The document details and evaluates different technologies for gesture recognition, including computer vision, accelerometers, and gloves. It provides a literature review of papers on vision-based and accelerometer-based gesture recognition techniques. The document proposes parameters for evaluating and comparing these technologies, such as resolution, accuracy, latency, range of motion, user comfort, and cost. It assigns weights to these parameters based on the goals of developing a gesture recognition system for research purposes.
The document describes a digital pen system for gesture recognition and control of devices. It contains an accelerometer to capture gesture data and a microcontroller to process the data and control output devices. The system can recognize digits 0-7 written in air and uses gestures to control a fan and light. It aims to provide an alternative interface for human-machine interaction, especially for disabled users. The hardware includes an ATmega32 microcontroller, accelerometer module, LCD display, and output devices. The system works by measuring acceleration data during gestures and identifying the gestures to control external devices.
Human Motion Detection in Video Surveillance using Computer Vision TechniqueIRJET Journal
The document discusses a technique for detecting human motion in video surveillance using computer vision. It proposes a method called DECOLOR (Detecting Contiguous Outliers in the LOw-rank Representation) that formulates object detection as outlier detection in a low-rank representation of video frames. This allows it to detect moving objects flexibly without assumptions about foreground or background behavior. DECOLOR simultaneously performs object detection and background estimation using only the test video sequence, without requiring training data. The method models the outlier support explicitly and favors spatially contiguous outliers, making it suitable for detecting clustered foreground objects like people. It achieves more accurate detection and background estimation than state-of-the-art robust principal component analysis methods.
Digital Pen for Handwritten Digit and Gesture Recognition Using Trajectory Re...IOSR Journals
The document describes a digital pen system for handwritten digit and gesture recognition using a trajectory recognition algorithm. The system uses a tri-axial accelerometer, ARM processor, and Zigbee module in a pen-like device to capture acceleration signals from hand motions. The signals are transmitted wirelessly and a trajectory recognition algorithm processes the data through steps of acquisition, preprocessing, feature generation/selection, and extraction to recognize digits and gestures written in air. The system aims to allow for flexible use without limitations of range, environment, or surface that other methods impose. It provides a portable and generalized approach to human-computer interaction through writing and gestures.
The paper presents a methodology for detecting a virtual passive pointer. The passive pointer or device does not have any active energy source within it (as opposed to a laser pointer) and thus cannot easily be detected or identified. The modeling and simulation task is carried out by generating high resolution color images of a pointer viewing via two digital cameras with a popular three-dimensional (3D) computer graphics and animation program, Studio 3D Max by Discreet. These images are then retrieved for analysis into a Microsoft’s Visual C++ program developed based on the theory of image triangulation. The program outputs a precise coordinates of the pointer in the 3D space in addition to it’s projection on a view screen located in a large display/presentation room. The computational results of the pointer projection are compared with the known locations specified by the Studio 3D Max for different simulated configurations. High pointing accuracy is achieved: a pointer kept 30 feet away correctly hits the target location within a few inches. Thus this technology can be used in presenter-audience applications.
This document summarizes a research paper on a webcam-based intelligent surveillance system. The system uses a webcam as a sensor to capture live images of the monitored area. If any motion is detected in the images, the software stores the captured images in a folder. It also sends a wireless signal to a receiver. The system has three security levels - high, middle, and low - to control the monitoring sensitivity. The key modules are login, camera interfacing, image capturing and storage, motion detection, and hardware interfacing. Motion detection works by comparing images over time and detecting changes. The system is presented as an affordable alternative to other security systems like CCTV cameras that have drawbacks around costs and vulnerability to hacking.
An Approach for Object and Scene Detection for Blind Peoples Using Vocal Vision.IJERA Editor
This system help the blind peoples for the navigation without the help of third person so blind person can perform its work independently. This system implemented on android device in which object detection and scene detection implemented, so after detection there will be text to speech conversion so user or blind person can get message from that android device with the help of headphone connected to that device. Our project will help blind people to understand the images which will be converted to sound with the help of webcam. We shall capture images in front of blind peoples .The captured image will be processed through our algorithms which will enhances the image data. The hardware component will have its own database. The processed image is compare with the database in the hardware component .The result after processing and comparing will be converted into speech signals. The headphones guide the blind peoples.
IRJET- Intrusion Detection through Image Processing and Getting Notified ...IRJET Journal
This document describes a proposed smart home intrusion detection system that uses image processing, face recognition, and notification technologies. The system would use an infrared camera and sensors to detect intruders, capture images of their faces, compare the images to a database to identify known intruders, and immediately notify home owners via mobile app with push notifications and images of the intruders. It discusses the system architecture, use of Raspberry Pi, image processing algorithms like LBPH, challenges with current systems, and outlines the development of an Android mobile app to facilitate user notifications.
IRJET- A Survey on Control of Mechanical ARM based on Hand Gesture Recognitio...IRJET Journal
This document summarizes a research paper that proposed a system using wearable IMU sensors and machine learning to recognize hand gestures and control a mechanical arm. The system uses an IMU-based wearable device to collect gesture data from hand movements. A support vector machine classifier is used to classify the gestures in real-time and control the movements of a mechanical arm. The paper reviews several related works that used different sensors and machine learning algorithms for hand gesture recognition, finding that support vector machines provided high accuracy for gesture classification. The proposed system aims to allow remote control of machines through natural hand gestures.
The document describes a digital pen system for gesture recognition and control of devices. It contains an accelerometer to capture gesture data and a microcontroller to process the data and control output devices. The system can recognize digits 0-7 written in air and uses gestures to control a fan and light. It aims to provide an alternative interface for human-machine interaction, especially for disabled users. The hardware includes an ATmega32 microcontroller, accelerometer module, LCD display, and output devices. The system works by measuring acceleration data during gestures and identifying the gestures to control external devices.
Human Motion Detection in Video Surveillance using Computer Vision TechniqueIRJET Journal
The document discusses a technique for detecting human motion in video surveillance using computer vision. It proposes a method called DECOLOR (Detecting Contiguous Outliers in the LOw-rank Representation) that formulates object detection as outlier detection in a low-rank representation of video frames. This allows it to detect moving objects flexibly without assumptions about foreground or background behavior. DECOLOR simultaneously performs object detection and background estimation using only the test video sequence, without requiring training data. The method models the outlier support explicitly and favors spatially contiguous outliers, making it suitable for detecting clustered foreground objects like people. It achieves more accurate detection and background estimation than state-of-the-art robust principal component analysis methods.
Digital Pen for Handwritten Digit and Gesture Recognition Using Trajectory Re...IOSR Journals
The document describes a digital pen system for handwritten digit and gesture recognition using a trajectory recognition algorithm. The system uses a tri-axial accelerometer, ARM processor, and Zigbee module in a pen-like device to capture acceleration signals from hand motions. The signals are transmitted wirelessly and a trajectory recognition algorithm processes the data through steps of acquisition, preprocessing, feature generation/selection, and extraction to recognize digits and gestures written in air. The system aims to allow for flexible use without limitations of range, environment, or surface that other methods impose. It provides a portable and generalized approach to human-computer interaction through writing and gestures.
The paper presents a methodology for detecting a virtual passive pointer. The passive pointer or device does not have any active energy source within it (as opposed to a laser pointer) and thus cannot easily be detected or identified. The modeling and simulation task is carried out by generating high resolution color images of a pointer viewing via two digital cameras with a popular three-dimensional (3D) computer graphics and animation program, Studio 3D Max by Discreet. These images are then retrieved for analysis into a Microsoft’s Visual C++ program developed based on the theory of image triangulation. The program outputs a precise coordinates of the pointer in the 3D space in addition to it’s projection on a view screen located in a large display/presentation room. The computational results of the pointer projection are compared with the known locations specified by the Studio 3D Max for different simulated configurations. High pointing accuracy is achieved: a pointer kept 30 feet away correctly hits the target location within a few inches. Thus this technology can be used in presenter-audience applications.
This document summarizes a research paper on a webcam-based intelligent surveillance system. The system uses a webcam as a sensor to capture live images of the monitored area. If any motion is detected in the images, the software stores the captured images in a folder. It also sends a wireless signal to a receiver. The system has three security levels - high, middle, and low - to control the monitoring sensitivity. The key modules are login, camera interfacing, image capturing and storage, motion detection, and hardware interfacing. Motion detection works by comparing images over time and detecting changes. The system is presented as an affordable alternative to other security systems like CCTV cameras that have drawbacks around costs and vulnerability to hacking.
An Approach for Object and Scene Detection for Blind Peoples Using Vocal Vision.IJERA Editor
This system help the blind peoples for the navigation without the help of third person so blind person can perform its work independently. This system implemented on android device in which object detection and scene detection implemented, so after detection there will be text to speech conversion so user or blind person can get message from that android device with the help of headphone connected to that device. Our project will help blind people to understand the images which will be converted to sound with the help of webcam. We shall capture images in front of blind peoples .The captured image will be processed through our algorithms which will enhances the image data. The hardware component will have its own database. The processed image is compare with the database in the hardware component .The result after processing and comparing will be converted into speech signals. The headphones guide the blind peoples.
