Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.
In this article, we propose an automatic method for the segmentation of pollen grains from microscope images, followed by the automatic segmentation of their exine. The objective of exine segmentation is to separate the pollen grain in two regions of interest: exine and inner part. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. As a rough stage, grain segmentation is performed by a procedure involving clustering and morphological operations, while the exine is approximated by an iterative procedure consisting in consecutive cropping steps of the pollen…
In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal images. All stages of the framework's pipeline are designed to be used in an automatic fashion. First, the optimal focus is selected using the absolute gradient method. Then, pollen grains are extracted usin…
Medical University of Vienna（维也纳医科大学）；Loughborough University（拉夫堡大学）；TissueGnostics GmbH（蒂苏格诺斯蒂克有限公司）
Pollen grain micrograph classification has multiple applications in medicine and biology. Automatic pollen grain image classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. While a number of computer-based methods have been introduced in the literature to perform this task, classification performance needs to be improved for these methods to be useful in practice. In this paper, we present an ensemble approach for pollen grain microscopic image classification into four categories: Corylus Avellana well-developed pollen grain, Corylus Avel…
European Centre for Soft Computing（欧洲软计算中心）；Inspiralia Tecnologías Avanzadas（因斯皮拉利亚先进技术公司）；Automatics and Robotics Center - Universidad Politécnica de Madrid（马德里理工大学自动化与机器人中心）
We present a complete methodology for authenticating local bee pollen against fraudulent samples using image processing and machine learning techniques. The proposed standard methods do not need expensive equipment such as advanced microscopes and can be used for a preliminary fast rejection of unknown pollen types. The system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory. Methods are based on the color properties of bee pollen loads images and the use of one-class classifiers which are appropriate to…
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）
We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification of pollen. We embed images of pollen grains into a low-dimensional latent space and compare Euclidean and Riemannian metrics on these spaces for clustering. We propose this system for automated analysis of pollen and other microscopic biological structures which have only small or unlabelled datasets available.
Deep learning approaches have shown great success in image classification tasks and can aid greatly towards the fast and reliable classification of pollen grain aerial imagery. However, often-times deep learning methods in the setting of natural images can suffer generalization problems and yield poor performance on unseen test distribution. In this work, we present and a robust deep learning framework that can generalize well for pollen grain aerobiological imagery classification. We develop a convolutional neural network-based pollen grain classification approach and combine some of the best…
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
Institute of Technical Informatics, Graz University of Technology（格拉茨工业大学技术信息研究所）；University of Technology and Education, University of Da Nang（岘港大学理工大学）；Complexity Science Hub Vienna（维也纳复杂性科学中心）
We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to m…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Bureau of Economic Geology, University of Texas at Austin（德克萨斯大学奥斯汀分校经济地质局）
Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it ea…
Automated Palynological Analysis System: Integrating Deep Metric Learning, Detection and Classification in Bright Field Microscopy
Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción（物理系，物理与数学科学学院，康塞普西翁大学）；Facultad de Ingeniería, Universidad San Sebastián（工程学院，圣塞巴斯蒂安大学）；Facultad de Ingeniería, Universidad Andres Bello（工程学院，安德烈斯·贝洛大学）；Universidad Arturo Prat, Sede Victoria（阿尔托普大学，维多利亚校区）；Laboratory of Palynology and Plant Ecology, School of Sciences and Technologies, University of Concepción（古生物与植物生态实验室，科学与技术学院，康塞普西翁大学）
MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model
University of Science and Technology Beijing（北京科技大学）；Beijing Advanced Innovation Center for Materials Genome Engineering（北京材料基因工程高精尖创新中心）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
本文提出基于 SAM 的 MatSAM，通过无训练的自适应点提示与后处理实现材料显微结构零样本分割，在16个 OM/SEM 数据集上表现优于规则法并可比监督模型。
DS$^2$Net: Detail-Semantic Deep Supervision Network for Medical Image Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China（教育部多媒体可信感知与高效计算重点实验室）；Xiamen University（厦门大学）；Computer Engineering College（计算机工程学院）
IBM Research Europe - Zurich（IBM欧洲研究院-苏黎世）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect analysis on the sub-nanometer scale is enabled by high-resolution (scanning) transmission electron microscopy [HR(S)TEM], where the identification of defects is currently carried out based on human expertise. However, the process is tedious, highly time consuming and, in some cases, yields ambiguous results. Here we propose a semi-supervised machine learning method that assists in the detection of lattice defects from…
State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences（中国地质大学地球科学学院生物地质与环境地质国家重点实验室）
Petrographic analysis based on microfacies identification in thin sections is widely used in sedimentary environment interpretation and paleoecological reconstruction. Fossil recognition from microfacies is an essential procedure for petrographers to complete this task. Distinguishing the morphological and microstructural diversity of skeletal fragments requires extensive prior knowledge of fossil morphotypes in microfacies and long training sessions under the microscope. This requirement engenders certain challenges for sedimentologists and paleontologists, especially novices. However, a mach…
An Interactive, Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experiments
electron Physical Science Imaging Centre, Diamond Light Source（电子物理科学成像中心，钻石光源）；Department of Materials, University of Oxford（牛津大学材料系）；Johnson Matthey（庄信万丰）
