16-01-2013, 10:44 AM
A Study on Hand Gesture Recognition Technique
[attachment=47089]
ABSTARCT
Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy.
The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation.
Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels.
INTRODUCTION
HUMAN COMPUTER INTERFACE SYSTEM
Computer is used by many people either at their work or in their spare-time. Special input and output devices have been designed over the years with the purpose of easing the communication between computers and humans, the two most known are the keyboard and mouse [1]. Every new device can be seen as an attempt to make the computer more intelligent and making humans able to perform more complicated communication with the computer. This has been possible due to the result oriented efforts made by computer professionals for creating successful human computer interfaces [1]. As the complexities of human needs have turned into many folds and continues to grow so, the need for Complex programming ability and intuitiveness are critical attributes of computer programmers to survive in a competitive environment. The computer programmers have been incredibly successful in easing the communication between computers and human. With the emergence of every new product in the market; it attempts to ease the complexity of jobs performed. For instance, it has helped in facilitating tele operating, robotic use, better human control over complex work systems like cars, planes and monitoring systems. Earlier, Computer programmers were avoiding such kind of complex programs as the focus was more on speed than other modifiable features. However, a shift towards a user friendly environment has driven them to revisit the focus area [1].
GESTURES
It is hard to settle on a specific useful definition of gestures due to its wide variety of applications and a statement can only specify a particular domain of gestures. Many researchers had tried to define gestures but their actual meaning is still arbitrary.
Bobick and Wilson [2] have defined gestures as the motion of the body that is intended to communicate with other agents. For a successful communication, a sender and a receiver must have the same set of information for a particular gesture.
As per the context of the project, gesture is defined as an expressive movement of body parts which has a particular message, to be communicated precisely between a sender and a receiver. A gesture is scientifically categorized into two distinctive categories: dynamic and static [1].
A dynamic gesture is intended to change over a period of time whereas a static gesture is observed at the spurt of time. A waving hand means goodbye is an example of dynamic gesture and the stop sign is an example of static gesture. To understand a full message, it is necessary to interpret all the static and dynamic gestures over a period of time. This complex process is called gesture recognition. Gesture recognition is the process of recognizing and interpreting a stream continuous sequential gesture from the given set of input data.
DATABSE DESCRIPTION
In this project all operations are performed on gray scale image .We have taken hand gesture database from [20].The database consist of 25 hand gesture of International sign language. The letter j,z and have been discard for their dynamic content. Gesture ae is produced as it is a static gesture .The system works offline recognition ie. We give test image as input to the system and system tells us which gesture image we have given as input. The system is purely data dependent.
We take gray scale image here for ease of segmentation problem. A uniform black background is placed behind the performer to cover all of the workspace. The user is required to wear a black bandage around the arm reaching from the wrist to the shoulder. By covering the arm in a color similar to the background the segmentation process is fairly straight forward.
A low-cost black and white camera is used to capture the hand gesture performed by performer .it produces 8-bit gray level image. The resolution of grabbed image is 256*248. Each of the gestures/signs is performed in front of a dark background and the user's arm is covered with a similar black piece of cloth, hence easy segmentation of the hand is possible. Each gesture is performed at various scales, translations, and a rotation in the plane parallel to the image-plane [20].There are total 1000 images, 40 images per gesture.
[attachment=47089]
ABSTARCT
Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy.
The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation.
Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels.
INTRODUCTION
HUMAN COMPUTER INTERFACE SYSTEM
Computer is used by many people either at their work or in their spare-time. Special input and output devices have been designed over the years with the purpose of easing the communication between computers and humans, the two most known are the keyboard and mouse [1]. Every new device can be seen as an attempt to make the computer more intelligent and making humans able to perform more complicated communication with the computer. This has been possible due to the result oriented efforts made by computer professionals for creating successful human computer interfaces [1]. As the complexities of human needs have turned into many folds and continues to grow so, the need for Complex programming ability and intuitiveness are critical attributes of computer programmers to survive in a competitive environment. The computer programmers have been incredibly successful in easing the communication between computers and human. With the emergence of every new product in the market; it attempts to ease the complexity of jobs performed. For instance, it has helped in facilitating tele operating, robotic use, better human control over complex work systems like cars, planes and monitoring systems. Earlier, Computer programmers were avoiding such kind of complex programs as the focus was more on speed than other modifiable features. However, a shift towards a user friendly environment has driven them to revisit the focus area [1].
GESTURES
It is hard to settle on a specific useful definition of gestures due to its wide variety of applications and a statement can only specify a particular domain of gestures. Many researchers had tried to define gestures but their actual meaning is still arbitrary.
Bobick and Wilson [2] have defined gestures as the motion of the body that is intended to communicate with other agents. For a successful communication, a sender and a receiver must have the same set of information for a particular gesture.
As per the context of the project, gesture is defined as an expressive movement of body parts which has a particular message, to be communicated precisely between a sender and a receiver. A gesture is scientifically categorized into two distinctive categories: dynamic and static [1].
A dynamic gesture is intended to change over a period of time whereas a static gesture is observed at the spurt of time. A waving hand means goodbye is an example of dynamic gesture and the stop sign is an example of static gesture. To understand a full message, it is necessary to interpret all the static and dynamic gestures over a period of time. This complex process is called gesture recognition. Gesture recognition is the process of recognizing and interpreting a stream continuous sequential gesture from the given set of input data.
DATABSE DESCRIPTION
In this project all operations are performed on gray scale image .We have taken hand gesture database from [20].The database consist of 25 hand gesture of International sign language. The letter j,z and have been discard for their dynamic content. Gesture ae is produced as it is a static gesture .The system works offline recognition ie. We give test image as input to the system and system tells us which gesture image we have given as input. The system is purely data dependent.
We take gray scale image here for ease of segmentation problem. A uniform black background is placed behind the performer to cover all of the workspace. The user is required to wear a black bandage around the arm reaching from the wrist to the shoulder. By covering the arm in a color similar to the background the segmentation process is fairly straight forward.
A low-cost black and white camera is used to capture the hand gesture performed by performer .it produces 8-bit gray level image. The resolution of grabbed image is 256*248. Each of the gestures/signs is performed in front of a dark background and the user's arm is covered with a similar black piece of cloth, hence easy segmentation of the hand is possible. Each gesture is performed at various scales, translations, and a rotation in the plane parallel to the image-plane [20].There are total 1000 images, 40 images per gesture.