Готові списки джерел за темами / Human body partys detecting

Добірка наукової літератури з теми "Human body partys detecting"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Human body partys detecting"

Johan, Nurul Fatiha, Yasir Mohd Mustafah, and Nahrul Khair Alang Md Rashid. "Human Body Parts Detection Using YCbCr Color Space." Applied Mechanics and Materials 393 (September 2013): 556–60. http://dx.doi.org/10.4028/www.scientific.net/amm.393.556.

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Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

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Miao, Ying, Danyang Shao, and Zhimin Yan. "Privacy-Oriented Successive Approximation Image Position Follower Processing." Complexity 2021 (June 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6853809.

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In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.

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BARANWAL, MAYANK, M. TAHIR KHAN, and CLARENCE W. DE SILVA. "ABNORMAL MOTION DETECTION IN REAL TIME USING VIDEO SURVEILLANCE AND BODY SENSORS." International Journal of Information Acquisition 08, no. 02 (June 2011): 103–16. http://dx.doi.org/10.1142/s0219878911002379.

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This paper presents a method for detecting abnormal motion in real time using a computer vision system. The method is based on the modeling of human body image, which takes into account both orientation and velocity of prominent body parts. A comparative study is made of this method with other existing algorithms based on optical flow and the use of accelerometer body sensors. From the real time experiments conducted in the present work, the developed method is found to be efficient in characterizing human motion and classifying it into basic types such as falling, sitting, and walking. The method uses a Radial Basis Function Network (RBFN) to compute the severity coefficient associated with the type of motion, based on experience. The paper evaluates the various methods and incorporates the advantages of other methods in order to develop a more reliable system for abnormal motion detection.

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Hong, Sungjin, and Yejin Kim. "Dynamic Pose Estimation Using Multiple RGB-D Cameras." Sensors 18, no. 11 (November 10, 2018): 3865. http://dx.doi.org/10.3390/s18113865.

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Human poses are difficult to estimate due to the complicated body structure and the self-occlusion problem. In this paper, we introduce a marker-less system for human pose estimation by detecting and tracking key body parts, namely the head, hands, and feet. Given color and depth images captured by multiple red, green, blue, and depth (RGB-D) cameras, our system constructs a graph model with segmented regions from each camera and detects the key body parts as a set of extreme points based on accumulative geodesic distances in the graph. During the search process, local detection using a supervised learning model is utilized to match local body features. A final set of extreme points is selected with a voting scheme and tracked with physical constraints from the unified data received from the multiple cameras. During the tracking process, a Kalman filter-based method is introduced to reduce positional noises and to recover from a failure of tracking extremes. Our system shows an average of 87% accuracy against the commercial system, which outperforms the previous multi-Kinects system, and can be applied to recognize a human action or to synthesize a motion sequence from a few key poses using a small set of extremes as input data.

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Ocepek, Marko, Anja Žnidar, Miha Lavrič, Dejan Škorjanc, and Inger Lise Andersen. "DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming." Agriculture 12, no. 1 (December 21, 2021): 2. http://dx.doi.org/10.3390/agriculture12010002.

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The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

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Myllylä, Teemu, Vesa Korhonen, Erkki Vihriälä, Hannu Sorvoja, Tuija Hiltunen, Osmo Tervonen, and Vesa Kiviniemi. "Human Heart Pulse Wave Responses Measured Simultaneously at Several Sensor Placements by Two MR-Compatible Fibre Optic Methods." Journal of Sensors 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/769613.

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This paper presents experimental measurements conducted using two noninvasive fibre optic methods for detecting heart pulse waves in the human body. Both methods can be used in conjunction with magnetic resonance imaging (MRI). For comparison, the paper also performs an MRI-compatible electrocardiogram (ECG) measurement. By the simultaneous use of different measurement methods, the propagation of pressure waves generated by each heart pulse can be sensed extensively in different areas of the human body and at different depths, for example, on the chest and forehead and at the fingertip. An accurate determination of a pulse wave allows calculating the pulse transit time (PTT) of a particular heart pulse in different parts of the human body. This result can then be used to estimate the pulse wave velocity of blood flow in different places. Both measurement methods are realized using magnetic resonance-compatible fibres, which makes the methods applicable to the MRI environment. One of the developed sensors is an extraordinary accelerometer sensor, while the other one is a more common sensor based on photoplethysmography. All measurements, involving several test patients, were performed both inside and outside an MRI room. Measurements inside the MRI room were conducted using a 3-Tesla strength closed MRI scanner in the Department of Diagnostic Radiology at the Oulu University Hospital.