IRJET- Intrusion Detection through Image Processing and Getting Notified ...IRJET Journal
This document describes a proposed smart home intrusion detection system that uses image processing, face recognition, and notification technologies. The system would use an infrared camera and sensors to detect intruders, capture images of their faces, compare the images to a database to identify known intruders, and immediately notify home owners via mobile app with push notifications and images of the intruders. It discusses the system architecture, use of Raspberry Pi, image processing algorithms like LBPH, challenges with current systems, and outlines the development of an Android mobile app to facilitate user notifications.
IRJET- A Survey on Control of Mechanical ARM based on Hand Gesture Recognitio...IRJET Journal
This document summarizes a research paper that proposed a system using wearable IMU sensors and machine learning to recognize hand gestures and control a mechanical arm. The system uses an IMU-based wearable device to collect gesture data from hand movements. A support vector machine classifier is used to classify the gestures in real-time and control the movements of a mechanical arm. The paper reviews several related works that used different sensors and machine learning algorithms for hand gesture recognition, finding that support vector machines provided high accuracy for gesture classification. The proposed system aims to allow remote control of machines through natural hand gestures.
IRJET- Convenience Improvement for Graphical Interface using Gesture Dete...IRJET Journal
This document discusses a proposed system for improving graphical user interfaces using hand gesture detection. The system aims to allow users to access information from the internet without using input devices like a mouse or keyboard. It uses a webcam to capture images of hand gestures, which are then processed using techniques like skin color segmentation, principal component analysis, and template matching to recognize the gestures. The recognized gestures can then be linked to retrieving specific data from pre-defined URLs. An evaluation of the system found it had an accuracy rate of 90% in real-time testing for retrieving data from 10 different URLs using 10 unique hand gestures. The proposed system provides a more convenient interface compared to traditional mouse and keyboard methods.
Surveillance using the video is a bit sophisticated task, yet making use of
technology things can be done perfect. Security has been so difficult in the past that it was
overlooked or avoided by security installers unless absolutely necessary. The present focus
of computer vision Technology aimed at automating the analysis of Closed Circuit Tele
Vision (CCTV) footages. This includes automatic identification of objects in a raw video,
following those objects over time and between cameras, and the interpretation of those
object’s appearance and movements. Here achieving video analytics by implementing its
segments, through Open CV with an e.g., Extracting the edges of a live video through web
cam and finding the motion detection in Live video. In this paper we even discuss about the
feature of 3-D sensors in video surveillance.
This document discusses technologies that can improve depth perception and accuracy in intelligent cameras. It describes limitations with monocular vision systems and how technologies like stereo vision, time-of-flight sensing, and structured light using digital light processing can overcome these limitations. Texas Instruments offers processor solutions and reference designs that integrate these depth sensing technologies to significantly improve video analytics accuracy for intelligent camera applications.
IRJET- Moving Object Detection with Shadow Compression using Foreground Segme...IRJET Journal
The document describes a study that investigates using foreground segmentation to extract moving objects from videos by detecting differences between frames, with the goal of tracking silhouettes of moving objects to create an interactive video display. The researchers propose using a statistical approach to segment foreground objects and apply filtering techniques to reduce noise from the extracted shadows. The results indicate the extraction process accurately tracks motion and outlines of foreground objects between frames.
Video / Image Processing ( ITS / Task 5 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document discusses video and image processing. It begins by defining image processing as the conversion of images to digital form to perform operations like enhancement. The main purposes and types of image processing are then outlined. Applications of video/image processing discussed include intelligent transportation systems, remote sensing, object tracking, defense surveillance, biomedical imaging, and automatic visual inspection systems. Pixel analysis and its relationship to image resolution is also explained. The document concludes by discussing the use of CCTV cameras in traffic management centers to monitor traffic conditions and incidents.
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...Innerspec Technologies
This paper introduces a new wireless solution that permits performing accurate and traceable ultrasonic scans of components with complex geometries using a hand-held scanner. The system integrates an array of 3D cameras that track the position of the hand of the inspector with a high-performance PAUT instrument to provide accurate, highresolution C-Scans on any component. This paper provides results of hand-held scans on complex composite parts,
and explores how the solution compares with traditional semi-automatic and automatic systems in terms of setup, ease-of-use, performance, productivity, and cost.
OBSERVATIONAL DISCRETE LINES FOR THE DETECTION OF MOVING VEHICLES IN ROAD TRA...ijcseit
The paper deals with the development of mathematical models and algorithms for video processing in digital video surveillance systems to detect moving objects. The model and algorithm can be applied in video surveillance systems to identify moving objects on a surveillance area. The reduction of the
calculations for the segmentation of video is considered and describes the algorithm of Observational discrete lines for the detection and tracking of moving objects is proposed in this article.
This document discusses machine vision and various components of machine vision systems. It describes different types of sensors used in machine vision like cameras, frame grabbers, and describes the process of sensing and digitizing image data through analog to digital conversion, image storage, and lighting techniques. It also discusses image processing and analysis techniques like segmentation, feature extraction and object recognition. Finally, it provides examples of applications of machine vision systems in inspection, identification, and navigation.
This document summarizes a student project to develop a reading system for blind people using optical character recognition and a braille glove. The system uses a webcam to capture text, an OCR software to recognize the text, and transmits the text to a braille glove using a microcontroller circuit board. The project was developed in two stages - using a laptop and webcam, and then modifying it to use a smartphone's camera and OCR software to make it more portable. The document provides details on the objectives, components, software, and development process of the assistive reading system.
IRJET - Gesture Controlled Home Automation using CNNIRJET Journal
This document describes a gesture controlled home automation system using a convolutional neural network (CNN). The system uses an Android application to capture gestures from the user via the smartphone camera. These images are sent to a Raspberry Pi which classifies the gestures using a CNN model trained on image data. Based on the predicted gesture class, the Raspberry Pi controls connected home appliances such as lights and fans. The system aims to provide a portable and flexible way for users, especially those with disabilities, to remotely control appliances through simple gestures without additional hardware.
Mems Sensor Based Approach for Gesture Recognition to Control Media in ComputerIJARIIT
Gesture Recognition is the method of identifying and understanding meaningful movements of the arms, hands,
face, or sometimes head. It is one of the most important aspects in the field of Human-Computer interface. There has been a
continuous research in this field because of its ability for application in user interfaces. Gesture Recognition is one of the
important areas of research for engineers and scientists. Nowadays the industry is working on the different implementation for
the trouble free, natural and easy product which can be easy to handle. This paper proposed a method to work with motion
sensors and interpret the motion of hand into various applications in a virtual interface. The Micro-Electro-Mechanical
Systems (MEMS) accelerometers are used to capture the dynamic hand gesture. These sensors information is transferred to
the microcontroller from where these data are transferred wirelessly to the computer system for actual processing of the data
with the use of various algorithms.
IRJET= Air Writing: Gesture Recognition using Ultrasound Sensors and Grid-Eye...IRJET Journal
This document presents a method for recognizing gestures using ultrasound sensors and infrared array sensors. Two ultrasound sensor pairs are used to capture hand motion in vertical and horizontal directions. An infrared Grid-Eye sensor is used to trigger the ultrasound sensors when a hand gesture is detected. The sensors capture data on the distance and movement of the hand. This data is preprocessed and extracted into features representing the average and count of upward and downward motions. An artificial neural network with two hidden layers is trained on these features to classify gestures for two letters, achieving an accuracy of 83%. The proposed method aims to provide a contactless gesture recognition system without some of the disadvantages of vision-based techniques.
Design of wheelchair using finger operation with image processing algorithmseSAT Publishing House
The document describes the design of a finger-operated wheelchair using digital image processing and embedded technology. The system would allow handicapped individuals to control a wheelchair through finger gestures detected by a camera and processed by a microcontroller. The wheelchair would be navigated based on finger position analysis and gesture recognition algorithms to move the wheelchair forward, backward, left, and right.
Design of wheelchair using finger operation with image processing algorithmseSAT Journals
Abstract This paper may be helpful in making the living of handicapped people easy which will be helpful for the handicapped people to drive their wheelchair through figure operation. It is an opportunity to be a handicapped as independent. System comprises of integration of finger operation using digital image processing and embedded technology.According to the World Health Organization (WHO), between the 7 and 10% of the population worldwide suffer from some physical disability. In Latin-America the physically disabled are estimated in 55 million people, which represent the 9% of its total population. This census indicates that the most common disability is motor, followed by blindness, deafness, intellectual and language. All alone, the motor-disabled achieve 20 million in Latin-America and there is an anticipated continued growth due to increasing aging, longevity and accident related injuries. Wheelchairs make up a significant portion of the mobility assistive devices in use for those. Since the 1930s, the design concept of a wheelchair has been the same: a main frame, two large rear wheels and two small front wheels called casters. This basic design has been used to develop many of today’ wheelchairs by applying slight design modifications to produce lighter, more durable and more comfortable wheelchairs for people who use them on a daily basis. Finally it means that is affordable and beneficial by the people. Keywords: Digital Image Processing, Embedded Technology, Finger operated Wheelchair
The document describes the assembly and programming of an autonomous line following robot to avoid obstacles. It includes:
1. Objectives to assemble the robotic kit using a Texas Instruments MSP430G2553 microcontroller and program it for line following and obstacle avoidance. Components used include sensors, motors, and the IAR compiler.
