scholarly journals MorphoCluster: Efficient Annotation of Plankton Images by Clustering

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3060
Author(s):  
Simon-Martin Schröder ◽  
Rainer Kiko ◽  
Reinhard Koch
Keyword(s):  
Image Annotation ◽  
Novelty Detection ◽  
Image Data ◽  
Software Tool ◽  
Data Driven ◽  
Data Sets ◽  
Fine Grained ◽  
Novel Approach ◽  
Human Ability ◽  
Similar Images

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.

scholarly journals Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

2020 ◽  
Vol 12 (21) ◽  
pp. 3672
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Software Tool ◽  
Absolute Error ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Forecast Horizon ◽  
Novel Approach ◽  
Forecasting System

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

Using manifold structure for automatic image annotation by fusion of multiple feature spaces

2015 ◽  
Vol 2 ◽  
pp. 1 ◽  
Author(s):  
Mohammad Ali Zare Chahooki ◽  
Hamid Kargar Shooroki
Keyword(s):  
Image Annotation ◽  
Similarity Measures ◽  
Main Idea ◽  
Image Data ◽  
Spatial Relations ◽  
Data Sets ◽  
Automatic Image Annotation ◽  
Manifold Structure ◽  
Feature Spaces ◽  
Multiple Feature

Automatic image annotation has been an active research topic in recent years. Low level features like as color, texture, shape as well as object spatial relations are extracted to represent images in general. These syntaxes are further used to retrieve images from large image data sets.  However, the similarity of images could not be found correctly by similarity measures such as Euclidean distance in many situations. On the other hand, graph models have been shown powerful in solving many machine learning problems in recent years. In this paper, we propose a graph-based learning approach, named Conceptual Manifold Structure (CMS), based on transition from conceptual to observation space. In the proposed method, a graph including both the trained and tested samples is constructed by fusion of multiple feature spaces. Conceptual transition in graph structure is found by altering the edge values in an innovative manner. This is caused to learn the manifold structure where the samples dissimilarity is closer to the conceptual distance. Furthermore, the continuity between the instances of a semantic in the conceptual space is kept in feature space. Keeping the continuity in manifold structure is the main idea to decrease the semantic gap in this study. The experiments on different image data sets indicated that the geometrical distances between the samples on the manifold space are closer to their conceptual distance. The proposed method has been compared to other well-known approaches. The results confirmed the effectiveness and validity of the proposed method.

scholarly journals Annotation for and Robust Parsing of Discourse Structure on Unrestricted Texts

2007 ◽  
Vol 26 (2) ◽  
Author(s):  
Jason Baldridge ◽  
Nicholas Asher ◽  
Julie Hunter
Keyword(s):  
Discourse Structure ◽  
Data Driven ◽  
Small Data ◽  
Data Sets ◽  
Wall Street ◽  
Efficient Manner ◽  
Fine Grained ◽  
Recent Developments ◽  
Robust Parsing ◽  
Computationally Intensive

AbstractPredicting discourse structure on naturally occurring texts and dialogs is challenging and computationally intensive. Attempts to construct hand-built systems have run into problems both in how to specify the required knowledge and how to perform the necessary computations in an efficient manner. Data-driven approaches have recently been shown to be successful for handling challenging aspects of discourse without using lots of fine-grained semantic detail, but they require annotated material for training. We describe our effort to annotate Segmented Discourse Representation Structures on Wall Street Journal texts, arguing that graph-based representations are necessary for adequately capturing the dependencies found in the data. We then explore two data-driven parsing strategies for recovering discourse structures. We show that the generative PCFG model of Baldridge & Lascarides (2005b) is inherently limited by its inability to incorporate new features when learning from small data sets, and we show how recent developments in dependency parsing and discriminative learning can be utilized to get around this problem and thereby improve parsing accuracy. Results from exploratory experiments on Verbmobil dialogs and our annotated news wire texts are given; these results suggest that these methods do indeed enhance performance and have the potential for significant further improvements by developing richer feature sets.

scholarly journals Optimal Features Selection for X-Ray Image Retrieval with Regular and Irregular Zoning Methods

2018 ◽  
Vol 7 (4.5) ◽  
pp. 87
Author(s):  
P. Nalini ◽  
Dr B. L. Malleswari
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Image Data ◽  
Image Features ◽  
Data Sets ◽  
Features Selection ◽  
X Ray ◽  
Eigen Values ◽  
Textural Attributes