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Mustafa, Rashed, Yang Min, and Dingju Zhu. "Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/753860.

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Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

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Gan, Lu, Aobo Geng, Ying Wu, Linjie Wang, Xingyu Fang, Lijie Xu, and Changtong Mei. "Antibacterial, Flexible, and Conductive Membrane Based on MWCNTs/Ag Coated Electro-Spun PLA Nanofibrous Scaffolds as Wearable Fabric for Body Motion Sensing." Polymers 12, no. 1 (January 5, 2020): 120. http://dx.doi.org/10.3390/polym12010120.

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In the present study, flexible and conductive nanofiber membranes were prepared by coating PLA nanofibrous scaffolds with carbon nanotubes and silver nanoparticles. The morphology and structure of the prepared membrane was characterized, as well as its mechanical properties, electrical sensing behavior during consecutive stretching-releasing cycles and human motion detecting performance. Furthermore, the antibacterial properties of the membrane was also investigated. Due to the synergistic and interconnected three-dimensional (3D) conductive networks, formed by carbon nanotubes and silver nanoparticles, the membrane exhibited repeatable and durable strain-dependent sensitivity. Further, the prepared membrane could accurately detect the motions of different body parts. Accompanied with promising antibacterial properties and washing fastness, the prepared flexible and conductive membrane provides great application potential as a wearable fabric for real-time body motion sensing.

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Adolf, Jindrich, Jaromir Dolezal, Patrik Kutilek, Jan Hejda, and Lenka Lhotska. "Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset." Applied Sciences 12, no. 2 (January 13, 2022): 799. http://dx.doi.org/10.3390/app12020799.

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In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.

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Jang, Seok-Woo, and Gye-Young Kim. "Learning-Based Detection of Harmful Data in Mobile Devices." Mobile Information Systems 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3919134.

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The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

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Дисертації з теми "Human body partys detecting"

Поліщук, Михайло Олегович. "Модифікований метод виявлення частин тіла людини на зображеннях". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/26685.

