scholarly journals CobWeb 1.0: Machine Learning Tool Box for Tomographic Imaging

2019 ◽  
Author(s):  
Swarup Chauhan ◽  
Kathleen Sell ◽  
Freider Enzmann ◽  
Wolfram Rühaak ◽  
Thorsten Wille ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
User Interface ◽  
Image Enhancement ◽  
Graphical User Interface ◽  
Supervised Machine Learning ◽  
Easy Access ◽  
Digital Rock ◽  
Computational Performance

Abstract. Despite the availability of both commercial and open source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pin point. More often image segmentation is driven manually where the performance remains limited to two phases. Discrepancies due to artefacts causes inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0 which is automated and explicitly tailored for accurate grayscale (multi-phase) image segmentation using unsupervised and supervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform Image enhancement, Image segmentation and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of windows executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray micro-tomography datasets. Albeit the high spatial resolution (sub-micrometer), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.

scholarly journals CobWeb 1.0: machine learning toolbox for tomographic imaging

2020 ◽  
Vol 13 (1) ◽  
pp. 315-334
Author(s):  
Swarup Chauhan ◽  
Kathleen Sell ◽  
Wolfram Rühaak ◽  
Thorsten Wille ◽  
Ingo Sass
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
User Interface ◽  
Image Enhancement ◽  
Graphical User Interface ◽  
Machine Learning Techniques ◽  
Easy Access ◽  
Digital Rock ◽  
Learning Techniques

Abstract. Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform image enhancement and image segmentation, and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3-D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of Windows-executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation, and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray microtomography datasets. Regardless of the high spatial resolution (submicrometre), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.

siritool: A Matlab Graphical User Interface for Image Analysis in Structured-Illumination Reflectance Imaging for Fruit Defect Detection

2020 ◽  
Vol 63 (4) ◽  
pp. 1037-1047
Author(s):  
Yuzhen Lu ◽  
Renfu Lu
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
User Interface ◽  
Image Enhancement ◽  
Graphical User Interface ◽  
Defect Detection ◽  
Object Segmentation ◽  
Structured Illumination ◽  
Image Preprocessing ◽  
Pickling Cucumbers

HIGHLIGHTSA Matlab GUI, siriTool, was developed for structured-illumination reflectance imaging.siriTool enables image preprocessing, feature extraction, and classification.siriTool was demonstrated for detection of spot defects on pickling cucumbers.Abstract. Structured-illumination reflectance imaging (SIRI) is an emerging imaging modality that provides more useful discriminative features for enhancing detection of defects in fruit and other horticultural and food products. In this study, we developed a Matlab graphical user interface (GUI), siriTool (available at https://codeocean.com/capsule/5699671/tree), to facilitate image analysis in SIRI for fruit defect detection. The GUI enables image preprocessing (i.e., demodulation, object segmentation, and image enhancement), feature extraction and selection, and classification. Demodulation is done using a three-phase or two-phase approach depending on the image data acquired, object segmentation (or background removal) is implemented based on automatic unimodal thresholding, and image enhancement is achieved using fast bi-dimensional empirical decomposition followed by selective image reconstructions. For defect detection, features of different types are extracted from the enhanced images, and feature selection is performed to reduce the feature set. Finally, the full or reduced set of features are then input into different classifiers, e.g., support vector machine (SVM), for image-level classifications. An application example is presented on the detection of yellowish subsurface spot defects in pickling cucumbers. SIRI achieved over 98% classification accuracies based on SVM modeling with the extracted features, which were significantly better than the accuracies obtained under uniform illumination. Keywords: Defect detection, Demodulation, Image enhancement, Machine learning, Matlab, Structured illumination.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

Image Analysis and Development of Graphical User Interface for Pole Vault Action

2018 ◽  
Vol 6 (2) ◽  
pp. 109-116
Author(s):  
Rajeev Kanth ◽  
◽  
Jukka-Pekka Skön ◽  
Kari Lehtomäki ◽  
Paavo Nevalainen ◽  
...  
Keyword(s):  
Image Analysis ◽  
User Interface ◽  
Graphical User Interface ◽  
Pole Vault

scholarly journals HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246039
Author(s):  
Shilan S. Hameed ◽  
Rohayanti Hassan ◽  
Wan Haslina Hassan ◽  
Fahmi F. Muhammadsharif ◽  
Liza Abdul Latiff
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Efficient Algorithm ◽  
Gene Selection ◽  
High Dimensional ◽  
Competitive Performance ◽  
User Friendly ◽  
High Dimensional Datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

Computer Microvision for MEMS

1999 ◽  
Vol 605 ◽  
Author(s):  
Dennis M. Freeman
Keyword(s):  
Image Analysis ◽  
Data Analysis ◽  
User Interface ◽  
Graphical User Interface ◽  
3D Structure ◽  
Ccd Camera ◽  
Optical Microscope ◽  
Stroboscopic Illumination ◽  
Frame Grabber

AbstractWe have developed a versatile instrument for in situ measurement of motions of MEMS. Images of MEMS are magnified with an optical microscope and projected onto a CCD camera. Stroboscopic illumination is used to obtain stop-action images of the moving structures. Stopaction images from multiple focal planes provide information about 3D structure and 3D motion. Image analysis algorithms determine motions of all visible structures with nanometer accuracy.Hardware for the system includes the microscope, CCD camera and associated frame grabber, piezoelectric focusing element, and a modular stimulator that generates arbitrary periodic waveforms and synchronized stroboscopic illumination. These elements are controlled from a Pentium-based computer using a graphical user interface that guides the user through both data collection and data analysis. The system can measure motions at frequencies as high as 5 MHz with nanometer resolution, i.e., well below the wavelength of light.

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