Study of Various Types of Data Annotation

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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Detecting discriminatory risk through data annotation based on Bayesian inferences

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency ◽

10.1145/3442188.3445940 ◽

2021 ◽

Author(s):

Elena Beretta ◽

Antonio Vetrò ◽

Bruno Lepri ◽

Juan Carlos De Martin

Keyword(s):

Data Annotation ◽

Bayesian Inferences

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Towards Large-Scale Data Annotation of Audio from Wearables: Validating Zooniverse Annotations of Infant Vocalization Types

2021 IEEE Spoken Language Technology Workshop (SLT) ◽

10.1109/slt48900.2021.9383511 ◽

2021 ◽

Author(s):

Chiara Semenzin ◽

Lisa Hamrick ◽

Amanda Seidl ◽

Bridgette Kelleher ◽

Alejandrina Cristia

Keyword(s):

Large Scale ◽

Data Annotation ◽

Large Scale Data ◽

Infant Vocalization ◽

Scale Data

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EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

Remote Sensing ◽

10.3390/rs13132622 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2622

Author(s):

Haozhou Wang ◽

Yulin Duan ◽

Yun Shi ◽

Yoichiro Kato ◽

Seish Ninomiya ◽

...

Keyword(s):

Data Processing ◽

Plant Phenotyping ◽

Phenotypic Traits ◽

Data Annotation ◽

Intermediate Data ◽

Reconstruction Quality ◽

Open Source Software Package ◽

Machine Learning Applications ◽

Data Processor ◽

Uav Images

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include 1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and 2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

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Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

Remote Sensing ◽

10.3390/rs13152868 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2868

Author(s):

Yonglin Tian ◽

Xiao Wang ◽

Yu Shen ◽

Zhongzheng Guo ◽

Zilei Wang ◽

...

Keyword(s):

Point Cloud ◽

Closed Loop ◽

Real Data ◽

Point Clouds ◽

Autonomous Driving ◽

Training Model ◽

Labor Cost ◽

Open Loop ◽

Training Dataset ◽

Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

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Unsupervised content-preserving transformation for optical microscopy

Light Science & Applications ◽

10.1038/s41377-021-00484-y ◽

2021 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Xinyang Li ◽

Guoxun Zhang ◽

Hui Qiao ◽

Feng Bao ◽

Yue Deng ◽

...

Keyword(s):

Deep Learning ◽

Optical Microscopy ◽

Training Data ◽

Fluorescence Labeling ◽

Imaging Data ◽

Image Transformation ◽

General Applicability ◽

Data Annotation ◽

Biomedical Image ◽

Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

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Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

Applied Sciences ◽

10.3390/app11136006 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6006

Author(s):

Huy Le ◽

Minh Nguyen ◽

Wei Qi Yan ◽

Hoa Nguyen

Keyword(s):

Machine Learning ◽

Augmented Reality ◽

Object Detection ◽

Feature Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Data Annotation ◽

Machine Learning Model ◽

Potential Benefits ◽

Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

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A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽

10.3390/s21103406 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3406

Author(s):

Jie Jiang ◽

Yin Zou ◽

Lidong Chen ◽

Yujie Fang

Keyword(s):

Image Retrieval ◽

Indoor Localization ◽

Data Sets ◽

Indoor Environments ◽

Global Features ◽

Data Set ◽

Data Annotation ◽

Wide Range ◽

Annotation Costs ◽

Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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