2. Project execution was divided into hardware development, software development, and testing of line following, obstacle avoidance, and integrated functions. The schedule planned was impacted by changes to critical assumptions.
3. Hardware was developed by modifying the SAM board to integrate with the MSP430G2553 and using tools and materials like the Launchpad board.
4. Software was programmed using
Smart Bank Locker Access System Using Iris ,Fingerprints,Face Recognization A...IJERA Editor
In today's modern world, security plays an important role. For that purpose, we proposed advance security systems for banking locker system and the bank customers. This specialized security is proposed through four different modules in combination i.e. face detection technique, Password verification, finger prints and Iris verification .All these steps are followed in the sequence if anything goes wrong he or she is unable to access the system. We are also providing some additional billing with it i.e. it will always for after 1 transaction. It will give the intimation of first transaction is complete and you are left with prescribed number transaction mentioned in system.
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...IRJET Journal
This document reviews object detection using the Zynq-7000 FPGA for embedded applications. It discusses how the Zynq-7000 FPGA is a promising platform for embedded applications due to its dual-core ARM processor and programmable logic on a single chip. The document reviews various object detection algorithms such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO and compares their prediction times. It is proposed to implement object detection on the Zynq-7000 FPGA using algorithms like YOLO that provide fast and accurate detection in real-time.
The document describes a gesture-based lamp control system created by team members William, Alex, and Amy. The system uses motion sensors worn by users to detect gestures and activities that control smart lamps connected over a local network. The system diagram shows motion sensors communicating with beacons and a computer program that interfaces with Philips Hue lamps and bridges using Bluetooth, ZigBee, and HTTP. Amy's subsystem implements data collection from sensors and controls lamps and feedback using vibrators. William's subsystem performs gesture recognition using dynamic time warping and adaptive training. Alex's subsystem identifies targets using magnetometer data and DTW distances to predefined templates. The team members discuss their individual work and challenges in signal processing, recognition, and training
final presentation from William, Amy and AlexZiwei Zhu
The document describes a gesture-based lamp control system created by team members William, Alex, and Amy. The system uses motion sensors worn by users to detect gestures and activities that control smart lamps connected over a local network. The system diagram shows motion sensors communicating with beacons and a computer program that interfaces with Philips Hue lamps and bridges using Bluetooth, ZigBee, and HTTP. Amy's subsystem implements data collection from sensors and controls lamps and feedback using vibrators. William's subsystem uses dynamic time warping for gesture recognition and classification. Alex's subsystem identifies targets using magnetometer data and DTW distances to predefined templates. The team discusses their work on signal processing, adaptive training, recognition, and solving related challenges
Applying Support Vector Learning to Stem Cells Classificationbutest
The document discusses applying machine learning algorithms like perceptrons and support vector machines to classify stem cell images. It describes imaging stem cell nuclei, the problem of stem cell classification, and online machine learning methods. The perceptron achieved 70.38% accuracy on test data while support vector machines with radial basis function kernels optimized by CMA-ES achieved 97.02% accuracy, showing machine learning is feasible for stem cell image classification.
The document discusses a novel domain ontology discovery method that exploits contextual information from knowledge sources to construct domain ontologies. It involves parsing text, identifying lexical patterns, extracting linguistic patterns, performing statistical token analysis using mutual information, and developing a taxonomy of domain concepts. The proposed method aims to assist in building domain ontologies more quickly and accurately compared to existing methods.
Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
IRJET- Convenience Improvement for Graphical Interface using Gesture Dete...IRJET Journal
This document discusses a proposed system for improving graphical user interfaces using hand gesture detection. The system aims to allow users to access information from the internet without using input devices like a mouse or keyboard. It uses a webcam to capture images of hand gestures, which are then processed using techniques like skin color segmentation, principal component analysis, and template matching to recognize the gestures. The recognized gestures can then be linked to retrieving specific data from pre-defined URLs. An evaluation of the system found it had an accuracy rate of 90% in real-time testing for retrieving data from 10 different URLs using 10 unique hand gestures. The proposed system provides a more convenient interface compared to traditional mouse and keyboard methods.
Surveillance using the video is a bit sophisticated task, yet making use of
technology things can be done perfect. Security has been so difficult in the past that it was
overlooked or avoided by security installers unless absolutely necessary. The present focus
of computer vision Technology aimed at automating the analysis of Closed Circuit Tele
Vision (CCTV) footages. This includes automatic identification of objects in a raw video,
following those objects over time and between cameras, and the interpretation of those
object’s appearance and movements. Here achieving video analytics by implementing its
segments, through Open CV with an e.g., Extracting the edges of a live video through web
cam and finding the motion detection in Live video. In this paper we even discuss about the
feature of 3-D sensors in video surveillance.
This document discusses technologies that can improve depth perception and accuracy in intelligent cameras. It describes limitations with monocular vision systems and how technologies like stereo vision, time-of-flight sensing, and structured light using digital light processing can overcome these limitations. Texas Instruments offers processor solutions and reference designs that integrate these depth sensing technologies to significantly improve video analytics accuracy for intelligent camera applications.
IRJET- Moving Object Detection with Shadow Compression using Foreground Segme...IRJET Journal
The document describes a study that investigates using foreground segmentation to extract moving objects from videos by detecting differences between frames, with the goal of tracking silhouettes of moving objects to create an interactive video display. The researchers propose using a statistical approach to segment foreground objects and apply filtering techniques to reduce noise from the extracted shadows. The results indicate the extraction process accurately tracks motion and outlines of foreground objects between frames.
Video / Image Processing ( ITS / Task 5 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document discusses video and image processing. It begins by defining image processing as the conversion of images to digital form to perform operations like enhancement. The main purposes and types of image processing are then outlined. Applications of video/image processing discussed include intelligent transportation systems, remote sensing, object tracking, defense surveillance, biomedical imaging, and automatic visual inspection systems. Pixel analysis and its relationship to image resolution is also explained. The document concludes by discussing the use of CCTV cameras in traffic management centers to monitor traffic conditions and incidents.
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...Innerspec Technologies
This paper introduces a new wireless solution that permits performing accurate and traceable ultrasonic scans of components with complex geometries using a hand-held scanner. The system integrates an array of 3D cameras that track the position of the hand of the inspector with a high-performance PAUT instrument to provide accurate, highresolution C-Scans on any component. This paper provides results of hand-held scans on complex composite parts,
and explores how the solution compares with traditional semi-automatic and automatic systems in terms of setup, ease-of-use, performance, productivity, and cost.
OBSERVATIONAL DISCRETE LINES FOR THE DETECTION OF MOVING VEHICLES IN ROAD TRA...ijcseit
The paper deals with the development of mathematical models and algorithms for video processing in digital video surveillance systems to detect moving objects. The model and algorithm can be applied in video surveillance systems to identify moving objects on a surveillance area. The reduction of the
calculations for the segmentation of video is considered and describes the algorithm of Observational discrete lines for the detection and tracking of moving objects is proposed in this article.
This document discusses machine vision and various components of machine vision systems. It describes different types of sensors used in machine vision like cameras, frame grabbers, and describes the process of sensing and digitizing image data through analog to digital conversion, image storage, and lighting techniques. It also discusses image processing and analysis techniques like segmentation, feature extraction and object recognition. Finally, it provides examples of applications of machine vision systems in inspection, identification, and navigation.
This document summarizes a student project to develop a reading system for blind people using optical character recognition and a braille glove. The system uses a webcam to capture text, an OCR software to recognize the text, and transmits the text to a braille glove using a microcontroller circuit board. The project was developed in two stages - using a laptop and webcam, and then modifying it to use a smartphone's camera and OCR software to make it more portable. The document provides details on the objectives, components, software, and development process of the assistive reading system.
IRJET - Gesture Controlled Home Automation using CNNIRJET Journal
This document describes a gesture controlled home automation system using a convolutional neural network (CNN). The system uses an Android application to capture gestures from the user via the smartphone camera. These images are sent to a Raspberry Pi which classifies the gestures using a CNN model trained on image data. Based on the predicted gesture class, the Raspberry Pi controls connected home appliances such as lights and fans. The system aims to provide a portable and flexible way for users, especially those with disabilities, to remotely control appliances through simple gestures without additional hardware.
Mems Sensor Based Approach for Gesture Recognition to Control Media in ComputerIJARIIT
Gesture Recognition is the method of identifying and understanding meaningful movements of the arms, hands,
face, or sometimes head. It is one of the most important aspects in the field of Human-Computer interface. There has been a
continuous research in this field because of its ability for application in user interfaces. Gesture Recognition is one of the
important areas of research for engineers and scientists. Nowadays the industry is working on the different implementation for
the trouble free, natural and easy product which can be easy to handle. This paper proposed a method to work with motion
sensors and interpret the motion of hand into various applications in a virtual interface. The Micro-Electro-Mechanical
Systems (MEMS) accelerometers are used to capture the dynamic hand gesture. These sensors information is transferred to
the microcontroller from where these data are transferred wirelessly to the computer system for actual processing of the data
with the use of various algorithms.
