Abrasion Measurement of Spun Yarns by Image Analysis and Artificial Intelligence Techniques

2014 ◽  
Vol 18 (3) ◽  
pp. 61-68
Author(s):  
D. Semnani
Keyword(s):  
Research Work ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The Body ◽  
Neural Nets ◽  
Image Processing Techniques ◽  
New Methods ◽  
Spun Yarns ◽  
Processing Techniques ◽  
First Time

Previously, to evaluate the abrasion of spun yarns, ASTM standard D1379-64 (1970) was applied and valid until 1975. After that, much research work has been carried out to study the abrasion resistance of yarns by using different methods. Recently, new methods based on image processing techniques have been developed. In this research, first, to calculate the abrasion indexes for an image of yarns that are wrapped side by side, the inputs for a back propagation neural network are provided and abrasion destruction indexes are the output. The training of the net is done with data from model images. Moreover, the network has been tested with those model images. To design the model images, attempts are made to simulate various types of defects which are made by abrasion on the body of yarn. After that, groups of spun and filament yarns are tested with both a standard and the new intelligent method and the results are compared. The results prove that trained neural nets have the ability to evaluate the images of yarns trained to the net before; in addition, they can evaluate the images which are inserted into the net for the first time.

scholarly journals Skin cancer detection and stage prediction using image processing techniques

2018 ◽  
Vol 7 (1.8) ◽  
pp. 204 ◽  
Author(s):  
Sheeju Diana ◽  
Ramamurthy B
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Noise Removal ◽  
The Body ◽  
Back Propagation Algorithm ◽  
Environmental Threats ◽  
Image Processing Techniques ◽  
Processing Techniques

Skin cancer is one of the perilous forms of cancer that most recently occurred in preceding and in recent years as well. Early detection of skin cancer is curable and it eliminates the cost that is spent on the advanced treatment. Skin cancer mainly occurs due to exposure to sun’s ultraviolet radiation and other environmental threats. It can be categorized into, Melanoma and Non-Melanoma. Melanoma is dangerous one. Once it is occurred it starts spreading across other parts of the body if not treated in the early stages. Non-Melanoma is a static cancer which does not affect the normal cells of the skin. This paper aims to develop an application to detect skin cancer and stage prediction using Image Processing Techniques. Stage is predicted, so that the treatment for the same is done without any delay. Skin cancer affected image is taken as input and various preprocessing techniques is applied for the same. The Preprocessing Techniques such as Noise Removal is applied on the image to filter out the noise. Filtered image is enhanced using Histogram Equalization and image is segmented to extract the affected portion. The Area, Perimeter and Eccentricity values are calculated for the affected portion of the skin. The values are then fed into the Neural Networks using Back Propagation algorithm in order to predict the Stage and type of the Skin cancer.

Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

scholarly journals An Intelligent Baby Monitor with Automatic Sleeping Posture Detection and Notification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 290-306
Author(s):  
Tareq Khan
Keyword(s):  
Language Translation ◽  
Video Data ◽  
The Body ◽  
Night Vision ◽  
Monitoring Device ◽  
Peace Of Mind ◽  
Image Processing Techniques ◽  
Pose Detection ◽  
Processing Techniques ◽  
Monitoring Devices

Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

scholarly journals GENOTOXIC AND CARCINOGENIC STUDIES OF NORGESTREL IN DROSOPHILA MELANOGASTER

2018 ◽  
Vol 11 (10) ◽  
pp. 372
Author(s):  
Abou-eisha A ◽  
Adel E El-din
Keyword(s):  
Somatic Mutation ◽  
Cellular Proliferation ◽  
Research Work ◽  
The Body ◽  
The Other ◽  
Direct Stimulation ◽  
Carcinogenic Activity ◽  
Genetic Examination ◽  
First Time

Objective: The aim of this study was to investigate, for the first time, the possible in vivo genotoxic and carcinogenic activity associated with exposure to norgestrel (NGT) drug through employing the very recently established and adjusted genotoxic and tumorigenic methods in Drosophila melanogaster.Methods: Two in vivo genotoxic test systems were used; one detects the somatic mutation and recombination effects (somatic mutation and recombination test [SMART] wing-spot test) and the other detects the primary DNA damage (the comet test) in the body cells of D. melanogaster. On the other hand, the warts (wts)-based SMART assay is a vital genetic examination in Drosophila used to identify and characterize cancer potential of compounds.Results: Four experimental doses of NGT were used (ranging from 0.24 μM to 16 μM). NGT was found to be non-genotoxic at all tested concentrations even at the highest dose level 16 μM and failed to increase the frequency of tumors in the somatic cells of D. melanogaster.Conclusion: Our results strengthen the hypothesis that steroidal drugs might act through a non-genotoxic carcinogen mechanism where the carcinogenic properties occur by direct stimulation of cellular proliferation through a steroid receptor-mediated mechanism. In addition, the results obtained in this research work may contribute to highlighting the importance of NGT as a potent neuroprotective antioxidant drug.

