Gesture recognition
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#1
03-11-2009, 03:31 PM


Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from the face and hand gesture recognition. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse. Gesture recognition enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at the computer screen so that the cursor will move accordingly. This could potentially make conventional input devices such as mouse, keyboards and even touch-screens redundant.

Gesture recognition can be conducted with techniques from computer vision and image processing.
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19-03-2010, 08:06 PM

i want full info plz send me to vijaykreddy86@gmail.com
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#3
23-06-2010, 06:25 PM

hi,
it wud b really helpful if cud u send me a detailed seminar and presentation report of gesture recognition as well as human area network.

amrita.
plz do send the reports on amrita.asok@gmail.com
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25-06-2010, 05:40 PM

Hi, visit this thread for human area network full report:
topicideashow-to-human-area-network-full-report
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#5
25-06-2010, 06:06 PM

Gesture recognition
This is a field which interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. The current areas of focus in this field are emotion recognition from the face and hand gesture recognition. This technology allows the human beings to interface with the machine (HMI) and interact naturally without any mechanical devices. Controlling a cursor using the gesture could be an example which can displace the devices like the mouse, keyboards and even touch-screens .

Uses of Gesture recognition

-For socially assistive robotics: By reading the accelerometers and gyros sensors in the patient's body the robots can assist in patient rehabilitation in cases such as stroke.

-Sign language recognition.:
esture recognition software can transcribe the symbols represented through sign language into letters of text.

-Control through facial gestures.:computer can be controlled through facial gestures. Eye tracking in particular may be of use for controlling cursor motion

-Directional indication through pointing:has application sin the field of robotics.

-Immersive game technology
-Virtual controllers etc:

Input devices

Depth-aware cameras.: a depth map of the image , then a 3d representation can be helpful in gesture recoggnition in short range

Controller-based gestures: motion can becaptured by software

Challenges
image noise., quality of the equipment used are the major challenges.


for more details, refer:
en.wikipediawiki/Gesture_recognition
cs.rutgers.edu/~vladimir/pub/pavlovic97pami.pdf
academypublisherjmm/vol01/no07/jmm01071829.pdf
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#6
04-10-2010, 03:37 PM


.docx   Gesture recognition.docx (Size: 506.89 KB / Downloads: 243)
This article is presented by:
Sunny Dwivedi
Bhaskar Pal
Ajit Yadav

DEPARTMENT OF E & TC ENGINEERING
ARMY INSTITUTE OF TECHNOLOGY
DIGHI HILLS, PUNE 411015
Gesture recognition



ABSTRACT
Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gesture via mathematical algorithms. Gesture can originate from any bodily motion or state but commonly originate from face or hand. Current focuses in the field of
Using Neural network a simple and fast algorithm will be developed to work on a workstation. It will recognize static hand gestures, namely, a subset of American Sign Language (ASL).
A pattern recognition system emotion recognition from the face and hand gesture recognition. will be using a transform that converts an image into a feature vector, which will then be compared with the feature vectors of a training set of gestures. The final system will be implemented with a Perceptron network.
PROJECT OBJECTIVE
The scope of this project and implimentation is to create a method to recognize hand gestures, based on a pattern recognition technique developed by McConnell; employing histograms of local orientation. The orientation histogram will be used as a feature vector for gesture classification and interpolation.

High priority for the system is to be simple without making use of any special hardware. All the computation should occur on a workstation or PC. Special hardware would be used only to digitize the image (scanner or digital camera).

