scholarly journals Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1098
Author(s):  
Michael Henke ◽  
Kerstin Neumann ◽  
Thomas Altmann ◽  
Evgeny Gladilin
Keyword(s):  
Image Data ◽  
Ground Truth ◽  
Classification Model ◽  
Computationally Efficient ◽  
Computational Tools ◽  
Ground Truth Data ◽  
Large Variability ◽  
Average Accuracy ◽  
Plant Image ◽  
Binary Segmentation

Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models

2018 ◽  
Author(s):  
Christian Damgaard
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Learning Algorithms ◽  
Plant Competition ◽  
Image Data ◽  
Ground Truth ◽  
Ecological Models ◽  
Machine Learning Algorithms ◽  
Ground Truth Data ◽  
Ground Truthing

AbstractIn order to fit population ecological models, e.g. plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

scholarly journals Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models

2021 ◽  
Vol 13 (6) ◽  
pp. 1161
Author(s):  
Christian Damgaard
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Learning Algorithms ◽  
Plant Competition ◽  
Image Data ◽  
Ground Truth ◽  
Ecological Models ◽  
Machine Learning Algorithms ◽  
Ground Truth Data ◽  
Ground Truthing

In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

scholarly journals A Corpus-Based Study of Linguistic Deception in Spanish

2021 ◽  
Vol 11 (19) ◽  
pp. 8817
Author(s):  
Ángela Almela
Keyword(s):  
Language Processing ◽  
Ad Hoc ◽  
Deception Detection ◽  
Ground Truth ◽  
Research Area ◽  
Classification Model ◽  
Computer Assisted ◽  
Ground Truth Data ◽  
Linguistic Cues ◽  
Cues To Deception

In the last decade, fields such as psychology and natural language processing have devoted considerable attention to the automatization of the process of deception detection, developing and employing a wide array of automated and computer-assisted methods for this purpose. Similarly, another emerging research area is focusing on computer-assisted deception detection using linguistics, with promising results. Accordingly, in the present article, the reader is firstly provided with an overall review of the state of the art of corpus-based research exploring linguistic cues to deception as well as an overview on several approaches to the study of deception and on previous research into its linguistic detection. In an effort to promote corpus-based research in this context, this study explores linguistic cues to deception in the Spanish written language with the aid of an automatic text classification tool, by means of an ad hoc corpus containing ground truth data. Interestingly, the key findings reveal that, although there is a set of linguistic cues which contributes to the global statistical classification model, there are some discursive differences across the subcorpora, yielding better classification results on the analysis conducted on the subcorpus containing emotionally loaded language.

scholarly journals PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

2017 ◽  
Author(s):  
Malia A Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C Berry ◽  
Steven T Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

2017 ◽  
Author(s):  
Malia A Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C Berry ◽  
Steven T Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 684-704
Author(s):  
Karen Panetta ◽  
Landry Kezebou ◽  
Victor Oludare ◽  
James Intriligator ◽  
Sos Agaian
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Image Data ◽  
Ground Truth ◽  
Quantitative Model ◽  
Transportation Systems ◽  
Ground Truth Data ◽  
Vehicle Recognition ◽  
And Performance

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to compute valid target information from textual input. This work further introduces two novel datasets to advance AI research in these challenging areas. These datasets include (a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 color classes—twice as many as the number of color classes in the largest existing such dataset—to facilitate finer-grain recognition with color information; and (b) a Vehicle Recognition in Video (VRiV) dataset, a first of its kind video testbench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of annotated traffic vehicle recognition video testbench dataset. Finally, to address the gap in the field, five novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. T One major advantage of the proposed vehicle search and continuous localization framework is that it could be integrated in ITS software solution to aid law enforcement, especially in critical cases such as of amber alerts or hit-and-run incidents.