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Актуальність теми. Комп’ютерний зір – це сучасний напрямок технологій який має широкий потенціал для використання у великій кількості сфер діяльності. Перспективним напрямом є саме розпізнання людського тіла та його частин. Такі технології могли б широко використовуватися у медицині, системах відеоспостереження або у кіноіндустрії, щоб не використовувати дорогі прилади захоплення руху. Об’єкт дослідження – методи розпізнавання об’єктів. Предмет дослідження – пошук шляхів для модифікації розпізнавання частин людського тіла у згортковій неронній мережі, огляд та аналіз всіх кроків згорткової нейронної мережі. Мета роботи: модифікація методу розпізнавання частин тіла людини для підвищення ефективності обробки темних зображень з низькою деталізацією. Наукова новизна полягає у створенні методу який дозволяє ефективніше розпізнавати образи частин тіла людини на зображеннях з поганою деталізацією у порівнянні з існуючими аналогами розпізнавання. Практична цінність одержаних в роботі результатів можуть бути використані для вибору шляхів модифікації існуючих методів розпізнавання частин тіла людини. Апробація роботи. Основні результати роботи були представлені та обговорювались на XI науковій конференції молодих вчених «Прикладна математика та комп’ютинг» ПМК-2018-2 (Київ, 14-16 листопада 2018 р.), а також на V Міжнародній науково-технічній конференції «Сучасні методи, інформаційне, програмне та технічне забезпечення систем керування організаційно-технічними та технологічними комплексами» (Київ, 22-23 листопада 2018 р.). Структура та обсяг роботи. Магістерська дисертація складається з вступу, трьох розділів та висновків. У вступі подано загальну характеристику роботи, описано сучасні методи розпізнавання і перспектив розвитку комп’ютерного зору. У першому розділі наведено загальний огляд методів розпізнавання за допомогою нейронних мереж, їх основні характеристики, архітектури та особливості, а також обгрунтовано чому був обраний метод на основі згорткових мереж. У другому розділі наведено основні недоліки розпізнавання частин людського тіла, можливі модифікації методу розпізнавання. У третьому розділі проаналізовано результати тестів до і після модифікацій. У висновках представлені результати проведеної роботи. Робота представлена на 86 аркушах, містить посилання на список використаних літературних джерел.
Actuality of theme. Computer vision is a modern technology trend that has wide potential for use in a wide range of fields of activity. A promising direction is the recognition of the human body and its parts. Such technologies could be widely used in medicine, video surveillance systems or in the cinema industry, in order not to use expensive motion capture devices. Object of research - methods of object recognition. The subject of the study is the search for ways to modify the recognition of parts of the human body in the convolutional neural network, review and analysis of all steps of the convolutional neural network. Purpose: to modify the method of recognizing human body parts to improve the processing efficiency of dark images with low detail. The scientific novelty consists in the creation of a method that allows more efficient recognition of images of parts of the human body in images with poor detail compared with existing analogues of recognition. The practical value of the results obtained in the work can be used to select ways to modify existing methods for recognizing human body parts. Test work. The main results of the work were presented and discussed at the XI Scientific Conference of Young Scientists "Applied Mathematics and Computer", PMK-2018-2 (Kyiv, November 14-16, 2018), as well as at the V International Scientific and Technical Conference "Modern Methods , information, software and technical support of control systems for organizational, technical and technological complexes "(Kyiv, November 22-23, 2018). Structure and scope of work. The master's dissertation consists of an introduction, three sections and conclusions. The introduction gives a general description of the work, describes the modern methods of recognition and the prospects for the development of computer vision. The first section provides a general overview of methods for recognizing with neural networks, their main characteristics, architecture and features, and also why the method was chosen. The second section presents the main disadvantages of recognizing parts of the human body, possible modifications to the method of recognition. The third section analyzes the test results before and after the modifications. The conclusions are the results of the work. The work is presented on 86 pages, contains a reference to the list of used literary sources.
Актуальность темы. Компьютерное зрение - это современное направление технологий который имеет широкий потенциал для использования в большом количестве сфер деятельности. Перспективным направлением является именно распознавание человеческого тела и его частей. Такие технологии могли бы широко использоваться в медицине, системах видеонаблюдения или в киноиндустрии, чтобы не использовать дорогие приборы захвата движения. Объект исследования - методы распознавания объектов. Предмет исследования - поиск путей для модификации распознавания частей человеческого тела в згорткових неронний сети, обзор и анализ всех шагов згорткових нейронной сети. Цель работы: модификация метода распознавания частей тела человека для повышения эффективности обработки темных изображений с низкой детализацией. Научная новизна заключается в создании метода который позволяет эффективно распознавать образы частей тела человека на изображениях с плохой детализацией по сравнению с существующими аналогами распознавания. Практическая ценность полученных в работе результатов могут быть использованы для выбора путей модификации существующих методов распознавания частей тела человека. Апробация работы. Основные результаты работы были представлены и обсуждались на XI научной конференции молодых ученых «Прикладная математика и компьютинг» ПМК-2018-2 (Киев, 14-16 ноября 2018), а также на V Международной научно-технической конференции «Современные методы, информационное, программное и техническое обеспечение систем управления организационно-техническими и технологическими комплексами» (Киев, 22-23 ноября 2018). Структура и объем работы. Магистерская диссертация состоит из введения, трех глав и выводов. Во введении представлена общая характеристика работы, описаны современные методы распознавания и перспектив развития компьютерного зрения. В первой главе приведен общий обзор методов распознавания с помощью нейронных сетей, их основные характеристики, архитектуры и особенности, а также обоснованно почему был выбран метод на основе сверточный сетей. Во втором разделе приведены основные недостатки распознавания частей человеческого тела, возможные модификации метода распознавания. В третьем разделе проанализированы результаты тестов до и после модификаций. В выводах представлены результаты проведенной работы. Работа представлена на 86 листах, содержит ссылки на список использованных литературных источников.

