Abstract: In the context of fitting straight lines to noisy images, the subspace-based line detection algorithm (SLIDE) offers two benefits over the conventional Hough transform method: low computational complexity and high resolution of the estimates. These improvements are due to the fact that the line fitting problem is converted to an equivalent problem of fitting exponentials to a time series, which can then be solved efficiently by using subspace methods like ESPRIT. The SLIDE algorithm establishes this equivalence by transforming the two dimensional binary image to a single observation vector using a propagation scheme. The difficulty with having a single observation vector in this approach is that the total number of snapshots may not be adequately large. This limits the estimation accuracy and degrades the performance of the detection algorithm (which estimates the number of present lines in the image). In this paper we propose to utilize multiple observation vectors to circumvent the problem of inadequate number of snapshots. The challenge with the multiple observation vector approach is how to combine these vectors to form a covariance matrix that possesses the desired structure. We overcome this difficulty by considering only a specific set of observation vectors along with an interleaving technique. Simulation results show that this technique significantly improves the efficiency of the detection algorithm as well as the accuracy of the estimates of the line angles. !5
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