scholarly journals Using Depth Cameras for Recognition and Segmentation of Hand Gestures

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Khalid Twarish Alhamazani ◽  
Jalawi Alshudukhi ◽  
Talal Saad Alharbi ◽  
Saud Aljaloud ◽  
Zelalem Meraf
Keyword(s):  
Infrared Camera ◽  
Microsoft Kinect ◽  
Machine Learning Techniques ◽  
Leap Motion ◽  
Hand Gestures ◽  
Depth Cameras ◽  
User Adoption ◽  
Lighting Conditions ◽  
Technological Advances ◽  
Interface Devices

In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

A Real-Time Compressive Tracking System for Amphibious Spherical Robots

2018 ◽  
pp. 390-409
Author(s):  
Shuxiang Guo ◽  
Liwei Shi
Keyword(s):  
High Speed ◽  
Tracking System ◽  
Microsoft Kinect ◽  
Depth Cameras ◽  
Compressive Tracking ◽  
Depth Images ◽  
Working Environments ◽  
Robotic Tracking ◽  
Prediction Mechanism ◽  
Amphibious Robot

Given the special working environments and application functions of the amphibious robot, an improved RGB-D visual tracking algorithm with dual trackers is proposed and implemented in this chapter. Compressive tracking (CT) was selected as the basis of the proposed algorithm to process colour images from a RGB-D camera, and a Kalman filter with a second-order motion model was added to the CT tracker to predict the state of the target, select candidate patches or samples, and reinforce the tracker's robustness to high-speed moving targets. In addition, a variance ratio features shift (VR-V) tracker with a Kalman prediction mechanism was adopted to process depth images from a RGB-D camera. A visible and infrared fusion mechanism or feedback strategy is introduced in the proposed algorithm to enhance its adaptability and robustness. To evaluate the effectiveness of the algorithm, Microsoft Kinect, which is a combination of colour and depth cameras, was adopted for use in a prototype of the robotic tracking system.

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

2020 ◽  
pp. 1096-1117
Author(s):  
Rodrigo Ibañez ◽  
Alvaro Soria ◽  
Alfredo Raul Teyseyre ◽  
Luis Berdun ◽  
Marcelo Ricardo Campo
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Gesture Recognition ◽  
Microsoft Kinect ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Natural Interaction ◽  
Computer Communication ◽  
Human Computer Interfaces ◽  
Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

scholarly journals A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guoliang Chen ◽  
Kaikai Ge
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Calculation Model ◽  
Opening Angle ◽  
Hand Gesture ◽  
Leap Motion ◽  
Recognition Method ◽  
Hand Gestures ◽  
Multiple Features

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

scholarly journals Comparison of Kinect and Leap Motion for Intraoperative Image Interaction

2020 ◽  
pp. 155335062094720
Author(s):  
Yuanyuan Feng ◽  
Uchenna A. Uchidiuno ◽  
Hamid R. Zahiri ◽  
Ivan George ◽  
Adrian E. Park ◽  
...  
Keyword(s):  
Intraoperative Imaging ◽  
Microsoft Kinect ◽  
Body Motion ◽  
Hand Tracking ◽  
Task Completion ◽  
Leap Motion ◽  
Hand Motion ◽  
Interaction Mechanisms ◽  
Touchless Interaction ◽  
Similar Accuracy

Background. Touchless interaction devices have increasingly garnered attention for intraoperative imaging interaction, but there are limited recommendations on which touchless interaction mechanisms should be implemented in the operating room. The objective of this study was to evaluate the efficiency, accuracy, and satisfaction of 2 current touchless interaction mechanisms—hand motion and body motion for intraoperative image interaction. Methods. We used the TedCas plugin for ClearCanvas DICOM viewer to display and manipulate CT images. Ten surgeons performed 5 image interaction tasks—step-through, pan, zoom, circle measure, and line measure—on the 3 input interaction devices—the Microsoft Kinect, the Leap Motion, and a mouse. Results. The Kinect shared similar accuracy with the Leap Motion for most of the tasks. But it had an increased error rate in the step-through task. The Leap Motion led to shorter task completion time than the Kinect and was preferred by the surgeons, especially for the measure tasks. Discussion. Our study suggests that hand tracking devices, such as the Leap Motion, should be used for intraoperative imagining manipulation tasks that require high precision.

scholarly journals The revolution of personalized psychiatry: will technology make it happen sooner?

