Hand Gesture Recognition System

More than 20 years after presenting his first interpretation of the mosaic from the House of Aion in a paper entitled “Uwagi na temat mozaiki z Domu Aiona w Nea Paphos (Cypr)” (Meander 9/10, 1987, p. 421-438, in Polish, and translated to English: « The Immortality of the Human Soul and the Dionysiac Mysteries in the 4th Century Allegory [in Connection with the Interpretation of the Mosaic from the “House of Aion” at Paphos in Cyprus]»), where he proposed reading the five mythological panels (1. Leda and Zeus; 2. Dionysus on Mount Nysa; 3. The Dionysian thiasos; 4. Apollo and Marsyas; 5. The Apotheosis of Cassiopeia) as an allegory of the destiny of the human soul and body, starting with conception and ending with the liberation of the soul and its apotheosis, the author now enhances his initial interpretation with numerous reflections concerning the historical context of this work of art, created in the years 320-360.
If, according to the author, the mosaic is a “Hymn” in honour of the pagan gods, Man (his soul) and his place in the (pagan) universe, then at the same time it polemicises with the Christian faith and its God, Jesus Christ.
The author puts forward numerous new solutions relating to the mosaic’s iconography and iconology. Analysis of the mosaic also leads him to propose a solution to the as-yet unanswered question regarding the absence of images of the crucified Christ in Christian art of the 3rd and 4th centuries, up until 420/430, when the first images of Christ on the cross appear.
CONTENTS:
Introduction
1. Research to-date (recap)
2. Description of mosaic (recap)
2.1. Composition of five mythological scenes
2.2. Brief iconographic description of mythological panels
2.3. Outline of individual iconographic elements and their respective roles
2.4. The pagan gods’ air of majesty and dignity as an expression of piety
3. A few words about the pagan reaction
4. An apology of pious human life according to pagan principles and a polemic on the Christian model of Man and his theological idol, Jesus Christ
4.1. Divine intervention in the act of human conception (allusion to/polemic on the Annunciation to the Virgin Mary)
4.2. The Epiphany of Dionysus (an allusion to the Epiphany of Christ – Theou Pais)
4.3. The procession and dissemination of Dionysian order (allusion to/polemic on Christ’s entry into Jerusalem)
4.4. The error of Marsyas (polemic on/allusion to the error of Jesus the Messiah): arrest, sentencing for arrogance and lack of piety (asebeia) and condemnation to a martyr’s death
4.5. The anabasis (or anagoge psyches) of a ‘beautiful soul’ (allusion to/polemic on the Ascension of Christ)
5. A few words on the mosaic programme prior to final conclusions
Final conclusions
Key words: mythological mosaic, House of Aion, Nea Paphos, Late Antiquity, Roman art, allegorical narrative, allusive language of mythological pictures, wordplay (Marsyas-Messias), Jesus Christ, anti-Christian polemic, Dionysos, Cassiopeia, Apollon, Nereides, Abstract Personifications, Greek Mythology, ΚΥΠΡΟΣ

In this era of touch screen devices, human machine interaction is a very important factor nowadays. Many devices are developed which can be operated without touching the system. So, in this project we are going to demonstrate how one can operate the system without actually touching the system but with the use of gestures. Human-Computer Interaction (HCI) can be described as the point of communication between the human user and a computer. Commonly used input devices include the following: keyboard, computer mouse, trackball, touchpad and a touch-screen. All these devices require manual control and cannot be used by persons impaired in movement capacity. Therefore, there is a necessity for developing various strategies of communication between human and personal computers that would be required for the persons with motor impairments and would enable them to become a part of the Information Society. In this project we will be implementing a system which will include Hand gesture recognition approach for ASL language-Sign language being a method used for communicational purposes by deaf people. We will discuss the different steps used to provide the system with the required information, to identify the provided data and further analyse the hand gestures making use of various algorithms. Hence, this system will give them a new way to interact with the computer world and the objective of connecting the specially abled with the computer world will be sufficed with the help of this project.

—In this paper, a smart wireless wristband is proposed. The potential of innovative gesture based interactivity with connected lighting solutions is reviewed. The solution is intended to offer numerous benefits, in terms of ease of use, and enhanced dynamic interactive functionality. A comparative analysis will be carried out between this work and existing solutions. The evolution of lighting and gesture controls will be discussed and an overview of alternative applications will be provided, as part of the critical analysis.

Hand gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this paper a survey of recent hand gesture recognition systems is presented. Key issues of hand gesture recognition system are presented with challenges of gesture system. Review methods of recent postures and gestures recognition system presented as well. Summary of research results of hand gesture methods, databases, and comparison between main gesture recognition phases are also given. Advantages
and drawbacks of the discussed systems are explained finally.

Kinect sensor is being used for the motion control of a mobile robot according to the movements of hands (gesture), as a ubiquitous human machine interface. The robot is in the form of a loader vehicle, which can be used for transportation of goods. After decision making based on gesture recognition, the corresponding command is sent wirelessly to the robot and action is performed according to the movement of the hands. Moreover, Graphical User Interference (GUI) for monitoring and controlling the robot using computer is also implemented in this project. Obstacles avoidance is being used to avoid hurdles that come across the robot’s path using sonar sensors. The robot can also be controlled using speech recognition.

