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Triangulation laser range scanning, which has been wildly used in various applications, can reconstruct the 3D geometric of the object with high precision by processing the image of laser stripe. The unbiased line extractor proposed by Steger is one of the most commonly used algorithms in laser stripe center extraction for its precision and robustness. Therefore, it is of great significance to assess the statistical performance of the Steger method when it is applied on laser stripe with Gaussian intensity profile. In this paper, a statistical behavior analysis for the laser stripe center extractor based on Steger method has been carried out. Relationships between center extraction precision, image quality and stripe characteristics have been examined analytically. Optimal scale of Gaussian smoothing kernel can be determined for each laser stripe image to achieve the highest precision according to the derived formula. Flexible three-step noise estimation procedure has been proposed to evaluate the center extraction precision of a typical triangulation laser scanning system by simply referring to the acquired images. The validity of our analysis has been verified by experiments on both artificial and natural images.

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We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many similarity metrics (e.g. covariance) needs very long period historic data (e.g. 3K days) which cannot represent current market effectively; (c) They cannot capture translation-invariance. To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each cluster according to their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.