2017 ◽  
Vol 48 (5) ◽  
pp. 705-713 ◽  
Author(s):  
G. Perna ◽  
M. Grassi ◽  
D. Caldirola ◽  
C. B. Nemeroff
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Time Data ◽  
New Approach ◽  
Technological Advances ◽  
Learning Techniques ◽  
Personalized Psychiatry ◽  
Smart Wearable

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

scholarly journals INTEGRATION OF KINECT AND LOW-COST GNSS FOR OUTDOOR NAVIGATION

2016 ◽  
Vol XLI-B5 ◽  
pp. 565-572 ◽  
Author(s):  
D. Pagliaria ◽  
L. Pinto ◽  
M. Reguzzoni ◽  
L. Rossi
Keyword(s):  
Low Cost ◽  
Integrated System ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Kinect Sensor ◽  
Gnss Receiver ◽  
Lighting Conditions ◽  
Satellite Visibility ◽  
New Generation ◽  
Microsoft Kinect Sensor

Since its launch on the market, Microsoft Kinect sensor has represented a great revolution in the field of low cost navigation, especially for indoor robotic applications. In fact, this system is endowed with a depth camera, as well as a visual RGB camera, at a cost of about 200$. The characteristics and the potentiality of the Kinect sensor have been widely studied for indoor applications. The second generation of this sensor has been announced to be capable of acquiring data even outdoors, under direct sunlight. The task of navigating passing from an indoor to an outdoor environment (and vice versa) is very demanding because the sensors that work properly in one environment are typically unsuitable in the other one. In this sense the Kinect could represent an interesting device allowing bridging the navigation solution between outdoor and indoor. In this work the accuracy and the field of application of the new generation of Kinect sensor have been tested outdoor, considering different lighting conditions and the reflective properties of the emitted ray on different materials. Moreover, an integrated system with a low cost GNSS receiver has been studied, with the aim of taking advantage of the GNSS positioning when the satellite visibility conditions are good enough. A kinematic test has been performed outdoor by using a Kinect sensor and a GNSS receiver and it is here presented.

scholarly journals SENSOR FUSION, GIS AND AI TECHNOLOGIES FOR DISASTER MANAGEMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1677-1683
Author(s):  
H. Kemper ◽  
G. Kemper
Keyword(s):  
Machine Learning ◽  
Disaster Management ◽  
Spatial Data ◽  
Integrated System ◽  
Theory And Practice ◽  
Machine Learning Techniques ◽  
Gis And Remote Sensing ◽  
Technological Advances ◽  
Learning Techniques ◽  
Important Benefit

Abstract. Modern Disaster Management Systems are based on several columns that combine theory and practice, software, and hardware being under technological advance. In all parts, spatial data is key in order to analyze existing structure, assist in risk assessment and update the information after a disaster incident. This paper focus on technological advances in several fields of spatial analysis putting together the advantages, limitations and technological aspects from well-known or even innovative methods, highlighting the huge potential of nowadays technologies for the field of Disaster Risk Management (DRM).A focus then is lying on GIS and Remote Sensing technologies that are showing the potential of high-quality sensors and image products that are getting easier to access and captured with recent technology. Secondly, several relevant sensors being thermal or laser-based are introduced pointing out the application possibilities, their limits, and potential fusion of them. Emphasis is further driven to Machine Learning techniques adopted from Artificial Intelligence that improve algorithms for auto-detection and represent an important step forwards to an integrated system of spatial data use in the Disaster Management Cycle. The combination of Multi-Sensor Systems, new Platform technologies, and Machine Learning indeed creates a very important benefit for the future.

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