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Existing camera-projector calibration methods typically warp feature points from a camera image to a projector image using estimated homographies, and often suffer from errors in camera parameters and noise due to imperfect planarity of the calibration target. In this paper we propose a simple yet robust solution that explicitly deals with these challenges. Following the structured light (SL) camera-project calibration framework, a carefully designed correspondence algorithm is built on top of the De Bruijn patterns. Such correspondence is then used for initial camera-projector calibration. Then, to gain more robustness against noises, especially those from an imperfect planar calibration board, a bundle adjustment algorithm is developed to jointly optimize the estimated camera and projector models. Aside from the robustness, our solution requires only one shot of SL pattern for each calibration board pose, which is much more convenient than multi-shot solutions in practice. Data validations are conducted on both synthetic and real datasets, and our method shows clear advantages over existing methods in all experiments.

Airborne laser scanning has been a valuable tool for forestry applications since it began to be used commercially. Thanks to the high 3D resolution provided by the Light Detection and Ranging (LiDAR) point cloud, it has provided great convenience in complex 3D modeling processes needed for forestry applications such as forest inventory, forest management, determination of carbon stocks and the characterization of biodiversity. LiDAR data provides a new dimension in forestry applications with its high 3D resolution and multiple return characteristics. The extraction of woodland areas from the LiDAR point cloud has great importance for automating the determination of tree heights, species and stand frequency which will be used for generating canopy height models (CHM). In this study, woodland areas in the urban scene were automatically extracted by using the multiple return properties of the LiDAR point cloud. The proposed approach consists of three major steps namely preprocessing, parameter calculation and k-d tree search for trees which were implemented in MATLAB. In the first step, multiple return points have been identified from the LiDAR point cloud, which will be then used to determine possible tree locations. Then, by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, neighborhood relations among the multi return points which were extracted from the initial point cloud data, were formed and a rule-based filter was applied by taking advantage of neighborhood relations. In addition, the initial point cloud was filtered with the Cloth Simulation Filtering (CSF) algorithm to separate ground and non-ground points where non-ground points used to extract trees. In the second step, non-vegetation points were removed by applying a threshold based on the change of curvature and planarity parameters, which are derived from the filtered non-ground point cloud. In the last step, in order to extract trees, a k-d tree structure was created from the filtered non-ground points to find nearest neighbors of each multi return point within a given diameter in the k-d tree structure. In order to evaluate the accuracy of the approach, the extracted boundaries were compared with the manually digitized woodland boundaries from the true orthophoto of the study area using correctness, completeness and quality metrics.

Building extraction from remotely sensed images plays an important role in many applications such as updating geographical information system, change detection, urban planning, disaster management and 3D building modeling. Automatic extraction of buildings from aerial images is not an easy task because of background complexity, lighting conditions and vegetation cover that reduces separability or visibility of buildings. As a result, automatic building extraction can be a complex process for computer vision and image processing techniques.In order to overcome this difficulty region-based active contour model was used to automatically detect the boundary of buildings for this study. To extract object boundaries, the model grows or shrinks the initial contour in the image. The main objective of this paper is making active contours algorithm perform without user interaction and to detect automatically initial contours to segment buildings with a software coded in Matlab. This task carried out by morphological operations, band ratio and thresholding methods. In this study, high resolution aerial images with 8 cm ground sampling distance (GSD) were used. Three separate test zones were selected with varying building level of detail on these images. Finally, it was assessed the accuracy of segmented buildings using Correctness, Completeness and Quality metrics by comparing the results images and manually digitized reference image.The proposed approach for building extraction from images was shown to be 98% accurate on buildings with simple geometry and homogeneous roof textures. However accuracy of extracted buildings with heterogeneous roof textures and lighting, and complex geometry is 89%. The results clearly show that automatically calculated initial contour positions work in accordance with the active contour algorithm and easily extraction of the buildings boundaries.