INTRODUCTION
Since the introduction of the most common input computer devices not a lot have changed. This is probably because the existing devices are adequate. It is also now that computers have been so tightly integrated with everyday life, that new applications and hardware are constantly introduced. The means of communicating with computers at the moment are limited to keyboards, mice, light pen, trackball, keypads etc. These devices have grown to be familiar but inherently limit the speed and naturalness with which we interact with the computer.
As the computer industry follows Moore’s Law since middle 1960s, powerful machines are built equipped with more peripherals. Vision based interfaces are feasible and at the present moment the computer is able to “see”. Hence users are allowed for richer and user friendly man-machine interaction. This can lead to new interfaces that will allow the deployment of new commands that are not possible with the current input devices. Plenty of time will be saved as well. Recently, there has been a surge in interest in recognizing human hand gestures. Handgesture recognition has various applications like computer games, machinery control (e.g. crane), and thorough mouse replacement. One of the most structured sets of gestures belongs to sign language. In sign language, each gesture has an assigned meaning (or meanings).
Computer recognition of hand gestures may provide a more natural-computer interface, allowing people to point, or rotate a CAD model by rotating their hands. Hand gestures can be classified in two categories: static and dynamic. A static gesture is a particular hand configuration and pose, represented by a single image. A dynamic gesture is a moving gesture, represented by a sequence of images. We will focus on the recognition of static images.
Interactive applications pose particular challenges. The response time should be very fast. The user should sense no appreciable delay between when he or she makes a gesture or motion and when the computer responds. The computer vision algorithms should be reliable and work for different people.
There are also economic constraints: the vision-based interfaces will be replacing existing ones, which are often very low cost. A hand-held video game controller and a television remote control each cost about $40. Even for added functionality, consumers may not want to spend more. When additional hardware is needed the cost is considerable higher. Academic and industrial researchers have recently been focusing on analyzing images of people.


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#7
23-03-2012, 01:02 PM

Gesture recognition



.docx   Gesture recognition in word.docx (Size: 84.83 KB / Downloads: 42)

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from the face and hand gesture recognition. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques.[1]
Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse.
Gesture recognition enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at the computer screen so that the cursor will move accordingly. This could potentially make conventional input devices such as mouse, keyboards and even touch-screens redundant.



Gesture types
In computer interfaces, two types of gestures are distinguished:[6]
• Offline gestures: Those gestures that are processed after the user interaction with the object. An example is the gesture to activate a menu.
• Online gestures: Direct manipulation gestures. They are used to scale or rotate a tangible object.
[edit] Uses
Gesture recognition is useful for processing information from humans which is not conveyed through speech or type. As well, there are various types of gestures which can be identified by computers.
• Sign language recognition. Just as speech recognition can transcribe speech to text, certain types of gesture recognition software can transcribe the symbols represented through sign language into text


emulate the functionality of most major brand television remote controls.

Input devices
The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools. Although there is a large amount of research done in image/video based gesture recognition, there is some variation within the tools and environments used between implementations.

• Wired gloves. These can provide input to the computer about the position and rotation of the hands using magnetic or inertial tracking devices. Furthermore, some gloves can detect finger bending with a high degree of accuracy (5-10 degrees), or even provide haptic feedback to the user, which is a simulation of the sense of touch.[18] The first commercially available hand-tracking glove-type device was the DataGlove,[19] a glove-type device which could detect hand position, movement and finger bending. This uses fiber optic cables running down the back of the hand. Light pulses are created and when the fingers are bent, light leaks through small cracks and the loss is registered, giving an approximation of the hand pose.
• Depth-aware cameras. Using specialized cameras such as time-of-flight 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.[20]
• Stereo cameras. Using two cameras whose relations to one another are known, a 3d representation can be approximated by the output of the cameras. To get the cameras' relations, one can use a positioning reference such as a lexian-stripe or infrared emitters.[21] In combination with direct motion measurement (6D-Vision) gestures can directly be detected.





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#8
27-03-2012, 10:36 AM

to get information about the topic "gesture recognition " full report ppt and related topic refer the link bellow

topicideashow-to-gesture-recognition--4601

topicideashow-to-gesture-recognition--4601?pid=77113#pid77113

topicideashow-to-vision-based-gesture-recognition?pid=76528

topicideashow-to-facial-gesture-recognition-using-correlation-and-mahalanobis-distance
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#9
21-09-2013, 12:37 AM

what are the advantages and dis advantages of gesture recognition?

and how does it work?
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