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shape classification of Cloud Particles Recorded by the 2D-S Imaging Probe Using a Convolutional Neural Network

Journal of Meteorological Research 2023年第4期37卷 521-535页

作者： Rong ZHANG Haixia XIAO Yang GAO Haizhou SU Dongnan LI Lei WEI Junxia LI Hongyu LI Cloud–Precipitation Physics and Weather Modification Key Laboratory(CPML) China Meteorological AdministrationBeijing 100081 CMA Weather Modification Centre China Meteorological Administration(CMA)Beijing 100081 Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041 Heilongjiang Weather Modification Office Harbin 150030

The airborne two-dimensional stereo(2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classification tools,our ability to extract meaningful morphological information related to cloud microphysical processes is limited. To solve this issue, we propose a novel classification algorithm for 2D-S cloud particle images based on a convolutional neural network(CNN), named CNN-2DS. A 2D-S cloud particle shape dataset was established by using the 2D-S cloud particle images observed from 13 aircraft detection flights in 6 regions of China(Northeast, Northwest, North,East, Central, and South China). This dataset contains 33,300 cloud particle images with 8 types of cloud particle shape(linear, sphere, dendrite, aggregate, graupel, plate, donut, and irregular). The CNN-2DS model was trained and tested based on the established 2D-S dataset. Experimental results show that the CNN-2DS model can accurately identify cloud particles with an average classification accuracy of 97%. Compared with other common classification models [e.g., Vision Transformer(ViT) and Residual Neural Network(ResNet)], the CNN-2DS model is lightweight(few parameters) and fast in calculations, and has the highest classification accuracy. In a word, the proposed CNN-2DS model is effective and reliable for the classification of cloud particles detected by the 2D-S probe.

关键词： cloud particles particle shape 2D-S probe shape classification convolutional neural network

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Planar cubic hybrid hyperbolic polynomial curve and its shape classification

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Progress in Natural Science:Materials International 2004年第1期14卷 42-47页

作者： WANG Guozhao 1,2 * and YANG Qinmin 1,2 (1. Institute of Computer Graphics and Image Processing, 2. Department of Mathematics, Zhejiang University, Hangzhou 310027, China) Zhejiang Univ Inst Comp Graph & Image Proc Hangzhou 310027 Peoples R China Zhejiang Univ Dept Math Hangzhou 310027 Peoples R China

The planar cubic hybrid hyperbolic polynomial curves and cubic H Bézier curves are presented. The conditions leading to inflection points and singularities (cusps and loops) are investigated and the shape of these curves is classified. The conclusions enable us to detect inflection points and singularities and get an idea of how to preserve the fair shape when designing such curves.

关键词： hybrid hyperbolic polynomial curve H-Bézier curve shape classification inflection points singularities

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A Unified Feature Representation and Learning Framework for 3D shape

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Chinese Journal of Electronics 2019年第5期28卷 993-999页

作者： MU Panpan ZHANG Sanyuan PAN Xiang HONG Zhenjie Art Design College Zhejiang Gongshang University College of Computer Science and Technology Zhejiang University College of Computer Science and Technology Zhejiang University of Technology School of Mathematics and Information Science Wenzhou University

In conventional 3D shape retrieval and classification, they differentiate each other in their final stages. We propose a unified feature representation and learning framework for the instance-based shape retrieval and classification. Firstly, we render every 3D model in several directions and use the produced view-sets to represent the 3D models. In this way, both tasks can be tackled by measuring the distances between rendered views of 3D models. Secondly, we construct the viewsets as Symmetric positive definite matrices(SPDMs),which are points on a Riemannian manifold. Thus, the shape retrieval and classification tasks are reduced to a problem of measuring the distances between projected views and SPDMs. To solve this heterogeneous problem,we map them to a Hilbert space using a method of point-to-set matching. In this Hilbert space, the distances are surprisingly easy to calculate. Finally, we use a robust nearest-neighbor approach to unify the instancebased shape retrieval and classification. Our framework combines the state-of-the-art deep learning approaches with traditional mathematical optimization method, makes full use of both advantages, which is much more flexible than pure deep learning methods. Experimental results show the efficiency of our approach.

关键词： shape retrieval shape classification Deep learning Euclidean space Riemannian manifold Hilbert space Metric learning

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