In order to select the optimal threshold for pedestrian segmentation in infrared images, a novel algorithm based on local autocorrelation is proposed. The algorithm calculates the local autocorrelation feature of a given image. Next, it constructs a new feature matrix based on this spatial correlation and the original grayscale. Then, it obtains an automatic threshold related with local combined features using the geometrical method based on histogram analysis. Finally, it extracts the image region of pedestrian and yields the binary result. It is indicated by the experiments that, the proposed method performs good result of pedestrian region extraction and thresholding, and it is reasonable and effective.
Unreasonable decision-making results and unavailable stability analysis are the main drawbacks of current connection
numbers ranking methods. The novel ranking methods based on the relative certain probability power, relative optimistic
probability power and relative pessimistic probability power were proposed in this paper to overcome these
disadvantages. The permissible range of uncertain evolution factor which maintains the stability of the sorting among
connection numbers was calculated as well. The numerical computation results indicated the effectiveness of the
proposed ranking methods.