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Zhan, Wenjie, and Maowei Zheng. "From Body Parts Responses to Underwater Human Detection: A Deep Learning Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20099.

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Context. Underwater human detection has been an important problem in computer vision areas. Body part-based models could gain good performance in on-land human detection with occlusion existing scenarios. This thesis explores the feasibility of human body parts detection in underwater environment. Objectives. This thesis aims to build a DNN-based underwater human body part detector for human body part detection task. Three body part detectors implemented with different DNN-based models (Faster R-CNN, SSD and YOLO) are built and compared over underwater human body part detection task. Methods. In this thesis, experiments are used as research methods. Three DNN-based models which are regarded as the independent variables in the experiment is trained, tested and evaluated. And the detection results of detector based on the three different models are dependent variables. Finally the detection performance calculated on the result for each detector is compared. Results. Underwater Body part detector based on Faster R-CNN provides the best detection performance on the body part detection task in terms of mAP, and YOLOv2 achieves the fastest detection speed but it has the smallest mAP value. In addition, SSD model has both decent detection performance and also detection speed. Conclusions. Underwater Body part detector based on Faster R-CNN, SSD, and YOLO could gain good performance over underwater human body part detection task. Building an underwater body part detector via deep learning method is feasible.

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Twyman, Nathan W. "Automated Human Screening for Detecting Concealed Knowledge." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222874.

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Screening individuals for concealed knowledge has traditionally been the purview of professional interrogators investigating a crime. But the ability to detect when a person is hiding important information would be of high value to many other fields and functions. This dissertation proposes design principles for and reports on an implementation and empirical evaluation of a non-invasive, automated system for human screening. The screening system design (termed an automated screening kiosk or ASK) is patterned after a standard interviewing method called the Concealed Information Test (CIT), which is built on theories explaining psychophysiological and behavioral effects of human orienting and defensive responses. As part of testing the ASK proof of concept, I propose and empirically examine alternative indicators of concealed knowledge in a CIT. Specifically, I propose kinesic rigidity as a viable cue, propose and instantiate an automated method for capturing rigidity, and test its viability using a traditional CIT experiment. I also examine oculomotor behavior using a mock security screening experiment using an ASK system design. Participants in this second experiment packed a fake improvised explosive device (IED) in a bag and were screened by an ASK system. Results indicate that the ASK design, if implemented within a highly controlled framework such as the CIT, has potential to overcome barriers to more widespread application of concealed knowledge testing in government and business settings.

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Wong, Rick. "Eye “R” Glasses: Development of an Infrared Sensor System for Detecting the Human Body." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1019.

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Throughout the years, sensors have been an integral part of automation, alert, and medical systems. Many of these systems measure physiological characteristics of the human body to alert themselves of their current conditions. Drowsy driver systems, for instance, measure the eyes and facial movements with a camera to determine if the driver is falling asleep at the wheel. Electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) employ electrodes on the human body to measure electrical activity of a patient’s REM sleep cycle patterns. Pulse oximeters use optical light through a process called photoplethysmography (PPG) to measure heart rate. As diverse as these all may be, this thesis attempts to prove infrared technology as a single, resourceful, and inexpensive method for implementing all the aforementioned systems. This thesis specifically explores the development of Silicon Laboratories’ Si1143 Infrared Proximity/Ambient Light Sensor into a pair of eye tracking/heart rate detecting glasses. Through the use of a LabVIEW interface, a novel algorithmic solution is also presented to classify the eye movements and detect the heart rate signal. The results from the tests and calculations show that the Si1143 sensor can detect eye movements using only a 52μW of power. The novel algorithm can also classify the blinking motions robustly, but the algorithm starts to fail when additional motions are added. The results also show that the Si1143 sensor can detect heart rate using Reflection PPG method, but imprecise placement of the sensor on the glasses will render the measurement useless. This thesis concludes that Si1143 sensor is sensitive enough to track the human eye and measure the heart rate, but further work is required to make it robust.

