2018 | OriginalPaper | Buchkapitel
Shallow Convolutional Neural Network and Viterbi Algorithm for Dim Line Tracking
Erschienen in:
Computer Vision and Graphics
Abstract
The estimation of line is important in numerous practical applications. The most difficult case if the line is dim, even hidden in background noise. The application of Track–Before–Detect algorithms allows the tracking of such line. Additional preprocessing using shallow neural network trained for the detection of line features is proposed in this paper. Four variant of data fusion from neural network are compared. Direct output of neural network that works as a classifier gives best results for Mean Absolute Error (MAE) metric. Similar results are obtained if output of neural network is used as a mask for input image. Monte Carlo test are used for unbiased results. Test shows improvement of MAE about two times. The application of binary output from neural network is wrong solution and the error is largest. The influence of the number of convolutional layer neurons is not significant in this test.
