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 Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8455
Author(s):  
Diana Queirós Pokee ◽  
Carina Barbosa Pereira ◽  
Lucas Mösch ◽  
Andreas Follmann ◽  
Michael Czaplik
Keyword(s):  
Infrared Imaging ◽  
Longitudinal Axis ◽  
Video Data ◽  
Simulated Patients ◽  
Probable Case ◽  
Image Processing Techniques ◽  
Thermal Data ◽  
Thermal Cameras ◽  
Processing Techniques ◽  
Triage Algorithm

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

scholarly journals Detection of Brain Tumor using Image Processing Techniques

2019 ◽  
Vol 8 (5S3) ◽  
pp. 501-504
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
The Body ◽  
Body Parts ◽  
X Ray ◽  
Image Processing Techniques ◽  
Stage Process ◽  
Detection And Diagnosis ◽  
Processing Techniques ◽  
The Brain

Image processing in biomedical field is being increasingly used for the detection and diagnosis of various abnormalities in the body parts. The detection of brain tumours using image processing on MRI images is one such field where better results are obtained as comparative to CT-scan and x-ray. Prior detection of the brain tumour is desirable and possible with the help of machine learning and image processing techniques. These techniques detect even a small abnormality in the human brain following a four-stage process which includes pre-processing, segmentation, feature extraction and optimization. Different parameters such as accuracy, PSNR, MSE are calculated to find out the efficiency of process and to compare it with other methods. This paper reviews about various different approaches which are used to detect the brain tumor using image processing techniques.

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.

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 Mendeteksi Kondisi Organ Liver Melalui Citra Iris Menggunakan Teknik Pengolahan Citra Digital (Detecting the Condition of Liver Organ Through Iris Image Using Digital Image Processing Techniques)

2019 ◽  
Vol 3 (1) ◽  
pp. 138 ◽  
Author(s):  
Maxima Ari Saktiono
Keyword(s):  
Image Processing ◽  
Processing System ◽  
Region Of Interest ◽  
Video Camera ◽  
Visual Basic ◽  
The Body ◽  
Physical Test ◽  
Image Processing Techniques ◽  
Sobel Operator ◽  
Processing Techniques

Besides Physical test normally, early detection on the condition of the body by using the image processing of iris is an alternative method to observe the health of human’s body, especially the internal organ of the body. This paper uses Dr. Bernard Jensen’s chart of iris as reference, in which part and how deep is the damage happens in the tissue of iris. Organ disorder is represented by the form of broken tissue of iris. The broken tissue usually seems to be like a hole in certain area in the iris. In this paper, the instrumentation for data mining uses video camera and the software that will be developed uses Visual Basic on image processing programming. In the image of eye, the region of interest is only on the iris, and it will be grabbed by using circle and line equations.The area of Liver organ lies on 07.15 – 07.45 in the third Quadran. After wards, this slice of image is prepared for image processing system. The method that is going to be used in this paper is grey level, enhancing and sobel operator. Then, the output of the system will be compared with physical test to measure the precision on detecting the problem on Liver organ.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques
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