In order to make the general user take vision tasks more flexibly and easily, this paper proposes a new solution for the problem of camera calibration from correspondences between model lines and their noisy image lines in multiple images. In the proposed method the common planar items in hand with the standard size and structure are utilized as the calibration objects. The proposed method consists of a closed-form solution based on homography optimization, followed by a nonlinear refinement based on the maximum likelihood approach. To automatically recover the camera parameters linearly, we present a robust homography optimization method based on the edge model by redesigning the classic 3D tracking approach. In the nonlinear refinement procedure, the uncertainty of the image line segment is encoded in the error model, taking the finite nature of the observations into account. By developing the new error model between the model line and image line segment, the problem of the camera calibration is expressed in the probabilistic formulation. Simulation data is used to compare this method with the widely used planar pattern based method. Actual image sequences are also utilized to demonstrate the effectiveness and flexibility of the proposed method.

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In hyperspectral images (HSI) classification, it is important to combine multiple features of a certain pixel in both spatial and spectral domains to improve the classification accuracy. To achieve this goal, this article proposes a novel spatial-spectral feature dimensionality reduction algorithm based on manifold learning. For each feature, a graph Laplacian matrix is constructed based on discriminative information from training samples, and then the graph Laplacian matrices of the various features are linearly combined using a set of empirically defined weights. Finally, the feature mapping is obtained by an eigen-decomposition problem. Based on the classification results of the public Indiana Airborne Visible Infrared Imaging Spectrometer dataset and Texas Hyperspectral Digital Imagery Collection Experiment data set, the technical accuracies show that our method achieves superior performance compared to some representative HSI feature extraction and dimensionality reduction algorithms.