A threshold auto-adjustment method of feature points extraction based on grid

摘要

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.