三维点云数据的单木植物器官分割的研究仍处于起步阶段,对于植物器官的精确分割还未形成有效的解决方法,对于单个花朵点云分割出花瓣的研究更是少见。为此,文中提出一种基于PCL库三维点云花瓣分割的研究方法,以单个百合花朵三维点云数

针对叶缘具有锯齿特征的植物叶片进行研究,提出一种基于变形的叶片边缘锯齿的形状模拟方法。根据实际的树叶图像提取叶片的长宽比、圆形度等形状信息,确定基础矩形的参数;将变形函数应用在生成的矩形平面上,模拟矩圆形、椭圆形、卵圆形

三维点云在机器人与自动驾驶中都有着普遍的应用,深度学习在二维图像上的研究成果显著,但是如何利用深度学习识别不规则的三维点云,仍然是一个开放性的问题。目前大场景点云自身数据的复杂性,点云扫描距离的变化造成点的分布不均匀,噪

随着大场景三维点云应用在越来越多的领域中,近些年对激光点云大场景下的分类研究不断深入,各种分类模型层出不穷,在大场景点云分类任务中表现优异,但是依然存在训练时间长、计算复杂以及分类精度低等问题。针对分类精度低这一问题,提

三维点云数据包含着丰富的形状和比例信息,如何有效准确地对点云数据进行分类已经成为了目前计算机视觉领域的研究热点。由于点云在非欧氏空间中的不规则稀疏结构,并且现有的基于深度学习的三维点云分类模型中缺乏对各个点的局部信息和

The traditional stem model is inconsistent with the real geometry of the stem. Terrestrial laser scanning (TLS) provides a possibility of constructing a realistic stem model. In this study, we present a 3D stem model, which includes the stem axis curve and stem cross-sectional profile curve, with geometrical consistency and stem parameter retrieval methods using TLS data. The 3D stem axis curve is interpolated based on the geometric central points, which are calculated from stem slices formed by stem cross-sections. From the 3D stem axis curve, the stem shape characteristic is clearly depicted in 3D space. Stem parameters, such as the location of a stem cross-section, stem diameter, height, length, curvature and torsion at any position along the stem, are calculated. The stem cross-sectional profile curve is interpolated based on the stem cross-sectional profile points, which are calculated from stem cross-sectional points by angle simplification. Then, the convex-concave characteristic of the stem cross-section is represented by the stem cross-sectional profile curve, and from this, the integral method for basal area calculation is presented. The presented methods were tested on stem points from different tree species with different shape properties. The feasibility and validity of the 3D stem model was demonstrated by 3D stem model visualization. The root mean square error (RMSE) of the presented stem diameter method was 0.129 cm. Compared with the basal area calculated from the cross-sectional profile curve, the mean absolute percentage error (MAPE) and RMSE values of the traditional method were 8.921% and 49.926 cm2, respectively. The stem parameter retrieval experiment demonstrated the accuracy and practicability of the 3D stem model. Compared with the traditional stem model, the 3D stem model can accurately represent the geometric structure of the stem and provide accurate calculations of stem parameters. It will help improve the applications of TLS in forestry inventories. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Automatic understanding of floor plan images is a key component of various applications. Due to the style diversity of rural housing design, the latest learning-based approaches cannot achieve satisfactory recognition results. In this paper, we present a new framework for parsing floor plans of rural residence that combines semantic neural networks with a post processed room segmentation. First, we take case studies from typical residential buildings in China's rural areas and provide a novel image dataset, called RuralHomeData, containing 800 rural residence floor plans with accurate man-made annotations. Based on the dataset, we propose a new deep learning-based recognition framework using a joint neural network to predict the geometric elements and text information on the floor plan simultaneously. Our insight is that walls and openings (doors and windows) are the basic elements corresponding to the room boundary that a closed 1D loop must form a certain room. Then the semantic information (e.g., the room function) of room regions can be obtained through text detection and identification. Furthermore, we use the MIQP algorithm to divide the area containing multiple room type texts into multiple room areas. Finally, the input floor plan can be transformed into a room layout graph with room attributes and adjacent relationships. The proposed algorithm has been tested on both urban and rural datasets, and the experimental results demonstrate our efficiency and robustness compared with the state-of-the-art methods.(c) 2021 Elsevier Inc. All rights reserved.

针对有背景干扰的番茄病理叶片,将k-means分割与迁移学习相结合,提出一种基于k-means分割和迁移学习的方法对番茄病害叶片进行识别。首先对原始图像进行一系列预处理,再将处理后的图像进行k-means分割,得到叶片边缘的最小矩阵图像,之后

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bezier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.