IRJET= Air Writing: Gesture Recognition using Ultrasound Sensors and Grid-Eye...IRJET Journal
This document presents a method for recognizing gestures using ultrasound sensors and infrared array sensors. Two ultrasound sensor pairs are used to capture hand motion in vertical and horizontal directions. An infrared Grid-Eye sensor is used to trigger the ultrasound sensors when a hand gesture is detected. The sensors capture data on the distance and movement of the hand. This data is preprocessed and extracted into features representing the average and count of upward and downward motions. An artificial neural network with two hidden layers is trained on these features to classify gestures for two letters, achieving an accuracy of 83%. The proposed method aims to provide a contactless gesture recognition system without some of the disadvantages of vision-based techniques.
Design of wheelchair using finger operation with image processing algorithmseSAT Publishing House
The document describes the design of a finger-operated wheelchair using digital image processing and embedded technology. The system would allow handicapped individuals to control a wheelchair through finger gestures detected by a camera and processed by a microcontroller. The wheelchair would be navigated based on finger position analysis and gesture recognition algorithms to move the wheelchair forward, backward, left, and right.
Design of wheelchair using finger operation with image processing algorithmseSAT Journals
Abstract This paper may be helpful in making the living of handicapped people easy which will be helpful for the handicapped people to drive their wheelchair through figure operation. It is an opportunity to be a handicapped as independent. System comprises of integration of finger operation using digital image processing and embedded technology.According to the World Health Organization (WHO), between the 7 and 10% of the population worldwide suffer from some physical disability. In Latin-America the physically disabled are estimated in 55 million people, which represent the 9% of its total population. This census indicates that the most common disability is motor, followed by blindness, deafness, intellectual and language. All alone, the motor-disabled achieve 20 million in Latin-America and there is an anticipated continued growth due to increasing aging, longevity and accident related injuries. Wheelchairs make up a significant portion of the mobility assistive devices in use for those. Since the 1930s, the design concept of a wheelchair has been the same: a main frame, two large rear wheels and two small front wheels called casters. This basic design has been used to develop many of today’ wheelchairs by applying slight design modifications to produce lighter, more durable and more comfortable wheelchairs for people who use them on a daily basis. Finally it means that is affordable and beneficial by the people. Keywords: Digital Image Processing, Embedded Technology, Finger operated Wheelchair
The document describes the assembly and programming of an autonomous line following robot to avoid obstacles. It includes:
1. Objectives to assemble the robotic kit using a Texas Instruments MSP430G2553 microcontroller and program it for line following and obstacle avoidance. Components used include sensors, motors, and the IAR compiler.
2. Project execution was divided into hardware development, software development, and testing of line following, obstacle avoidance, and integrated functions. The schedule planned was impacted by changes to critical assumptions.
3. Hardware was developed by modifying the SAM board to integrate with the MSP430G2553 and using tools and materials like the Launchpad board.
4. Software was programmed using
Smart Bank Locker Access System Using Iris ,Fingerprints,Face Recognization A...IJERA Editor
In today's modern world, security plays an important role. For that purpose, we proposed advance security systems for banking locker system and the bank customers. This specialized security is proposed through four different modules in combination i.e. face detection technique, Password verification, finger prints and Iris verification .All these steps are followed in the sequence if anything goes wrong he or she is unable to access the system. We are also providing some additional billing with it i.e. it will always for after 1 transaction. It will give the intimation of first transaction is complete and you are left with prescribed number transaction mentioned in system.
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...IRJET Journal
This document reviews object detection using the Zynq-7000 FPGA for embedded applications. It discusses how the Zynq-7000 FPGA is a promising platform for embedded applications due to its dual-core ARM processor and programmable logic on a single chip. The document reviews various object detection algorithms such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO and compares their prediction times. It is proposed to implement object detection on the Zynq-7000 FPGA using algorithms like YOLO that provide fast and accurate detection in real-time.
The document describes a gesture-based lamp control system created by team members William, Alex, and Amy. The system uses motion sensors worn by users to detect gestures and activities that control smart lamps connected over a local network. The system diagram shows motion sensors communicating with beacons and a computer program that interfaces with Philips Hue lamps and bridges using Bluetooth, ZigBee, and HTTP. Amy's subsystem implements data collection from sensors and controls lamps and feedback using vibrators. William's subsystem performs gesture recognition using dynamic time warping and adaptive training. Alex's subsystem identifies targets using magnetometer data and DTW distances to predefined templates. The team members discuss their individual work and challenges in signal processing, recognition, and training
final presentation from William, Amy and AlexZiwei Zhu
The document describes a gesture-based lamp control system created by team members William, Alex, and Amy. The system uses motion sensors worn by users to detect gestures and activities that control smart lamps connected over a local network. The system diagram shows motion sensors communicating with beacons and a computer program that interfaces with Philips Hue lamps and bridges using Bluetooth, ZigBee, and HTTP. Amy's subsystem implements data collection from sensors and controls lamps and feedback using vibrators. William's subsystem uses dynamic time warping for gesture recognition and classification. Alex's subsystem identifies targets using magnetometer data and DTW distances to predefined templates. The team discusses their work on signal processing, adaptive training, recognition, and solving related challenges
Applying Support Vector Learning to Stem Cells Classificationbutest
The document discusses applying machine learning algorithms like perceptrons and support vector machines to classify stem cell images. It describes imaging stem cell nuclei, the problem of stem cell classification, and online machine learning methods. The perceptron achieved 70.38% accuracy on test data while support vector machines with radial basis function kernels optimized by CMA-ES achieved 97.02% accuracy, showing machine learning is feasible for stem cell image classification.
The document discusses a novel domain ontology discovery method that exploits contextual information from knowledge sources to construct domain ontologies. It involves parsing text, identifying lexical patterns, extracting linguistic patterns, performing statistical token analysis using mutual information, and developing a taxonomy of domain concepts. The proposed method aims to assist in building domain ontologies more quickly and accurately compared to existing methods.
Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
Motivated Machine Learning for Water Resource Managementbutest
The document discusses challenges in water resource management and the potential for embodied intelligence and motivated machine learning to help address these challenges. It proposes using a goal creation system in embodied intelligence to motivate a machine to learn how to efficiently interact with its environment. This approach could help integrate modeling and decision making to support sustainable water policies that consider various social, economic and environmental factors. The document outlines some key challenges in water management and argues that embodied intelligence trained with a goal creation mechanism may help overcome limitations of traditional machine learning models to better adapt to changing real-world environments.
The document provides an overview of machine learning and artificial intelligence concepts:
- Machine learning allows intelligent agents to autonomously discover knowledge from experience. It involves learning from examples without being explicitly programmed.
- Explanation-based learning systems generate explanations for training examples and generalize these to apply to new examples. Case-based learning directly applies previous cases to new problems.
- Connectionist models like neural networks are inspired by the brain and use interconnected nodes that are activated based on input signals to learn complex functions.
The document summarizes the work done in WP2 "Indexing" of the SemanticHIFI project. WP2 developed algorithms and modules for extracting audio features to enable functionalities like music segmentation, rhythm description, tonality description, generic audio descriptor extraction, music remixing, browsing by lyrics, audio identification, and tempo/phase detection. The work resulted in executable modules and libraries that were integrated into applications developed in other work packages. Scientific research methodologies were employed and results were disseminated through publications, conferences, and demonstrations.
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
Word accessible - .:: NIB | National Industries for the Blind ::.butest
The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
IRJET - A Smart Assistant for Aiding Dumb PeopleIRJET Journal
This document presents a proposed smart assistant system to help mute or vocally impaired people communicate with others using hand gestures. The system uses MEMS sensors in a glove to detect hand gestures, which are matched to pre-stored commands using an Arduino microcontroller. The relevant text is displayed on an LCD screen and audio is played back of the message in the local language as determined by a GPS module. An emergency notification can also be sent via GSM to a guardian if an emergency gesture is detected. The system is intended to help the mute community communicate more easily with others and ensure their safety in emergencies.
Human Activity Recognition Using SmartphoneIRJET Journal
The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.