HUMAN FACE IMAGE PROCESSING TECHNIQUES

2001 ◽  
Vol 01 (02) ◽  
pp. 197-215 ◽  
Author(s):  
HONG YAN
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Facial Feature ◽  
Face Image ◽  
Computer Algorithms ◽  
Human Face ◽  
Feature Location ◽  
Image Processing Techniques ◽  
Face Image Processing ◽  
Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

scholarly journals Analysis of Buffer Zones of Limestone Mines Areas using Digital Image Processing Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5431-5437
Keyword(s):  
Image Processing ◽  
Land Use ◽  
Digital Image Processing ◽  
Digital Image ◽  
Land Use Changes ◽  
Research Work ◽  
Mining Activities ◽  
Buffer Zones ◽  
Image Processing Techniques ◽  
Processing Techniques

The economic growth of any country crucially depends on the mining activity of that country. The mining activities require huge land for the extraction of mineral from the earth. The recent government policy imposing the systematic mapping of the land use and land cover in and around the mines. In the present study, work, the analysis of land used and land covered was carried out at Malkapur limestone mines. This study discussed the brief mapping of the buffer zones buffer zones areas in by using digital image processing techniques. This research work demonstrated the changes happened in and around mines for the buffer radius of 1 km, 5 km and 10 km. In this study it was found that there were no significant changes observed in land use which intern implies that mining activities are not having any impact in land use changes. Further, in this study, not much variation was reported against the forest land and water bodies situated in and around the mines

scholarly journals Quantum-Assisted Retinal Drusen Detection Algorithm using Entropy-Based Image Processing Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 2251-2256
Keyword(s):  
Image Processing ◽  
Composite System ◽  
Detection Method ◽  
Research Work ◽  
Detection Algorithm ◽  
Image Processing Techniques ◽  
Sensitivity Specificity ◽  
Processing Techniques ◽  
Retinal Fundus ◽  
Retinal Drusen

Drusen identification is the fundamental operation in the automated diagnosis of eye diseases. Manual and automatic detection of the drusen in the retinal fundus images has been developed recently in the classical manner only. This work provides the quantum-based retinal drusen detection method using entropy-based image processing techniques. This algorithm is the composite system of two channels, classical and quantum channels for the preprocessing and drusen detection respectively. This research work has been evaluated with the databases of DRIVE, STARE, MESSIDOR, E-Optha-Ex and ONH-Hunter. This quantum-based approach will be analyzed with the results of the existing classical methods and proves its efficiency from the calculations of sensitivity, specificity, accuracy and execution time.

scholarly journals Computer Aided Diagnosis System To Distinguish Adhd From Similar Behavioral Disorders

2018 ◽  
Vol 11 (2) ◽  
pp. 1135-1141
Author(s):  
Siba Shankar Beriha
Keyword(s):  
Fractal Dimension ◽  
Behavioral Disorders ◽  
Research Work ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Computer Aided Diagnosis ◽  
Signal Features ◽  
Computer Aided ◽  
Anxiety Depression ◽  
Aided Diagnosis

ADHD is one of the most prevalent psychiatric disorder of childhood, characterized by inattention and distractibility, with or without accompanying hyperactivity. The main aim of this research work is to develop a Computer Aided Diagnosis (CAD) technique with minimal steps that can differentiate the ADHD children from the other similar children behavioral disorders such as anxiety, depression and conduct disorder based on the Electroencephalogram (EEG) signal features and symptoms. The proposed technique is based on soft computing and bio inspired computing algorithms. Four non-linear features are extracted from the EEG such as Higuchi fractal dimension, Katz fractal dimension, Sevick fractal dimension and Lyapunov exponent and 14 symptoms which are most important in differentiation are extracted by experts in the field of psychiatry. Particle Swarm Optimization (PSO) tuned Back Propagation Neural Network (BPNN) and PSO tuned Radial Basis Function (RBF) employed as a classifier. By investigating these integrated features, we obtained good classification accuracy. Simulation results suggest that the proposed technique offer high potential in the diagnosis of ADHD and may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.

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