AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications.
Code (0)
등록된 구현이 없습니다.
Tasks
Plant PhenotypingSimilar Papers 제목 키워드 기반
An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks
In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field. This demand imply objective, precise a…
Objectobject-detectionObject DetectionUnsupervised Domain Adaptation For Plant Organ Counting
Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenoty…
Domain AdaptationObjectObject CountingPlant Phenotyping+1A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surround…
Medical Image AnalysisOrgan SegmentationSegmentationPrompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays
Image segmentation plays a vital role in the medical field by isolating organs or regions of interest from surrounding areas. Traditionally, segmentation models are trained on a specific organ or a disease, limiting thei…
AllImage SegmentationSegmentationSemantic SegmentationLow-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation
Deep segmentation networks achieve high performance when trained on specific datasets. However, in clinical practice, it is often desirable that pretrained segmentation models can be dynamically extended to enable segmen…
Continual Semantic SegmentationOrgan SegmentationSegmentationSemantic Segmentation