A Digital Pen with a Trajectory Recognition AlgorithmIOSR Journals
Abstract : Now a days, the development of miniaturization technologies in electronic circuits and components has seriously decreased the dimension and weight of consumer electronic products, those are smart phones and handheld computers, and thus prepared them more handy and convenient. This paper contains an accelerometer-based digital pen for handwritten digit and gesture trajectory recognition applications. The digital pen consists of a triaxial accelerometer, a microcontroller, and an Zigbee wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. with this project we can do human computer interaction. Users can utilize this pen to write digits or make hand gestures, and the accelerations of hand motions calculated by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. So, by varying the position of mems (micro electro mechanical systems) we can capable to show the alphabetical characters in the PC. The acceleration signals calculated from the triaxial accelerometer are transmitted to a computer via the wireless module. Keywords - ARM, Zigbee, Sensors module
A Digital Pen with a Trajectory Recognition AlgorithmIOSR Journals
Abstract : Now a days, the development of miniaturization technologies in electronic circuits and components has seriously decreased the dimension and weight of consumer electronic products, those are smart phones and handheld computers, and thus prepared them more handy and convenient. This paper contains an accelerometer-based digital pen for handwritten digit and gesture trajectory recognition applications. The digital pen consists of a triaxial accelerometer, a microcontroller, and an Zigbee wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. with this project we can do human computer interaction. Users can utilize this pen to write digits or make hand gestures, and the accelerations of hand motions calculated by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. So, by varying the position of mems (micro electro mechanical systems) we can capable to show the alphabetical characters in the PC. The acceleration signals calculated from the triaxial accelerometer are transmitted to a computer via the wireless module. Keywords - ARM, Zigbee, Sensors module
Hand Motion Gestures For Mobile Communication Based On Inertial Sensors For O...IJERA Editor
This project presents system based on inertial sensors and gesture recognition algorithm for SMS or calling for old age people. Users hold the device to make hand gestures with their preferred handheld style. Hand motions generate inertial signals, which are wirelessly transmitted to a computer for recognition. Here DTW recognition algorithm is used for recognition of hand gestures. Zigbee is used at the transmission section of inertial device to transmit sensor values and at the receiver section of PC to receive values. Recognized gesture is send to the microcontroller for further processing which gives AT commands to GSM to selects the SMS or calling option to the person. GSM model is used for the SMS or calling. An accelerometer-based gestures recognition systems that uses only a single 3-axis accelerometer. 3-axis accelerometer recognizes gestures, where gestures here are hand movements. DTW algorithm is used in this project for recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, motion detection, template selection, and recognition. Here „a‟, „b‟, „c‟, „d‟, „e‟, „f‟, „g‟, „h‟, „o‟, „v‟ letters are recognized in this system . This system can be used for the emergency calling or emergency SMS by the old age people or blind people from the home.
The document describes a digital pen system for handwritten digit and gesture recognition using a trajectory recognition algorithm. The system uses a tri-axial accelerometer, ARM processor, and Zigbee module in a pen-like device to capture acceleration signals from hand motions. The signals are transmitted wirelessly and a trajectory recognition algorithm processes the data through steps of acquisition, preprocessing, feature generation/selection, and extraction to recognize digits and gestures written in air. The system aims to allow for flexible use without limitations of range, environment, or surface that other methods impose.
This document summarizes a research paper on traffic sign recognition using convolutional neural networks (CNNs). It discusses how a two-tier CNN architecture combined with YOLO networks can accurately detect and identify traffic signs, even in adverse weather conditions. The first part provides background on traffic sign recognition and related work using methods like support vector machines and HOG features. It then describes the current implementation which uses a two-tier CNN for sign detection and identification, and analyzes the results showing over 95% accuracy. In conclusion, the implementation proves effective for traffic sign recognition under varying conditions.
SLIDE PRESENTATION BY HAND GESTURE RECOGNITION USING MACHINE LEARNINGIRJET Journal
The document discusses a slide presentation controlled by hand gesture recognition using machine learning. It describes how different hand gestures can be used to control slide presentation functions, such as using the index finger to draw, three fingers to undo drawing, the little finger to move to the next slide, and the thumb to move to the previous slide. The system uses a camera and machine learning techniques like neural networks to recognize hand gestures in real-time and map them to slide navigation and other presentation controls.
IRJET- Survey Paper on Vision based Hand Gesture RecognitionIRJET Journal
This document presents a survey of previous research on vision-based hand gesture recognition. It discusses various methods that have been used, including discrete wavelet transforms, skin color segmentation, orientation histograms, and neural networks. The document proposes a new methodology using webcam image capture, static and dynamic gesture definition, image processing techniques like localization, enhancement, segmentation, and morphological filtering, and a convolutional neural network for classification. The goal is to develop a more efficient and accurate system for hand gesture recognition and human-computer interaction.
This document describes a MEMS accelerometer-based system for hand gesture recognition. The system uses a portable device containing a triaxial accelerometer, microcontroller, and wireless transmission module. Users can write digits and make gestures, and the accelerometer measures the motions. A trajectory recognition algorithm processes the acceleration signals through preprocessing, feature generation/selection, and classification. The algorithm aims to accurately recognize gestures with high recognition rates. An experiment tests the algorithm on handwritten digit recognition with promising results.
This document summarizes research on developing a portable device using a MEMS accelerometer for hand gesture recognition. The device consists of a triaxial accelerometer, microcontroller, and wireless transmission module. It measures acceleration signals from hand motions and transmits them to a computer for trajectory recognition using a recognition algorithm. The algorithm processes the acceleration data through steps like filtering, feature extraction, and classification to identify gestures and enable control of electronic devices through hand motions. The research aims to create an accurate, low-cost gesture recognition system using a single accelerometer without additional sensors.
The International Journal of Engineering and Sciencetheijes
This document summarizes a research paper on a hand sign interpreter system that uses a sensor glove to recognize sign language gestures and translate them into voice signals in real time. The system aims to help normal people communicate more effectively with those who are speech impaired. It uses flex sensors on a glove to detect hand shapes and an accelerometer to detect hand orientations. The signals are fed to a microprocessor that analyzes the signals and retrieves the corresponding audio files from memory to be played through a speaker. The system is designed to be low-cost and portable compared to other sign language recognition systems on the market.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Controlling Computer using Hand GesturesIRJET Journal
This document describes a research project on controlling a computer using hand gestures. The researchers created a real-time gesture recognition system using convolutional neural networks (CNNs). They developed a dataset of 3000 training images of 10 different hand gestures for tasks like opening apps. A CNN model was trained to detect hands in images and recognize gestures. The model achieved 80.4% validation accuracy and was able to successfully perform operations like opening WhatsApp, PowerPoint and other apps based on detected gestures in real-time. The system provides a cost-effective and contactless way of interacting with computers using hand gestures only.
IRJET - Efficient Approach for Number Plaque Accreditation System using W...IRJET Journal
This document presents a proposed efficient approach for number plaque recognition system using Android devices. It discusses using optical character recognition and image processing techniques to extract vehicle number plates from images and recognize the characters. The system is designed to identify vehicles for applications like toll plazas, parking areas, and secure areas by automatically recognizing license plates from moving vehicles. It compares different methods like template matching and neural networks for the character recognition component. The proposed system aims to provide a user-friendly Android application to enable contactless verification of vehicle documents using Aadhaar card numbers, reducing the need to manually carry documents. It is intended to improve security, identify vehicles violating traffic rules, and reduce the complexity and time of existing systems.
The goal of this project is to provide a platform that allows for communication between able-bodied and disabled people or between computers and human beings. There has been great emphasis on Human-Computer-Interaction research to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans . As an important part of the body, recognizing hand gesture is very important for Human-Computer-Interaction. In recent years, there has been a tremendous amount of research on hand gesture recognition
Arduino Based Hand Gesture Controlled RobotIRJET Journal
This document describes an Arduino-based hand gesture controlled robot. The robot uses image processing of hand gestures captured by a camera to determine commands. The hand gestures are compared to reference images to detect gestures like moving up, down, left, right. The corresponding command is sent wirelessly to the robot via ZigBee. The robot contains an Arduino microcontroller, accelerometer, motor driver and receives signals through a Zigbee receiver. Based on the command signal, the microcontroller controls the motor polarity to move the robot in the appropriate direction, allowing it to be controlled remotely through hand gestures without any physical controls. This provides a natural human-machine interface for applications like industrial and medical robotics.
The document describes a gesture recognition system that uses computer vision techniques. It discusses different approaches to hand gesture recognition including vision-based, glove-based, and depth-based techniques. The proposed system uses computer vision and media pipe libraries to track hand landmarks and recognize gestures in real-time. It then uses those gestures to control functions like a virtual mouse, change volume, and zoom in/out. The system aims to provide natural human-computer interaction through contactless hand gesture recognition.
