Two-Stage Segmentation Framework Based on Distance Transformation

With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in medical images, making it challenging to achieve high-accuracy segmentation of lesions or organs. In this paper, we assume that the distance map obtained by performing distance transformation on the ROI edge can be used as a weight map to make the network pay more attention to the learning of the ROI edge region. To this end, we design a novel framework to flexibly embed the distance map into the two-stage network to improve left atrium MRI segmentation performance. Furthermore, a series of distance map generation methods are proposed and studied to reasonably explore how to express the weight of assisting network learning. We conduct thorough experiments to verify the effectiveness of the proposed segmentation framework, and experimental results demonstrate that our hypothesis is feasible.

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

EUCLIDIAN DISTANCE TRANSFORMATION, MAIN AXIS ROTATION AND NOISY DILITATION SUPPORTED CROSS-SECTION CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS

Polygon meshes and particularly triangulated meshes can be used to describe the shape of different types of geometry such as bicycles, bridges, or runways. In engineering, such polygon meshes can be supplied as finite element meshes, resulting from topology optimization or from laser scanning. Especially from topology optimization, frame-like polygon meshes with slender parts are typical and often have to be converted into a CAD (Computer-Aided Design) format, e.g., for further geometrical detailing or performing additional shape optimization. Especially for such frame-like geometries, CAD designs are constructed as beams with cross-sections and beam-lines, whereby the cross-section is extruded along the beam-lines or beam skeleton. One major task in the recognition of beams is the classification of the cross-section type such as I, U, or T, which is addressed in this article. Therefore, a dataset consisting of different cross-sections represented as binary images is created. Noisy dilatation, the distance transformation, and main axis rotation are applied to these images to increase the robustness and reduce the necessary amount of samples. The resulting images are applied to a convolutional neuronal network.

SUBDIVISION SURFACE MID-SURFACE RECONSTRUCTION OF TOPOLOGY OPTIMIZATION RESULTS AND THIN-WALLED SHAPES USING SURFACE SKELETONS

Polygon meshes and particularly triangulated meshes can be used to describe the shape of different types of geometry such as bicycles, bridges, or runways. In engineering, such polygon meshes can occur as finite element meshes, resulting from topology optimization or laser scanning. This article presents an automated parameterization of polygon meshes into a parametric representation using subdivision surfaces, especially in topology optimization. Therefore, we perform surface skeletonization on a volumetric grid supported by the Euclidian distance transformation and topology preserving and shape-preserving criterion. Based on that surface skeleton, an automated conversation into a Subdivision Surface Control grid is established. The final mid-surface-like parametrization is quite flexible and can be changed by variating the control gird or the local thickness.

A Novel Four-Stage Method for Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal Decorrelation Adaptive Estimation and Distance Transformation

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

Eros and Distance: Transformation of Desire in St Gregory of Nyssa*

The paper aims to analyze the relation between the notion of love or desire (eros) for God, and the notion of distance (diastema) between God and the created beings in the works of St Gregory of Nyssa. These two notions are interrelated on different levels, because distance that separates God from the created beings is traversed out of desire for God of the latter. First, the distance as temporal interval will be investigated, which separates the present day from the Second Coming of Christ, which is elaborated by Gregory in his early work On Virginity. The focus will then be shifted to the distance between good and evil, that Gregory explicates in the works of his middle period such as On the making of man, Against Eunomius III and The Great Catechetical Oration. Finally, the distance as an inherent characteristic of created nature that never disappears will be analyzed by focusing on Gregory’s later works, such as Homilies on the Song of Songs, On perfection and The Life of Moses.