scholarly journals The accuracy of several pose estimation methods for 3D joint centre localisation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laurie Needham ◽  
Murray Evans ◽  
Darren P. Cosker ◽  
Logan Wade ◽  
Polly M. McGuigan ◽  
...  
Keyword(s):  
Motion Capture ◽  
Pose Estimation ◽  
High Speed ◽  
Large Scale ◽  
Image Data ◽  
Ground Truth ◽  
Human Movement ◽  
Estimation Methods ◽  
Ground Truth Data ◽  
Estimation Algorithms

AbstractHuman movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies ‘in the wild’, i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied to 2D image data and 3D joint centre locations were reconstructed. Pose estimation derived joint centres demonstrated systematic differences at the hip and knee (~ 30–50 mm), most likely due to mislabeling of ground truth data in the training datasets. Where systematic differences were lower, e.g., the ankle, differences of 1–15 mm were observed depending on the activity. Markerless motion capture represents a highly promising emerging technology that could free movement scientists from laboratory environments but 3D joint centre locations are not yet consistently comparable to marker-based motion capture.

scholarly journals Deep Learning on 1-D Biosignals: a Taxonomy-based Survey

2018 ◽  
Vol 27 (01) ◽  
pp. 098-109 ◽  
Author(s):  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan ◽  
Thomas Deserno
Keyword(s):  
Deep Learning ◽  
Electrodermal Activity ◽  
Ground Truth ◽  
Training Data ◽  
Future Research ◽  
Learning Models ◽  
Restricted Boltzmann Machines ◽  
Ground Truth Data ◽  
Large Variability ◽  
Learning Techniques

Objectives: Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field. Methods: A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model. Results: Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography, photoplethysmography, electrooculography, continuous glucose monitoring, acoustic respiratory signal, blood pressure, and electrodermal activity signal, while ballistocardiography or seismocardiography have yet to be analyzed using deep learning techniques. In supervised and unsupervised applications, CNNs and restricted Boltzmann machines are the most and least frequently used, (n = 34) and (n = 15), respectively. Conclusion: Our key-code classification of relevant papers was used to cluster the approaches that have been published to date and demonstrated a large variability of research with respect to data, application, and network topology. Future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness. Furthermore, application-driven approaches and updated training data from mobile recordings are needed.

WEIGHT DETERMINATION FOR SUPERVISED BINARIZATION ALGORITHM BASED ON QR DECOMPOSITION

2017 ◽  
Vol 79 (2) ◽  
Author(s):  
Fauziah Kasmin ◽  
Azizi Abdullah ◽  
Anton Satria Prabuwono
Keyword(s):  
Decomposition Method ◽  
Heuristic Method ◽  
Ground Truth ◽  
Qr Decomposition ◽  
Ground Truth Data ◽  
Average Accuracy ◽  
Level Information ◽  
Binarization Algorithm ◽  
Selection Of

Supervised binarization is a method that learn pre-classified data in order to classify a particular pixel whether it is belong to a foreground or a background. The performance of supervised approach is usually better than that of unsupervised ones since it is designed to use classification criteria determined by ground truth data. By using this approach, orientations of local neighbourhood grey level information that are based on eight orientations have been developed to characterize a particular pixel. These orientations are combined together since it may reduce the risk of making a particular poor selection of these orientations. In order to ensemble all orientations, heuristic method have been used to determine weights for each orientation. However, determination of weights using heuristic method is not efficient and not enough as it provides incomplete information. Furthermore, these orientations might be influenced by other different factors. This will lead to wrongly assigning weights to a particular orientation. Hence, determination of weights to combine eight orientations to characterize a particular pixel by using QR decomposition method is proposed. By using QR decomposition method, computational complexity is low and weights obtained for each orientation are optimal. In order to test the proposed approach, 21 document images from DIBCO2009 and DIBCO2011 databases and 55 retinal images from DRIVE and STARE databases have been used. The results of the proposed method clearly show significant improvement where higher average accuracy is obtained compared to by using heuristic method.      

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