Medical Image Retrieval is mainly meant for enhancing the healthcare system by coordinating physicians and interact with computing machines. This helps the doctors and radiologists in understanding the case and leads to automatic medical image annotation process. The choice of image attributes have crucial role in retrieving similar looking images of various anatomic regions.  In this paper we presented an empirical analysis of an X-Ray image retrieval system with intensity, statistical features, DFT and DWT transformed coefficients and Eigen values using Singular Valued Decomposition techniques as parameters. We computed these features by dividing the images in five different regular and irregular zones. In our previous work we proved that analyzing the image with local attributes result in better retrieval efficiency and hence in this paper we computed the attributes by dividing the image into 64 regular and irregular zones. This experimentation carried out on IRMA 2008 and IRMA 2009 X-Ray image data sets. In this work we come up with some conclusions like wavelet based textural attributes, intensity features and Eigen values extracted from different regular zones worked well in retrieving the images over the features computed over irregular zones. We also determined like the set of image features in which form of zoning for different anatomical regions  result in excellent retrieval of  similar looking X-Ray images.

scholarly journals AMIDE: A Free Software Tool for Multimodality Medical Image Analysis

2003 ◽  
Vol 2 (3) ◽  
pp. 153535002003031 ◽  
Author(s):  
Andreas Markus Loening ◽  
Sanjiv Sam Gambhir
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Image Data ◽  
Software Tool ◽  
Original Data ◽  
Data Sets ◽  
Multiple Data ◽  
Volumetric Medical Images ◽  
Multiple Data Sets ◽  
User Friendly

Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric medical images. Central to the package's abilities to simultaneously display multiple data sets (e.g., PET, CT, MRI) and regions of interest is the on-demand data reslicing implemented within the program. Data sets can be freely shifted, rotated, viewed, and analyzed with the program automatically handling interpolation as needed from the original data. Validation has been performed by comparing the output of AMIDE with that of several existing software packages. AMIDE runs on UNIX, Macintosh OS X, and Microsoft Windows platforms, and it is freely available with source code under the terms of the GNU General Public License.

scholarly journals Methods, New Software Tools, and Best Practices for Developing High-quality Training Data for Machine Learning-based Image Analysis in Biodiversity Research

2019 ◽  
Vol 3 ◽  
Author(s):  
Brian Stucky ◽  
Laura Brenskelle ◽  
Robert Guralnick
Keyword(s):  
Machine Learning ◽  
Image Annotation ◽  
Image Data ◽  
Error Rates ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
High Quality ◽  
Biodiversity Research ◽  
Machine Learning Models

Recent progress in using deep learning techniques to automate the analysis of complex image data is opening up exciting new avenues for research in biodiversity science. However, potential applications of machine learning methods in biodiversity research are often limited by the relative scarcity of data suitable for training machine learning models. Development of high-quality training data sets can be a surprisingly challenging task that can easily consume hundreds of person-hours of time. In this talk, we present the results of our recent work implementing and comparing several different methods for generating annotated, biodiversity-oriented image data for training machine learning models, including collaborative expert scoring, local volunteer image annotators with on-site training, and distributed, remote image annotation via citizen science platforms. We discuss error rates, among-annotator variance, and depth of coverage required to ensure highly reliable image annotations. We also discuss time considerations and efficiency of the various methods. Finally, we present new software, called ImageAnt (currently under development), that supports efficient, highly flexible image annotation workflows. ImageAnt was created primarily in response to the challenges we discovered in our own efforts to generate image-based training data for machine learning models. ImageAnt features a simple user interface and can be used to implement sophisticated, adaptive scripting of image annotation tasks.

scholarly journals Imaging Informatics: Toward Capturing and Processing Semantic Information in Radiology Images

2010 ◽  
Vol 19 (01) ◽  
pp. 34-42 ◽  
Author(s):  
S. Napel ◽  
D. L. Rubin
Keyword(s):  
Large Scale ◽  
Image Annotation ◽  
Image Data ◽  
Molecular Data ◽  
Imaging Informatics ◽  
Future Research ◽  
Data Sets ◽  
Radiology Reports ◽  
Challenges And Opportunities

