2D tree object representation via the slope chain code

Conventional interactive 3D tree modeling systems are generally based on 2D input devices, and it's not convenient to generate desired 3D tree shape from 2D inputs due to the complexity of 3D tree structures. In this paper, we present a system for modeling trees interactively using a 3D gesture-based VR platform. The system contains a head-mounted display (HMD) and a 6-DOF motion controller for interaction. We propose an improved procedural modeling method to generate trees faster for VR platform. Using the 6-DOF motion controller, users can manipulate tree structures by a set of 3D interactive operations, including geometric editing using 3D gestures, sketching, brushing and silhouette-guided growth. Our interactions are more flexible and convenient than using traditional 2D input devices, e.g., we allow the user to simultaneously rotate and translate parts of a tree using a 3D gesture.

With the growing popularity of 3D printing, different shape classes such as fibers and hair have been shown, driving research toward class‐specific solutions. Among them, 3D trees are an important class, consisting of unique structures, characteristics and botanical features. Nevertheless, trees are an especially challenging case for 3D manufacturing. They typically consist of non‐volumetric patch leaves, an extreme amount of small detail often below printable resolution and are often physically weak to be self‐sustainable. We introduce a novel 3D tree printability method which optimizes trees through a set of geometry modifications for manufacturing purposes. Our key idea is to formulate tree modifications as a minimal constrained set which accounts for the visual appearance of the model and its structural soundness. To handle non‐printable fine details, our method modifies the tree shape by gradually abstracting details of visible parts while reducing details of non‐visible parts. To guarantee structural soundness and to increase strength and stability, our algorithm incorporates a physical analysis and adjusts the tree topology and geometry accordingly while adhering to allometric rules. Our results show a variety of tree species with different complexity that are physically sound and correctly printed within reasonable time. The printed trees are correct in terms of their allometry and of high visual quality, which makes them suitable for various applications in the realm of outdoor design, modeling and manufacturing.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Simulation of the 3D temperature field of the organism as the arterial tree is taken as a heat source is an accurate way to calculate the bio-heat transfer. Based on the corrosion casts of real arterial tree of human tongue, a vessel model tree is grown by a stepwise method. Firstly, the profunda lingual artery and the branches of first level were constructed by statistical rule. And then else arteries were modeled based on physical laws and optimization criterion. The arterial tree of human tongue formed by the stepwise method is compared with the corrosion cast of real arterial tree. Result shows that this arterial model tree is in good agreement with the actual structure of arterial tree of human tongue. This method can substitute the vessel cast experiment, and can create arterial trees with different configuration which is the basis to research the process of heat transfer of organism deeply.

Biomimetic optimized concept with Murray networks for accelerated solar-driven water evaporation

Feature level MRI fusion based on 3D dual tree compactly supported Shearlet transform

IntroductionAugmented Reality (AR) overlays context-sensitive information onto the real world in a meaningful way. As AR bridges the gap between the real and virtual world, it holds potential to enhance...

A detailed picture of how an aerosol is transported and deposited in the self-affine bronchial tree structure of patients is fundamental to design and optimize orally inhaled drug products. This work describes a Monte Carlo-based statistical deposition model able to simulate aerosol transport and deposition in a 3D human bronchial tree. The model enables working with complex and realistic inhalation maneuvers including breath-holding and exhalation. It can run on fully stochastically generated bronchial trees as well as on those whose proximal airways are extracted from patient chest scans. However, at present, a mechanical breathing model is not explicitly included in our trees; their ventilation can be controlled by means of heuristic airflow splitting rules at bifurcations and by an alveolation index controlling the distal lung volume. Our formulation allows us to introduce different types of pathologies on the trees, both those altering their morphology (e.g., bronchiectasis and chronic obstructive pulmonary disease) and those impairing their function (e.g., interstitial lung diseases and emphysema). In this initial activity we describe deposition and ventilation models as well as the stochastic tree construction algorithm, and we validate them against total, regional, lobar, and sub-lobar deposition for healthy subjects.

Vibratory harvest is the most appropriate and efficient way to harvest Chinese hickory (Carya cathayensis Sarg.) nuts because of the hilly growth environment and scattered shooting architecture. To develop a mechanical harvester with tree dynamics parameter identification, a three dimensional (3D) machine vision system to map Chinese hickory trees for mechanical harvest is required. This paper proposes an approach for 3D reconstruction of Chinese hickory trees based on two images with complex background. In the proposed system, tree branches are roughly segmented from image by stripe programming, and refined by the active contour. Then, a graph representing 2D tree structure is established based on the extracted skeletons. To improve the estimation accuracy, branch-to-branch correspondence instead of point-to-point correspondence is employed to recover the 3D skeleton of tree branches. Only few graphical user interfaces are used to generate the seeds of the stripes, and refine map of branches. An example is presented, and the result demonstrates that the reconstructed 3D Chinese Hickory tree is similar to the actual one.

In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT. The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256{*}256{*}144 image).

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees.

The box-counting method is sensitive to tree positions in the coordinate system, and based on this method, assuming self-similarity of beech tree models derived from mobile laser scanning was difficult. The repetitive branching architectures of trees lead us to think that trees exhibit self-similarity and fractal properties. Therefore, applying fractal analysis to assess tree structures may seem natural. This study aimed to evaluate the box-counting method (BCM), a simple and commonly used fractal analysis, for describing 3D biological trees, using a wide range of structural models of European beech trees (Fagus sylvatica L.) derived from mobile laser scanning. We specifically investigated the method’s sensitivity to the arbitrary placement of a tree within the coordinate system and the validity of the tree’s self-similarity based on the BCM. The BCM was sensitive to the tree position in the coordinate system, with observed minimum and maximum variations in the box-counting dimension (Db) values of 0.18 and 0.52, respectively, when translating the trees in the XYZ directions. The analysis of the local slopes of the BCM, which are the slopes between neighboring data point pairs, revealed a large variation of slope values across scales with a clear pattern, indicating that the structural patterns of the sampled trees were inconsistent and locally dependent. Thus, it is difficult to assume self-similarity of the beech tree models based on the BCM. Our results demonstrate the need to standardize the computation of the Db for single trees with respect to the coordinate system and caution in interpreting the Db. These findings contribute to a deeper understanding of the Db to assess tree structures and functions.

Communication orale + résumé UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières <br/>Communication orale + résumé UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières <br/>Communication orale + résumé UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières<br/>Communication orale + résumé

The methods and algorithms for evaluating geometrical parameters of 2D and 3D tree structures are represented. Main areas of applying methods are biomedical problems connected with analyzing and measuring features of vascular system, i.e., a retina (a 2D structure) and a cardiovascular system (3D structure)). An experimental investigation has been carried out of the stability of the evaluation methods against noises of different natures and the possibility of clustering of selection of vessels based on these features.

This paper presents a boundary representation algorithm, the algorithm is developed for Nastaliq characters, but is it general in nature and can be used for boundary representation of any object. The basic idea is that instead of storing and computing the chain code of each pixel, segments of the object are computed by using corner points and a new B-Rep code is developed for the segments not for pixels, that code is scale invariant and much shorter than the chain code. A segment is a portion between two corner points; as the ratio between segments will remain same even when we change the scale of image, so we compute the ratio of each segment to the whole image, and finally compute optimized scale invariant code. Scale invariant code is a code that will remain identical even when we change the scale of an object means when we scale in/out, the output chain code will remain same, so it can be used in object representation and recognition.