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Книги з теми "Human body partys detecting"

Streicher, John P. The pebble in the shoe: Detecting the causes of distress and pain in the human body. Emumclaw, WA: WinePress Pub., 2000.

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Youssef, Samuel J., and John A. Elefteriades. Pathophysiology, diagnosis, and management of aortic dissection. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0148.

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Aortic dissection represents a splitting apart of the layers of the aortic wall, with blood under pressure entering the dissection plane and propagating for long distances along the aorta. The pain is said to be the most severe that a human being can experience. Pain is felt substernally with ascending dissection and between the shoulder blades for descending dissection. A high degree of clinical suspicion is essential in order for the diagnosis not to be missed. Because the dissection process can impair any branch of the aorta, the patient may present with symptoms related to any organ in the body. D-dimer is 100% sensitive at detecting aortic dissection (but non-specific). The ‘Triple Rule-Out CT Scan’ can confirm the clinical suspicion of aortic dissection, while at the same time ruling-out the other two cardiac conditions that can take a patient’s life acutely. Ascending dissection (Type A) is a surgical emergency because of the likelihood of intra-pericardial rupture. Descending dissection (Type B) is usually treated medically (with ‘anti-impulse’ therapy with β‎-blockers and afterload reducers). This condition is highly litigated and lethal if missed on initial presentation. Using D-dimer and liberal imaging will prevent mis-diagnosis and save lives.

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Частини книг з теми "Human body partys detecting"

Kohlschütter, Tomáš, and Pavel Herout. "Automatic Human Body Parts Detection in a 2D Anthropometric System." In Advances in Visual Computing, 536–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_53.

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Lao, Weilun, Jungong Han, and Peter H. N. de With. "Fast Detection and Modeling of Human-Body Parts from Monocular Video." In Articulated Motion and Deformable Objects, 380–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70517-8_37.

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Chakraborty, Bhaskar, Marco Pedersoli, and Jordi Gonzàlez. "View-Invariant Human Action Detection Using Component-Wise HMM of Body Parts." In Articulated Motion and Deformable Objects, 208–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70517-8_20.

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Lakshman, K., Siddharth B. Dabhade, Mrudul Behare, S. N. Deshmukh, and Ranjan Maheshwari. "Medical Infrared Image Analysis for Detecting Malignant Regions of the Human Body." In International Conference on Mobile Computing and Sustainable Informatics, 643–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49795-8_61.

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Yan, Zhicong, Shuai Feng, Fangqi Li, Zhengwu Xu, and Shenghong Li. "A Multi-view Deep Learning Approach for Detecting Threats on 3D Human Body." In Lecture Notes in Electrical Engineering, 286–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6508-9_36.

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"Anthropometric Algorithm Used for Automatic Body Dimensions and Skin Color Detection Aimed for Homeland Security Systems." In Advances in Multimedia and Interactive Technologies, 141–56. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4896-8.ch011.

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Automatic body detection and identification of a person is one of the most recent research topics that has gained a lot of attention from researchers. Automated systems that will store human biometrics along with the personal information can be of significant assistance in investigations and security issues. Biometrics represents unique aspects of the body that are measurable, robust, distinctive, physical characteristic, or personal traits of an individual by which a person can be identified. Biometric surveillance systems measure and analyze human physical and behavioral characteristics for identification purposes. A method of body measurement can be used for human identification, with the means of using a static camera. Body measurement calculation based on similar triangles is proposed. The focal length of the camera is a very important aspect of the method. This process can provide the means for obtained image segmentation, measurement of the body parts of the subject, and finally, these measurements can be used for identification of the person.

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"Detecting human error symptom of body movement in monotonous work." In Industrial Engineering and Management Science, 91–96. CRC Press, 2014. http://dx.doi.org/10.1201/b17546-22.

