The experiment shows that the matching method based on GA is much faster than those based on Sequential Similarity Detection Algorithm(SSDA) and classical Mean Absolute Difference(MAD).

Experimental results show that the approach proposedgives higher matching probability and better positioning accuracy than those given by tradi-tional ones such as the MAD(mean absolute difference)algorithm and the feature matchingmethod.

When allocating the target bits for the encoding frame, the Zero Coefficient percentage is a good factor and is more effective than Mean Absolute Difference.

The mean absolute differences between the simulated and the observed were computed. The mean absolute difference of the amplitude is 3.46 cm,and the mean absolute difference of the phase-lag is 3.89°.

In order to more analyze the relativity of MB types, the mean absolute difference (MAD) and the mean of absolute frame difference (MAFD) are introduced to measure the relativity of MB types in P and B frame in temporal and spatial. And rules of MB types SKIP/DIRECT mode selection inter frames were set down according to the experiments.

Compared with the multiscale fractal signature, it does not have singular values and can be computed easily. For this reason, a natural scenery image matching method is proposed. Experimental results show that the δ net fractal fingerprint signature is a robust fractal feature of the images of natural scenery and the matching method proposed is more efficient than those in common use based on the grey level and edges, such as the mean absolute difference (MAD) algorithm and the multiscale fractal signature matching method.

The mean absolute differences between the simulated and the observed were computed. The mean absolute difference of the amplitude is 4.0cm, and the mean absolute difference of the phase-lag is 2.5°.

Comparisons of image reconstruction quality are based on the mean absolute difference in pixel intensities between the true and reconstructed images.

Typical LP computable risk measures, like the mean absolute deviation (MAD) or the Gini's mean absolute difference (GMD) are symmetric with respect to the below-mean and over-mean performances.

The variability between sessions had a mean absolute difference of 3 to 10 mm for the spatial parameters.

Three years of bi-weekly hydrologic data from each of the 43 sites were used to estimate mean surface water level and mean absolute difference (MAD) in surface water level for every plot.

Four statistics proved to be satisfactory; these were (1) the standard deviation, (2) the mean absolute difference, (3) the mean pair difference and (4) the interquartile range.

In this paper the asymptotic distribution of the mismu(?) absolute difference terrain contour matching algorithm is proved to be normal and the numerical algorithm for minimizing the computational cost is propounded. The multistage optimization method of computational cost is considered. The preprocessing approach of reference digital map normalization using subtracting local mean value and its applications worth is also discussed. The validity of theoretic analysis and considered approach is confirmed by a...

In this paper the asymptotic distribution of the mismu(?) absolute difference terrain contour matching algorithm is proved to be normal and the numerical algorithm for minimizing the computational cost is propounded. The multistage optimization method of computational cost is considered. The preprocessing approach of reference digital map normalization using subtracting local mean value and its applications worth is also discussed. The validity of theoretic analysis and considered approach is confirmed by a large amount of computer simulation results, and the actual match time is reduced to one sixth of the conventional mean absolute difference matching algorithm.

The problem of map matching becomes optimizing problem of multi-valley function after mismatching measure has been defined. Fast optimizing of multi-valley is a new method of solving the optimizing of mismatching measure proposed by authors. In this paper, appllying the method, an exhaust searching of map matching can be substituted by directional local searching, thus a lot of computation is saved and a high registration accuracy of matching is sure. Also the relationship between threshold and alarm probability...

The problem of map matching becomes optimizing problem of multi-valley function after mismatching measure has been defined. Fast optimizing of multi-valley is a new method of solving the optimizing of mismatching measure proposed by authors. In this paper, appllying the method, an exhaust searching of map matching can be substituted by directional local searching, thus a lot of computation is saved and a high registration accuracy of matching is sure. Also the relationship between threshold and alarm probability is derived and a region of threshold factor which make alarm probability be less than a defined value is given for Mean Absolute Difference algorithm. Finally, after a lot of matching experiments on three different aerospace photographs using fast optimizing of multivalley and comparing with Sequential Similarity Detection Algorithm, the correctness of (his method is proved and it is shown that this method has the advantages of high registration accuracy, small computational cost, easy to select threshold et'c.

A new method of two-stage image matching with multiple assumption tests based on two-fork deciding tree is proposed in this paper. First, the definition of twofork deciding tree representing multiple assumption tests and its computational cost formula are given. Then, a new variable threshold is derived. Finally, the computational cost of two-stage image matching with multiple assumption tests using variable threshold is obtained. A large amount of computer simulations shows that the limit of computational cost...

A new method of two-stage image matching with multiple assumption tests based on two-fork deciding tree is proposed in this paper. First, the definition of twofork deciding tree representing multiple assumption tests and its computational cost formula are given. Then, a new variable threshold is derived. Finally, the computational cost of two-stage image matching with multiple assumption tests using variable threshold is obtained. A large amount of computer simulations shows that the limit of computational cost of original twostage template matching can be broken by the proposed method and the computational cost of image matching is further reduced. Furthermore, it is sure that the matching location accuracy is almost the same as the Mean Absolute Difference Algorithm.