Segmentation Pyramid Classification

The method in this paper extracts information from multi-spectral image data by analyzing spectral and spatial image characteristics in an integrated classification and segmentation procedure. Problems concerning region fragmentation and region merging in image segmentation are dealt with by looking at class homogeneity, rather than spectral homogeneity. Terrain objects correspond to segments that occur in different levels of a segmentation pyramid. A set of segments is selected from the pyramid by classifying each segment at every level. The pyramid is traversed and pure segments, which are predominantly covered by a single class, are selected at the highest possible level, which may vary over the image. The selected segments form a non-overlapping set in which no region fragmentation or merging occurs, and for which the class labels are known. Due to non-separability of classes in certain image areas, the selected segments do not form a complete coverage. Therefore, for these areas, ..

Structuring Laser-Scanned Trees Using 3D Mathematical Morphology

The task addressed in this paper is 3D modelling and reconstruction of (real world) trees on the basis of terrestrial laser scans. To identify the structure of a tree in terms of stem and branches, an algorithm has been designed in 3D voxel space, based on a selection of basic and advanced 2D raster (image) processing algorithms, transferred into the 3D domain. The selection includes filtering, mathematical morphology, skeletonization, connected component labeling and shortest route computation

Automatic Registration of Terrestrial Scanning Data Based on Registered

In this paper, an algorithm is presented for automatic registration of terrestrial point clouds based on registered images captured from terrestrial laser scanner. Firstly, the Moravec interest operator is used to extract feature points in the left one of two adjacent images and probabilistic relaxation is employed to match corresponding points for those feature points. The strategy of matching on image pyramid is used to improve the reliability and speed of image matching. Registered images usually have low resolution, moreover, distinct geometric difference exits between adjacent images which are close-ranged. Consequently, the probability of erroneous matching becomes high. Therefore, geometric constraint (i.e. distance invariance) of 3D corresponding point pairs is used to eliminate erroneous corresponding point pairs. Iterative matching process is implemented to acquire high accuracy and stability. Thereafter, absolute orientation in photogrammetry is employed to compute six transformation parameters separated in rotation and translation. Experiments were implemented to testify the method, presented in this paper, on indoor and outdoor point clouds. Processes for those point clouds are fully automatic and acquire a good accuracy up to the order of millimeter