Inspection of Suspicious Human Activity in the Crowd Sourced Areas Captured i...IRJET Journal
The document proposes a system to detect suspicious human activity in crowdsourced video captured by surveillance cameras. The system uses Advanced Motion Detection (AMD) to detect moving objects and generate a reliable background model for analysis. A camera connected to a monitoring room would produce alert messages for any detected suspicious activity based on height, time, and body movement constraints. The system aims to automate real-time video processing for security applications like detecting unauthorized access. It extracts human objects from frames and identifies suspicious behavior using the AMD algorithm before sending alerts.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. STEFANO CARRINO<br />http://home.hefr.ch/carrinos/<br />PhD Student<br />2008-2011<br />Technologies Evaluation &<br />State of the Art<br />This document details technologies for gesture interpretation and analysis and proposes some parameters for a classification. The technologies proposed are <br /> TOC quot;
1-3quot;
Introduction PAGEREF _Toc217100831 3<br />Our vision, in brief PAGEREF _Toc217100832 3<br />Technologies Study PAGEREF _Toc217100833 3<br />State of the Art: papers PAGEREF _Toc217100834 3<br />Gesture recognition by computer vision PAGEREF _Toc217100835 3<br />Gesture Recognition by Accelerometers PAGEREF _Toc217100836 5<br />Technology PAGEREF _Toc217100837 7<br />Technology Evaluation PAGEREF _Toc217100838 8<br />Evaluation Criteria PAGEREF _Toc217100839 8<br />Technology Comparison PAGEREF _Toc217100840 8<br />Parameters’ weight PAGEREF _Toc217100841 8<br />Comparison PAGEREF _Toc217100842 10<br />Conclusions and Remarks PAGEREF _Toc217100843 11<br />Accelerometers, gloves and cameras… PAGEREF _Toc217100844 11<br />Proposition PAGEREF _Toc217100845 11<br />Divers PAGEREF _Toc217100846 12<br />Observation PAGEREF _Toc217100847 12<br />Some commonly features for gesture recognition by image analysis PAGEREF _Toc217100848 13<br />Gesture recognition or classification methods PAGEREF _Toc217100849 13<br />quot;
Gorilla armquot;
PAGEREF _Toc217100850 14<br />References PAGEREF _Toc217100851 14<br />Attached PAGEREF _Toc217100852 16<br />Introduction<br />In the following sections we illustrate the state of the art in technologies for the acquisition of data for gesture recognition. After that we introduce some parameters for the evaluation of these approaches, motivating the weight of each parameter according to our vision. In the last section we highlight the conclusion of this research in the state of the art in this field.<br />Our vision, in brief<br />The AVATAR system will be composed by two elements:<br />The Smart Portable Device (SPD).<br />The Smart Environmental Device (SED).<br />The SPD has to provide the gesture interpretation for all the applications that are environment independent for what may concern the data acquisition (i.e. the cause and effect actions, inputs, computing machine and out put are all inside the SPD self).<br />The SED offers the gesture recognition where the SPD has not good performances. And, in addition, it could offer a layer for the connection of multiple SPD and the possibility of faster elaboration offering its computing power.<br />In this first step of our work we will focus the attention on the SPD but keeping in mind the future developments.<br />Technologies Study<br />The choice of the employed technologies (input) for the gesture interpretation is very in important in order to achieve good results in the gesture recognition. In the last years the evolution of technology and materials has pushed forward the feasibility and the robustness of this kind of systems; also more complex algorithms are now ready for this kind of applications (augmented speed in the computing processes, in mobile devices too, make the “real-time approach” reality).<br />State of the Art: papers<br />Follow a simple list of articles we have read, after the name is attached a short description.<br />Gesture recognition by computer vision<br />Arm-pointing Gesture Interface Using Surrounded Stereo Cameras System REF _Ref216867245 [1]<br />- 2004<br />- Surrounding Stereo Cameras (four stereo cameras in four corners of the ceiling)<br />- Arm pointing<br />- Setting: 12 frame/s<br />- Recognition rate: 97.4% standing<br />- Recognition rate: 94% sitting posture<br />- The lighting environment had a slight influence<br />Improving Continuous Gesture Recognition with Spoken Prosody REF _Ref216867261 [2]<br />- 2003<br />- Cameras and microphone<br />- HMM - Bayesian Network<br />- Gesture and Speech Synchronization<br />- 72.4% of 1876 gestures were classified correctly<br />Pointing Gesture Recognition based on 3DTracking of Face, Hands an Head Orientation REF _Ref216867302 [3]<br />- 2003<br />- Stereo Camera (1)<br />- HMM<br />- 65% / 83% (without / with head orientation)<br />- 90% after user specific training<br />Real-time Gesture Recognition with Minimal Training Requirements and On-Line Learning REF _Ref216867288 [4]<br />- 2007<br />- (SNM) HMMs modified for reduced training requirement<br />- Viterbi inference<br />- Optical, pressure, mouse/pen<br />- Result: ???<br />Recognition of Arm Gestures Using Multiple Orientation Sensors: gesture classification REF _Ref216867331 [5]<br />- 2004<br />- IS-300 Pro Precision Motion Tracker by InterSense<br />- Results<br />Vision-Based Interfaces for Mobility REF _Ref216867337 [6]<br />- 2004<br />- Head-worn camera<br />- AdaBoost<br />- (Larger than 30x20 pixels) runs with 10 frames per second on a 640x480 sized video stream on a 3GHz desktop computer.<br />- Interesting references<br />- 93.76% postures were classified correctly<br />GestureVR: Vision-Based 3D Hand interface for Spatial Interaction REF _Ref216867359 [7]<br />- 1998<br />- 2 cameras 60Hz 3D space<br />- 3 gestures<br />- Finite state classification<br />Gesture Recognition by Accelerometers<br />Accelerometer Based Gesture Recognition for Real Time Applications<br />- Input: Accelerometer Bluetooth<br />- HMM<br />- Gesture Recognized Correctly 96%<br />- Reaction Time: 300ms<br />Accelerometer Based Real-Time Gesture Recognition REF _Ref216867368 [8]<br />- Input: Sony-Ericsson W910i (3 axial accel.)<br />- 97.4% and 96% accuracy on a personalized gesture set<br />- HMM & SVM (Support Vector Machine)<br />- HMM (My algorithm was based on a recent Nokia Research Center paper [11] with some modifications. I have used the freely available JAHMM library for implementation.)<br />- Runtime was tested on a new generation MacBook computer with a dual core 2 GHz processor and 1 GB memory.<br />- Recognition time was independent from the number of teaching examples and averaged at 3.7ms for HMM and 0.4ms for SVM.<br />Self-Defined Gesture Recognition on Keyless Handheld Devices using MEMS 3D Accelerometer REF _Ref216867392 [11]<br />- 2008<br />- Input: Three-dimensional MEMS accelerometer and a Single Chip Microcontroller<br />- 94% Arabic number recognition <br />Gesture-recognition with Non-referenced Tracking REF _Ref216867430 [12]<br />- 2005-2006 (?)<br />- Accelerometer Bluetooth (MEMS) + gyroscopes<br />- 3motion™<br />- Particular algorithm for gesture recognition<br />- No numerical results<br />Real time gesture recognition using Continuous Time Recurrent Neural Networks REF _Ref216867447 [13]<br />- 2007<br />- Accelerometers<br />- Continuous Time Recurrent Neural Networks (CTRNN)<br />- Neuro Fuzzy system (in a previously project)<br />- Isolated gesture: 98% was obtained for the training set and 94% for the testing set<br />- Realistic environment: 80.5% and 63.6 %<br />- Neuro fuzzy system can't work in dynamic (realistic situations)<br />- G. Bailador, G. Trivino, and S. Guadarrama. Gesture recognition using a neuro-fuzzy predictor. In International Conference of Artificial Intelligence and Soft Computing. Acta press, 2006.<br />ADL Classification Using Triaxial Accelerometers and RFID REF _Ref216867468 [14]<br />- >2004<br />- ADL = Activities of Daily living<br />- 2 wireless (Zigbee homemade) accelerometers for 5 body states<br />- Glove type RFID reader<br />- 90% over 12 ADLs<br />Technology<br />The input devices used in the last years are:<br />Accelerometers<br />Wireless<br />Non wireless<br />Camera REF _Ref216868035 [17]:<br />Depth-aware cameras. Using specialized cameras one can generate a depth map of what is being seen through the camera at a short range, and use this data to approximate a 3d representation of what is being seen. These can be effective for detection of hand gestures due to their short-range capabilities. <br />Stereo cameras. Using two cameras whose relations to one another are known, a 3d representation can be approximated by the output of the cameras. This method uses more traditional cameras, and thus does not hold the same distance issues as current depth-aware cameras. To get the cameras' relations, one can use a positioning reference such as a lexian-stripe (?) or infrared emitters. <br />Single camera. A normal camera can be used for gesture recognition where the resources/environment wouldn't be convenient for other forms of image-based recognition. Although not necessarily as effective as stereo or depth aware cameras, using a single camera allows a greater possibility of accessibility to a wider audience. <br />Angle Shape Sensor REF _Ref216868069 [18]:<br />Exploiting the reflexion of the light inside optical fibre we are able to rebuild a 3D hand(s) model<br />Available also in wireless (Bluetooth), the present solutions (gloves) have to be connected with<br />Infrared technology.<br />Ultrasound / UWB (Ultra WideBand)<br />RFID<br />Gyroscopes (two angular-velocity sensors)<br />Controller-based gestures. These controllers act as an extension of the body so that when gestures are performed, some of their motion can be conveniently captured by software. Mouse gestures are one such example, where the motion of the mouse is correlated to a symbol being drawn by a person's hand, as is the Wii Remote, which can study changes in acceleration over time to represent gestures. <br />Technology Evaluation<br />Evaluation Criteria<br />In the following table there is a list of parameters of evaluation for the technologies presented in previous section.<br />Resolution: in relative amounts, resolution describes the degree to which a change can be detected. It is expressed as a fraction of an amount to which you can easily relate. For example, printer manufacturers often describe resolution as dots per inch, which is easier to relate to than dots per page.<br />Accuracy: accuracy describes the amount of uncertainty that exists in a measurement with respect to the relevant absolute standard. It can be defined in several different ways and is dependent on the specification philosophy of the supplier as well as product design. Most accuracy specifications include a gain and an offset parameter. <br />Latency: waiting time until the system firstly responses.<br />Range of motion.<br />User Comfort.<br />Cost. In economic terms.<br />Technology Comparison<br />Parameters’ weight<br />In this section we show how the weights in the previous table are chosen to characterize “my personal choice”.<br />First) Cost: we are in a research context so is not so important to value the cost of our system following a marketing approach. But I agree with the idea forwarded by H. Ford: “True progress is made only when the advantages of a new technology are within reach of everyonequot;
