In this article, we propose a new planar shape representation, the medial branch graph representation (MBGR) which allows to easily generate, vary and edit all-new sorts of parametric shapes. Each MBGR shape is described by a collection of connected control circles organized as a graph. Each pair of connected circles generates a branch of the shape. The boundaries of the branches are designed using cubic Bézier curves which enable shape representation in a compact SVG format. Each circle is associated with a regularity parameter locally controlling the smoothness of the branch connections. In this way, we can manage shapes having both sharp corners and smooth boundaries. To illustrate the potential of the MBGR representation, we have created an online facility ( https://mbgrg.github.io/mbgrg/ ) where the user can create/edit MBGR shapes automatically or interactively.

An accurate estimation of the position, orientation, principal curvatures, and change in intensity of the edges in a 3D image provides highly useful information for many applications. The use of derivative operators to compute the gradient vector and the Hessian matrix in each voxel usually generates inaccurate results. This paper presents a new edge detector which is derived from the Partial Volume Effect (PVE). Instead of assuming continuity in the image values, edge features are extracted from the distribution of intensities within a neighborhood of each edge voxel. First, the influence of the intensities of the voxels in first- and second-order edges is analyzed to demonstrate that these types of edges can be precisely characterized from the intensity distribution. Afterward, this approach is extended to especially demanding situations by considering how adverse conditions can be tackled. This extension includes filtering noisy images, characterizing edges in blurred regions, and using windows with floating limits for close edges. The proposed technique has been tested on synthetic and real images, including some particularly difficult objects, and achieving a highly accurate subvoxel characterization of the edges. An open source implementation of our method is provided.

Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, its cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with over 2 million points represents a street scene and includes a couple of objects. There are five meaningful classes: building, car, ground, pole and vegetation. We aim at localizing and segmenting semantic objects from these large-scale 3D point clouds. Four groups contributed their results with different methods. The results show that learning-based methods are the trend and one of them achieves the best performance on both Overall Accuracy and mean Intersection over Union. Next to the learning-based methods, the combination of hand-crafted detectors are also reliable and rank second among comparison algorithms.

In this letter, we propose a supervised method for the classification of fully polarimetric synthetic aperture radar (PolSAR) images based on active contour models. We use an “a priori” estimation, obtained from training data, of the complex Wishart distributions of the different types of regions in the image (for instance, water, crops, grass, forest or urban). The information of the Wishart distributions is included in the active contour models to guide the level set evolution. We study the case of two classes and the case of three or more classes separately. We present some experimental results on the synthetic data and real PolSAR images to show the performance of the proposed model. The results are compared with other well-known supervised classification methods, and, for actual PolSAR data, our method shows an overall precision of 94.31% and a $\kappa $ coefficient of 0.937.

A freeware graphic window environment oriented to 2D/3D image processing is presented. The freeware, so-called AMILab, can be described from five different points of view: image processing environment, 3D visualization tool, image processing library, programming library, and pedagogic tool.