Summary Objectives: To identify challenges and opportunities in imaging informatics that can lead to the use of images for discovery, and that can potentially improve the diagnostic accuracy of imaging professionals. Methods: Recent articles on imaging informatics and related articles from PubMed were reviewed and analyzed. Some new developments and challenges that recent research in imaging informatics will meet are identified and discussed. Results: While much literature continues to be devoted to traditional imaging informatics topics of image processing, visualization, and computerized detection, three new trends are emerging: (1) development of ontologies to describe radiology reports and images, (2) structured reporting and image annotation methods to make image semantics explicit and machine-accessible, and (3) applications that use semantic image information for decision support to improve radiologist interpretation performance. The informatics methods being developed have similarities and synergies with recent work in the biomedical informatics community that leverage large highthroughput data sets, and future research in imaging informatics will build on these advances to enable discovery by mining large image databases. Conclusions: Imaging informatics is beginning to develop and apply knowledge representation and analysis methods to image datasets. This type of work, already commonplace in biomedical research with large scale molecular and clinical datasets, will lead to new ways for computers to work with image data. The new advances hold promise for integrating imaging with the rest of the patient record as well as molecular data, for new data-driven discoveries in imaging analogous to that in bioinformatics, and for improved quality of radiology practice.

scholarly journals Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning

2021 ◽  
Vol 17 (2) ◽  
pp. e1008630
Author(s):  
Philipp Mergenthaler ◽  
Santosh Hariharan ◽  
James M. Pemberton ◽  
Corey Lourenco ◽  
Linda Z. Penn ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Feature Learning ◽  
Image Data ◽  
Data Driven ◽  
Cell Segmentation ◽  
Data Sets ◽  
Phenotypic Analysis ◽  
Using Data ◽  
Microscopy Data ◽  
Free Machine

Phenotypic profiling of large three-dimensional microscopy data sets has not been widely adopted due to the challenges posed by cell segmentation and feature selection. The computational demands of automated processing further limit analysis of hard-to-segment images such as of neurons and organoids. Here we describe a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) image data; using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and advanced data visualization. We demonstrate the analysis potential on complex 3D images by investigating the phenotypic alterations of: neurons in response to apoptosis-inducing treatments and morphogenesis for oncogene-expressing human mammary gland acinar organoids. Our novel implementation of image analysis algorithms called Phindr3D allowed rapid implementation of data-driven voxel-based feature learning into 3D high content analysis (HCA) operations and constitutes a major practical advance as the computed assignments represent the biology while preserving the heterogeneity of the underlying data. Phindr3D is provided as Matlab code and as a stand-alone program (https://github.com/DWALab/Phindr3D).

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Truck Weight Enforcement Measures of Effectiveness: Development and Software Application

1998 ◽  
Vol 1643 (1) ◽  
pp. 152-160 ◽  
Author(s):  
F. R. Hanscom ◽  
M. W. Goelzer
Keyword(s):  
Software Tool ◽  
Field Evaluation ◽  
Seasonal Effects ◽  
Data Sets ◽  
Activity Data ◽  
Software Application ◽  
Enforcement Effort ◽  
Measures Of Effectiveness ◽  
Truck Weight ◽  
Enforcement Activity

A software tool was developed to determine what is accomplished as the result of truck weight enforcement efforts. Traditionally applied measures (e.g., numbers of trucks weighed and citations issued) have simply provided indications of enforcement effort. These previously applied measures failed to provide results in terms of real enforcement objectives, such as deterring overweight trucks and minimizing pavement wear and tear. Consequently the need exists to develop and validate truck weight enforcement measures of effectiveness (MOE). MOEs were developed via a series of analytical procedures. They were subsequently validated in a comprehensive four-state field evaluation. Matched (weigh-in-motion) (WIM) data sets, collected under controlled baseline and enforcement conditions, were analyzed to determine the sensitivity of candidate MOEs to actual enforcement activity. Data collection conditions were controlled in order to avoid contamination from hour-of-day, day-of-week, and seasonal effects. The following MOEs, were validated on the basis of their demonstrated sensitivity to truck weight enforcement objectives and the presence of enforcement activity: (1) severity of overweight violations, (2) proportion of overweight trucks, (3) average equivalent single-axle load (ESAL), (4) excess ESALs, and (5) bridge formula violations. These measures are sensitive to legal load-limit compliance objectives of truck weight enforcement procedures as well as the potential for overweight trucks to produce pavement deterioration. The software User Guide that statistically compares calculated MOEs between observed enforcement conditions is described in this paper. The User Guide also allows users to conduct an automated pavement design life analysis estimating, the theoretical pavement-life effect resulting from the observed enforcement activity.

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