. For this reason the cost too appears as parameter in the table: a concept without possible future practical application is useless (to use gloves for hands modelling with a cost of 5000 $ or more are quite hard to see in a cheaper form in the future).<br />Second) User comfort: a technology completely invisible to the user will be ideal. In this perspective isn’t easy deal with the challenge “how to interface the user with the system”. For example wondering about implementation of gesture recognition without any charge to the final user (gloves, camera, sensors…) is not a dream, but, in the other hand, the output and the feedback have to be presented to the user. From this viewpoint a head-mounted display (we are wondering about application in the context of the augmented reality) looks like the first natural solution. At this point adding camera to this device doesn’t make worse the situation with a huge advantage (and future possibilities):<br />Possible uncoupling from the environment (if enough computational power is provided to the user): all the technology is on the user. <br />In any case, if we need it, we can establish a network with other systems to gain more information and enrich our system.<br />We are able to enter in the domain of wearable/mobile systems. It is a challenge but it makes valuable and richer our system.<br />Third) Range of Motion: it is a direct consequence of the earlier point. With a wearable technology we can get rid of this problem; the range of motion is strictly related to the context and not dependents to our system. With other choices (e.g. cameras and sensors in the environment) the system will work in a specific environment and can lose in generality.<br />Fourth) Latency: to deal with this problem at this level is quite untimely. The latency depends on the used technology, the applied algorithms for gesture recognition and the tracking, but, potentially, also on other parameters such as the distance between input system, elaboration system and output/feedback system. (For example if the vector of information is the sound, the time of flight may be not negligible in a real-time system.)<br />Fifth) Accuracy & Resolution: first of all the system has to be reliable. Therefore these parameters are really meaningful in our application. As far as we are concerned we would like a tracking system able to discern correctly a little vocabulary of gestures and to make possible realistic interactions with three-dimensional virtual object in a three-dimensional mixed world.<br />Comparison<br />Analyzing input approach we have noticed two features:<br />Some of the equipments presented here are the direct evolution of the previous;<br />Nowadays some technologies are (of course in this domain) evidently inferior if compared with other technologies.<br />According to the first sentence we discard from further analysis wired accelerometer; they have not advantages compared to the wireless equivalent solution.<br />Depending on the second one we can exclude the RFID compared with the UWB.<br />In previous section we add “gyroscopes” like possible technology this isn’t completely correct; in reality this kind of technology have real applicability only if integrated with accelerometers or other sensors.<br />TechnologiesarametersResolution - AccuracyLatencyRange of motionUser ComfortCostRESULTSAccelerometers - wireless3452555Camera - singled camera2454453Camera - Stereo cameras32?3 (?)326+3*?Camera - depth-aware cameras44 (?)53360Angle shape sensor (gloves)44521 (-100)54Infrared technology4454463Ultrasound2????10+XWeight54321 <br />From this table we have evaluated two approaches as most interesting:<br />The infrared technology<br />The depth-aware camera.<br />In reality these two technologies are not uncorrelated. In deed the depth-aware cameras are often equipped with infrared emitters and receivers to calculate the position in the space of the object in the field of view of the camera REF _Ref216868115 [19]. <br />Conclusions and Remarks<br />Chose a technology to implement our future work was not easy at all! Above all is that: the validity of a technology is strictly linked with its use. For example the results using a camera for gestures interpretation is strictly connected with the algorithms used to recognise the gestures. So it is impracticable to say THIS IS THE technology to use. Moreover there are others factors (as technical evolution) that we have to take into account.<br />Computer vision offers the user a less cumbersome interface, requiring of them only that they remain within the field of view of the camera or cameras. By deducing features and movement in real-time from the images captured from the cameras, gesture and posture recognition. Computer vision typically also requires good lighting conditions and the occlusion issue makes this solution application dependent.<br />Generally we can show there are two principal ways to tackle the issues tied to the gesture recognition:<br />- Computer Vision;<br />- Accelerometers (often coupled with gyroscopes or other sensors). <br />Each approach has advantages and disadvantages. In general researches show a percentage of gesture recognition above the 80% (often the 90%) within a restrict vocabulary.<br />However the evolution of new technology pushes these results toward higher level.<br />Accelerometers, gloves and cameras…<br />The scenarios we have thought about are in the context of augmented reality, for this reason, it is ordinary wondering about head-mounted display and to add a lightweight camera will not change drastically the user comfort; <br />Wireless technology provides us not so much cumbersome sensors but their integration on a human body is somewhat intrusive.<br />Gloves are another simple device not too much intrusive (in my opinion), but the cost to have a reliable mapping in a 3D space nowadays have a cost not negligible REF _Ref216868069 [18].<br />However considering generalized scenarios and the most various types of gesture (body, arms, hands…) we don’t discard the idea to bring together more kind of sensors.<br />Proposition<br />What we propose for the next step is to think about scientific problems such user identification and multiuser management, context dependence (tracking), definition of model/language of gesture, and gesture recognition (acquisition and analyses).<br />All this fixing two goals for the future applications:<br />Usability.<br />That is:<br />Robustness;<br />Reliability.<br />That not is (at this moment):<br />Easy to wear (weight).<br />Augmented / virtual reality applicability:<br />Mobility;<br />3D gesture recognition capability;<br />Dynamic (and static?) gesture recognition.<br />As next steps I will define the following:<br />Work environment;<br />Definition of a framework for gesture modelling (???); <br />Acquisition technology selection;<br />Delve into state of the art for what concerns:<br />Gesture vocabulary definition<br />Action theory<br />Framework for gesture modelling<br />The choice of the kind of gesture model will be effectuated in the forecast of the following step: to extend gesture interpretation to the environment. In this perspective we will need also a strategy to add a tracking system to determine the user position coupled with the head position and orientation. This will be necessary if we want to be independent from visual marker or similar solutions.<br />Divers<br />Observation [13]:<br />Hidden Markov models, dynamic programming and neural networks have been investigated for gesture recognition with hidden Markov models being nowadays one of the predominant approach to classify sporadic gestures (e.g. classification of intentional gestures). Fuzzy systems expert has also been investigated for gesture recognition based on analyzing complex features of the signal like the Doppler spectrum. The disadvantage of these methods is that the classification is based on the separability of the features, therefore two different gestures with similar values for these features may be difficult to classify.<br />Some commonly features for gesture recognition by image analysis [6]:<br />Image moments.<br />Skin tone Blobs.<br />Coloured Markers.<br />Geometric Features.<br />Multiscale shape characterization.<br />Motion History Images and Motion Energy Images.<br />Shape Signatures.<br />Polygonal approximation-based Shape Descriptor.<br />Shape descriptors based upon regions and graphs.<br />Gesture recognition or classification methods REF _Ref217113918 [16]<br />Following are the list of gesture recognition or classification methods proposed in the literature so far:<br />Hidden Markov Model (HMM).<br />Time Delay Neural Network (TDNN).<br />Elman Network.<br />Dynamic Time Warping (DTW).<br />Dynamic Programming.<br />Bayesian Classifier.<br />Multi-layer Perceptions.<br />Genetic Algorithm.<br />Fuzzy Inference Engine.<br />Template Matching.<br />Condensation Algorithm.<br />Radial Basis Functions.<br />Self-Organizing Map.<br />Binary Associative Machines.<br />Syntactic Pattern Recognition.<br />Decision Tree.<br />quot;
Gorilla armquot;
<br />quot;
Gorilla armquot;
