FLIR) data set and two real FLIR imagery data sets. The evaluation tools incorporated include mean absolute error of the output data set and the overall performance of an automatic target acquisition system (developed at the Army Research Laboratory) that uses the algorithms as a front end preprocessor. We found that for this tracking application, extremely accurate subpixel stabilization was a requirement for proper operation. We also found that in this application, Algorithm 3 performed significantly better than the other two algorithms.
π SIMILAR VOLUMES
It is possible to summarize the schemes, used in the past to stabilize algorithms, in the form of algorithm models. These models serve as patterns for subsequent applications. Summm'y--There are a number of algorithms in the literature which both theoretically and empirically are known to be only l
## Abstract The peeling of a __d__βdimensional set of points is usually performed with successive calls to a convex hull algorithm; the optimal worstβcase convex hull algorithm, known to have an __O__(__n__^Λ^ Log (n)) execution time, may give an __O__(__n__^Λ^__n__^Λ^ Log (n)) to peel all the set;
Videodata processing in electronic digital image stabilization systems has been investigated. Advantages of the new information technique over the traditional electromechanic stabilization is discussed.