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Khan, Muhammad Hassan, and Marcin Grzegorzek. "Vojta-Therapy." In Research Anthology on Rehabilitation Practices and Therapy, 383–98. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3432-8.ch020.

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This paper proposed a novel computer vision-based framework to recognize the accurate movements of a patient during the Vojta-therapy. Vojta-therapy is a useful technique for the physical and mental impairments in humans. During the therapy, a specific stimulation is given to the patients to cause the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes available the previously blocked connections between the spinal cord and brain, and after a few sessions, patients can perform these movements without any external stimulation. In this paper the authors propose an automatic method for patient detection and recognition of specific movements in his/her various body parts during the therapy process, using Microsoft Kinect camera. The proposed method works in three steps. In the first step, a robust template matching based algorithm is exploited for patient's detection using his/her head location. Second, several features are computed to capture the movements of different body parts during the therapy process. Third, in the classification stage, a multi-class support vector machine (mSVM) is used to classify the accurate movements of patient. The classification results ultimately reveal the correctness of the given treatment. The proposed algorithm is evaluated on the authors' challenging dataset, which was collected in a children hospital. The detection and classification results show that the proposed method is highly useful to recognize the correct movement pattern either in hospital or in-home therapy systems.

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Nikumbh, Deepti Deepak, Shahzia Sayyad, Rupesh R. Joshi, Karan Sanjeev Dubey, Deep V. Mehta, and Davleen Kaur Matta. "Applied Intelligence for Medical Diagnosing." In Advances in Healthcare Information Systems and Administration, 44–79. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7709-7.ch004.

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Medical imaging is associated with different techniques and processes that are used to create visual representations of internal parts of the human body for diagnostic and treatment purposes within digital health. Machine learning plays a crucial role in the medical imaging field including analysis of various medical images, computer-aided diagnosis or detection, image retrieval, gene data analysis, image reconstruction, and organ segmentation. The machine learning algorithm framework recognizes the best combination of the medical image features for categorizing the medical images or processing some metric for the given image area. The images obtained are then processed using algorithms such as K-means, support vector machines, decision trees, neural networks, and deep learning techniques.

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Morozov, Alexei Alexandrovich, Olga Sergeevna Sushkova, and Alexander Fedorovich Polupanov. "Object-Oriented Logic Programming of Intelligent Visual Surveillance for Human Anomalous Behavior Detection." In Optoelectronics in Machine Vision-Based Theories and Applications, 134–87. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5751-7.ch006.

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The idea of the logic programming-based approach to the intelligent visual surveillance is in usage of logical rules for description and analysis of people behavior. New prospects in logic programming of the intelligent visual surveillance are connected with the usage of 3D machine vision methods and adaptation of the multi-agent approach to the intelligent visual surveillance. The main advantage of usage of 3D vision instead of the conventional 2D vision is that the first one can provide essentially more complete information about the video scene. The availability of exact information about the coordinates of the parts of the body and scene geometry provided by means of 3D vision is a key to the automation of behavior analysis, recognition, and understanding. This chapter supplies the first systematic and complete description of the method of object-oriented logic programming of the intelligent visual surveillance, special software implementing this method, and new trends in the research area linked with the usage of novel 3D data acquisition equipment.

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Тези доповідей конференцій з теми "Human body partys detecting"

Ren, Hailin, Anil Kumar, Xinran Wang, and Pinhas Ben-Tzvi. "Parallel Deep Learning Ensembles for Human Pose Estimation." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9007.

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This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network. The network presented in this approach consists of two sequentially connected stages of 13 parallel CNN ensembles, where each ensemble is trained to detect one specific kind of linkage of the human skeleton structure. After detecting all skeleton linkages, a voting score-based post-processing algorithm assembles the individual linkages to form a complete human structure. This algorithm exploits human structural heuristics while assembling skeleton links and searches only for adjacent link pairs around the expected common joint area. The use of structural heuristics in the presented approach heavily simplifies the post-processing computations. Furthermore, the parallel architecture of the presented network enables mutually independent computing nodes to be efficiently deployed on parallel computing devices such as GPUs for computationally efficient training. The proposed network has been trained and tested on the COCO 2017 person-keypoints dataset and delivers pose estimation performance matching state-of-art networks. The parallel ensembles architecture improves its adaptability in applications aimed at identifying only specific body parts while saving computational resources.

