Graphical User Interface (GUI) for Thumbprint Image Enhancement and Minutiae Extraction

2011 ◽  
Author(s):  
Nani Fadzlina Naim ◽  
Abdul Rahman Bin Daud ◽  
Ahmad Ihsan Mohd Yassin
Keyword(s):  
User Interface ◽  
Image Enhancement ◽  
Graphical User Interface ◽  
Minutiae Extraction

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.

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 A Smart Image Enhancement Monitoring System Using Graphical User Interface (GUI)

2020 ◽  
Vol 1529 ◽  
pp. 022081
Author(s):  
Syafiq Sam ◽  
Wan Azani Mustafa ◽  
Syed Zulkarnain Syed Idrus ◽  
Mohd Aminudin Jamlos ◽  
Mohamad Nur Khairul Hafizi Rohani ◽  
...  
Keyword(s):  
User Interface ◽  
Image Enhancement ◽  
Monitoring System ◽  
Graphical User Interface

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.

SMART GRAPHICAL USER INTERFACE FOR WIRELESS SENSOR NETWORK

2018 ◽  
Vol 13 (1) ◽  
pp. 1 ◽  
Author(s):  
P. V. MANE-DESHMUKH ◽  
Keyword(s):  
User Interface ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Graphical User Interface ◽  
Wireless Sensor

A Graphical User Interface (GUI) for Automated Classification of Bradley Fighting Vehicle Shock Absorbers

1998 ◽  
Author(s):  
Patrick Sincebaugh ◽  
William Green ◽  
Gerard Rinkus
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Automated Classification ◽  
Shock Absorbers ◽  
Bradley Fighting Vehicle

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

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