REF _Ref216868255 [21] was a side-effect that destroyed vertically-oriented touch-screens as a mainstream input technology despite a promising start in the early 1980s.<br />Designers of touch-menu systems failed to notice that humans aren't designed to hold their arms in front of their faces making small motions. After more than a very few selections, the arm begins to feel sore, cramped, and oversized -- the operator looks like a gorilla while using the touch screen and feels like one afterwards. This is now considered a classic cautionary tale to human-factors designers; quot;
Remember the gorilla arm!quot;
is shorthand for quot;
How is this going to fly in real use?quot;
<br />Gorilla arm is not a problem for specialist short-term-use uses, since they only involve brief interactions which do not last long enough to cause gorilla arm.<br />References<br />Yamamoto, Y.; Yoda, I.; Sakaue, K.; Arm-pointing gesture interface using surrounded stereo cameras system, Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on Volume 4, 23-26 Aug. 2004 Page(s):965 - 970 Vol.4 <br />Kettebekov, S.; Yeasin, M.; Sharma, R.; Improving continuous gesture recognition with spoken prosody, Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference onVolume 1, 18-20 June 2003 Page(s):I-565 - I-570 vol.1<br />Kai Nickel , Rainer Stiefelhagen, Pointing gesture recognition based on 3D-tracking of face, hands and head orientation, Proceedings of the 5th international conference on Multimodal interfaces, November 05-07, 2003, Vancouver, British Columbia, Canada <br />Rajko, S.; Gang Qian; Ingalls, T.; James, J.; Real-time Gesture Recognition with Minimal Training Requirements and On-line Learning, Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on 17-22 June 2007 Page(s):1 - 8 <br />Lementec, J.-C.; Bajcsy, P.; Recognition of arm gestures using multiple orientation sensors: gesture classification, Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on 3-6 Oct. 2004 Page(s):965 - 970 <br />Kolsch, M.; Turk, M.; Hollerer, T.; Vision-based interfaces for mobility, Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004. The First Annual International Conference on 22-26 Aug. 2004 Page(s):86 - 94 <br />Jakub Segen , Senthil Kumar, Gesture VR: vision-based 3D hand interface for spatial interaction, Proceedings of the sixth ACM international conference on Multimedia, p.455-464, September 13-16, 1998, Bristol, United Kingdom <br />Beedkar ,K.; Shah, D.; Accelerometer Based Gesture Recognition for Real Time Applications, Real Time Systems, Project description; MS CS Georgia Institute of Technology<br /> Zoltán Prekopcsák, Péter Halácsy, and Csaba Gáspár-Papanek; Design and development of an everyday hand gesture interface in MobileHCI '08: Proceedings of the 10th international conference on Human computer interaction with mobile devices and services. Amsterdam, the Netherlands, September 2008.<br />Zoltán Prekopcsák (2008) Accelerometer Based Real-Time Gesture Recognition in POSTER 2008: Proceedings of the 12th International Student Conference on Electrical Engineering. Prague, Czech Republic, May 2008.<br />Zhang, Shiqi; Yuan, Chun; Zhang, Yan; Self-Defined Gesture Recognition on Keyless Handheld Devices using MEMS 3D Accelerometer, Natural Computation, 2008. ICNC '08. Fourth International Conference on Volume 4, 18-20 Oct. 2008 Page(s):237 - 241 <br />Keir, P.; Payne, J.; Elgoyhen, J.; Horner, M.; Naef, M.; Anderson, P.; Gesture-recognition with Non-referenced Tracking, 3D User Interfaces, 2006. 3DUI 2006. IEEE Symposium on25-29 March 2006 Page(s):151 - 158 <br />G. Bailador, D. Roggen, G. Tröster, and G. Triviño. Real time gesture recognition using Continuous Time Recurrent Neural Networks. In 2nd Int. Conf. on Body Area Networks (BodyNets), 2007.<br />Im, Saemi; Kim, Ig-Jae; Ahn, Sang Chul; Kim, Hyoung-Gon; Automatic ADL classification using 3-axial accelerometers and RFID sensor; Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 2008. IEEE International Conference on 20-22 Aug. 2008 Page(s):697 - 702 <br />S. Mitra, T. Acharya; Gesture Recognition- A Survey, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 2007<br />Hafiz Adnan Habib. Gesture Recognition Based intelligent Algorithms for Virtual keyboard Development. A thesis submitted in partial fulfilment for the degree of Doctor of Philosophy.<br />http://en.wikipedia.org/wiki/Gesture_recognition<br />HYPERLINK quot;
http://www.5dt.com/quot;
http://www.5dt.com/see the attached documentation.<br />HYPERLINK quot;
http://www.3dvsystems.com/quot;
http://www.3dvsystems.com/ see the attached documentation.<br />http://en.wikipedia.org/wiki/Touchscreen<br />Attached INCLUDEPICTURE quot;
http://www.5dt.com/textures/sidetop.jpgquot;
MERGEFORMATINET <br />5DT Data Glove 5 UltraProduct Description The 5DT Data Glove 5 Ultra is designed to satisfy the stringent requirements of modernMotion Capture and Animation Professionals. It offers comfort, ease of use, a small form factorand multiple application drivers. The high data quality, low cross-correlation and high data ratemake it ideal for realistic realtime animation.The 5DT Data Glove 5 Ultra measures finger flexure (1 sensor per finger) of the user's hand. The system interfaces with the computer via a USB cable. A Serial Port (RS 232 - platform independent) option is availible through the 5DT Data Glove Ultra Serial Interface Kit. It features 8-bit flexure resolution, extreme comfort, low drift and an open architecture. The 5DT Data Glove Ultra Wireless Kit interfaces with the computer via Bluetooth technology (up to 20m distance) for high speed connectivity for up to 8 hours on a single battery. Right- and left-handed models are available. One size fits many (stretch lycra). Features Advanced Sensor Technology Wide Application Support Affordable quality Extreme comfort One size fits many Automatic calibration - minimum 8-bit flexture resolution Platform independant - USB Or Serial interface (RS 232) Cross-platform SDK Bundled software High update rate On-board processor Low crosstalk between fingers Wireless version available (5DT Ultra Wireless Kit) Quick quot;
hot releasequot;
connectionRelated Products 5DT Data Glove 14 Ultra5DT Data Glove 5 MRI (For Magnetic Resonance Imaging Applications)5DT Data Glove 16 MRI (For Magnetic Resonance Imaging Applications)5DT Wireless Kit Ultra5DT Serial Interface Kit Data SheetsData sheets must be viewed with a PDF-viewer. If you do not have a PDF-viewer, you can download Adobe Acrobat Reader from Adobe's site at http://www.adobe.com/products/acrobat/readstep.html. 5DT Data Glove Series Data Sheet: 5DTDataGloveUltraDatasheet.pdf (124 KB) Manuals Manuals must be viewed with a PDF-viewer. If you do not have a PDF-viewer, you can download Adobe Acrobat Reader from Adobe's site at http://www.adobe.com/products/acrobat/readstep.html. 5DT Data Glove 5 Manual: 5DT Data Glove Ultra - Manual.pdf (2,168 KB) Glove SDK Windows and Linux SDK (free):The current version of the windows SDK is 2.0 and Linux 1.04a. The driver works for all versions of the 5DT Data Glove Series. Please refer to the driver manual for instructions on how to install and use it. Windows users will need a program that can open ZIP files, such as WinZip, from www.winzip.com. For Linux, use the quot;
unzipquot;
command. Windows 95/98/NT/2000 SDK: GloveSDK_2.0.zip (212 KB) Linux SDK: 5DTDataGloveDriver1_04a.zip (89.0 KB) The following files contains all the SDK, manuals, glove software and data sheets for the 5DT Data Glove Series: Windows 95/98/NT/2000: GloveSetup_Win2.2.exe (13.4 MB) Linux: 5DTDataGloveSeriesLinux1_02.zip (1.21 MB ) Unix Driver:The 5DT Data Glove Ultra Driver for Unix provides access to the 5DT range of data gloves at an intermediate level. The driver functionality includes multiple instances, easy initialization and shutdown, basic (raw) sensor values, scaled (auto-calibrated) sensor values, calibration functions, basic gesture recognition and a cross-platform Application Programming Interface (API). The driver utilizes Posix threads. Pricing for this driver is shown below. Go to our Downloads page for more drivers, data sheets, software and manuals.PricingPRODUCT NAMEPRODUCT DESCRIPTIONPRICE5DT Glove 5 Ultra Right-handed5 Sensor Data Glove: Right-handedUS$9955DT Glove 5 Ultra Left-handed5 Sensor Data Glove: Left-handedUS$995Accessories 5DT Ultra Wireless KitKit allows for 2 Gloves in one compact packageUS$1,4955DT Data Glove Serial KitSerial Interface Kit US$195Drivers & Software Alias | Kaydara MOCAP Driver US$4953D Studio Max 6.0 Driver US$295Maya Driver US$295SoftImage XSI Driver US$295UNIX SDK* Please Note Serial Only (No USB Drivers)US$495<br />ZCamTM3D video cameras by 3DVSince it was established 3DV Systems has developed 4 generations of depth cameras. Its primary focus in developing new products throughout the years has been to reduce their cost and size, so that the unique state-of-the-art technology will be affordable and meet the needs of consumers as well as of these of multiple industries. In recent years 3DV has been developing DeepCTM, a chipset that embodies the company's core depth sensing technology. This chipset can be fitted to work in any camera for any application, so that partners (e.g. OEMs) can use their own know-how, market reach and supply chain in the design and manufacturing of the overall camera capabilities. The chipset will be available for sale soon.The new ZCamTM (previously Z-Sense), 3DV's most recently completed prototype camera, is based on DeepCTM and is the company's smallest and most cost-effective 3D camera. At the size of a standard webcam and at affordable cost, it provides very accurate depth information at high speed (60 frames per second) and high depth resolution (1-2 cm). At the same time, it provides synchronized and synthesized quality colour (RGB) video (at 1.3 M-Pixel). With these specifications, the new ZCamTM (previously Z-Sense) is ideal for PC-based gaming and for background replacement in web-conferencing. Game developers, web-conferencing service providers and gaming enthusiasts interested in the new ZCamTM (previously Z-Sense) are invited to contact us. As previously mentioned, the new ZCamTM (previously Z-Sense) and DeepCTM are the latest achievements backed by a tradition of providing high quality depth sensing products. Z-CamTM, the first depth video camera, was released in 2000 and was targeted primarily at broadcasting organizations. Z-MiniTM and DMC-100TM followed, each representing another leap forward in reducing cost and size. <br />