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Jalal, Ahmad, Amir Nadeem, and Satoshi Bobasu. "Human Body Parts Estimation and Detection for Physical Sports Movements." In 2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE). IEEE, 2019. http://dx.doi.org/10.1109/c-code.2019.8680993.

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Chakraborty, Bhaskar, Ognjen Rudovic, and Jordi Gonzalez. "View-invariant human-body detection with extension to human action recognition using component-wise HMM of body parts." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813302.

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Shetty, Devdas, Suhash Ghosh, Claudio Campana, and Mustafa Atalay. "A New Precision Non-Contact Laser-Based Hybrid Measurement Methodology." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63954.

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Precise and accurate manufacturing became an obligation in aerospace industry in last decades. Uniformity of turbine blades, nozzle geometries, gaps, diameter changes and misalignment issues in turbine assemblies have to be inspected carefully in terms of quality and exactitude. Like broadly used aluminum and titanium based materials, ceramics and special coated composites are also used in aerospace applications. A wide selection of measurement methods used is based on intensity sensing and range imaging. With the recent development in advanced laser techniques, new methods that involve non contact measurement methodologies are being investigated by many industries. In addition to their accuracy and precision, speed of measurement and compactness of such systems are also of high significance. In this paper, a hybrid approach consisting of laser based triangulation, photogrammetry and edge detection techniques has been investigated to measure inner surfaces of parts that have limited access, especially where human presence is impossible. The system is capable of detecting and measuring misalignments, gaps, inclinations as well as surface variations such as cracks and dents. The system employs the accuracy and speed of measurement of triangulation systems and combines these with the mobility and cost effectiveness of photogrammetry and edge detection techniques. In addition to gap and alignment offset inspections, the methodology and the instrument enables angle measurements, detailed surface texture examinations and other inspections needed to be done inside assemblies with narrow openings, with its compact body. Additionally, a comprehensive experimental study has been conducted to show that two different edge detection methods, namely, the “Simple Edge Tool” and “Straight Edge (Rake) Tool” can be used with great accuracy and precision for such measurement purposes. With this system, any surface, whether they have a reflectance or not, can be scrutinized.

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Chen, Hai-Wen, and Mike McGurr. "Improved color and intensity patch segmentation for human full-body and body-parts detection and tracking." In 2014 International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2014. http://dx.doi.org/10.1109/avss.2014.6918695.

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Chen, Hai-Wen, and Mike McGurr. "Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition." In SPIE Defense + Security, edited by Firooz A. Sadjadi and Abhijit Mahalanobis. SPIE, 2016. http://dx.doi.org/10.1117/12.2224319.

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Aardal, Oyvind, Sverre Brovoll, Yoann Paichard, Tor Berger, Tor Sverre Lande, and Svein-Erik Hamran. "Detecting changes in the human heartbeat with on-body radar." In 2013 IEEE Radar Conference (RadarCon). IEEE, 2013. http://dx.doi.org/10.1109/radar.2013.6586027.

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Nakashima, Yuuki, Joo Kooi Tan, Seiji Ishikawa, and Takashi Morie. "Detecting a human body direction using a feature selection method." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670329.

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Zaman, S. M. Tahsin, Subrata Kumer Paul, Rakhi Rani Paul, and Md Ekramul Hamid. "Detecting Diabetes in Human Body using Different Machine Learning Techniques." In 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). IEEE, 2021. http://dx.doi.org/10.1109/ic4me253898.2021.9768501.

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Nadeem, Amir, Ahmad Jalal, and Kibum Kim. "Human Actions Tracking and Recognition Based on Body Parts Detection via Artificial Neural Network." In 2020 3rd International Conference on Advancements in Computational Sciences (ICACS). IEEE, 2020. http://dx.doi.org/10.1109/icacs47775.2020.9055951.

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