Sign language is a tool used by deaf for communication. They use different gestures to express their thoughts and communicate to people. Each gestures or movement of the hand has a special assigned meaning. Previous studies were conducted on hand gestures using Kinect camera, detection gloves, and leap motion controller to improve the accuracy in detection of the implied meanings. This study aims to develop a model that would increase the accuracy rate of detection, using a customized camera that addresses the background and lighting conditions. Moreover, the study will provide learning to those people who are not familiar in sign languages. The model were tested to 28 gestures together with the forming of words, it acquired an accuracy rate of 94.49%.

Gesture is the most primitive technique for conversation amongst human being. Today in the time of current day innovation gesture recognition affects the world differently, from the physically challenged individuals to robotic manage to virtual fact conditions. Human hand gestures provide the natural and advantageous approach of non-verbal conversation with the computer interface. Hand gestures are the considerable body moves that are actions of hands, arms or fingers. Hand gesture recognizable proof levels from the static gesture with the complicated foundation or dynamic gestures that express the human feeling and communicate with computer or humans. The hand is specifically use as the contribution to the machine, for the verbal exchange reason for gesture identification there is no need of an intermediate medium. In this paper, a deep convolution neural network is proposed to immediately classify hand gestures in pictures without any segmentation or detection stage that could discard the irrelevant not-hand area.

Hand gestures can be used for natural and intuitive human-computer interaction. To achieve this goal, computers should be able to visually recognize hand gestures from video input. However, vision-based hand tracking and gesture recognition is an extremely challenging problem due to the complexity of hand gestures, which are rich in diversities due to high degrees of freedom involved by the human hand. On the other hand, computer vision algorithms are notoriously brittle and computation intensive, which make most current gesture recognition systems fragile and inefficient.

We intend to develop a gesture based mouse and application controller which we named "G-MAC". Our system will be developed with two major visions in mind:(1) to build a virtual mouse system which will virtually replace all the major mouse functionalities and (2) to build a gesture application which enables us to control various applications via hand gestures. Our Gesture Application will consists of two types of gesture recognition:(1) static gesture recognition which will open up an application based on matched gestures from image database, and (2) a simple linear swipe based motion gesture which will enable us to control selected application such as \Picasa" to move to next/previous images and even zoom in/out. The ultimate goal is to build a gesture based interface which will
be more intuitive and flexible for users.

In this work, a vision-based approach is used to build a dynamic hand gesture recognition system. Various challenges such as complicated background, change in illumination and occlusion make the detection and tracking of hand difficult in any vision-based approaches. To overcome such challenges, a hand detection technique is developed by combining three-frame differencing and skin filtering. The three-frame differencing is performed for both colored and grayscale frames. The hand is then tracked using modified Kanade–Lucas–Tomasi feature tracker where the features were selected using the compact criteria. Velocity and orientation information were added to remove the redundant feature points. Finally, color cue information is used to locate the final hand region in the tracked region. During the feature extraction, 44 features were selected from the existing literatures. Using all the features could lead to overfitting, information redundancy and dimension disaster. Thus, a system with optimal features was selected using analysis of variance combined with incremental feature selection. These selected features were then fed as an input to the ANN, SVM and kNN model. These individual classifiers were combined to produce classifier fusion model. Fivefold cross-validation has been used to evaluate the performance of the proposed model. Based on the experimental results, it may be concluded that classifier fusion provides satisfactory results (92.23 %) compared to other individual classifiers. One-way analysis of variance test, Friedman's test and Kruskal– Wallis test have also been conducted to validate the statistical significance of the results.

This ‘work in progress’ paper presents a framework, a analysis and a design of a set of mobile phone based prototypes. These prototypes have been used to evaluate mobile interaction in smart environments. Other work has shown that mobile phones can act as a universal remote control for the interaction with smart objects but, there has been no research which has analyzed when a given mobile interaction technique should be used. In this research we analyze the appropriateness of three interaction techniques as selection techniques in smart environments, namely Scanning, Pointing and Touching. Our main goal is to understand in which context of use each of these techniques is useful. In support of this aim we have developed a prototype to test Scanning, Pointing and Touching interaction techniques within different scenarios of use. The development process was divided in an analysis, a design and an implementation phase: during all these phases the end user has been involved by questionnaires, interviews, prototyping, and user tests. The results of the finished analysis and design phases are described in this work together with the progress of the implementation phase.

Sign language detection and recognition (SLDR) using computer vision is a very challenging task. In respect to Bangladesh, sign language users are around 2.4 million [16]. In this paper, we try to focus for communicating with those users by computer vision. In this respect, an efficient method is proposed consists of some significant steps and they are, skin detection, preprocessing, different machine learning techniques like PCA and LDA, neural network for training and testing purpose of the system. Various hand sign images are used to test the proposed method and results are presented to provide its effectiveness.

In this paper, we present a smart hand gesture recognition experimental set up for collaborative robots using a Faster R-CNN object detector to find the accurate position of the hands in the RGB images taken from a Kinect v2 camera. We used MATLAB to code the detector and a purposely designed function for the prediction phase, necessary for detecting static gestures in the way we have defined them. We performed a number of experiments with different datasets to evaluate the performances of the model in different situations: a basic hand gestures dataset with four gestures performed by the combination of both hands, a dataset where the actors wear skin-like color clothes while performing the gestures, a dataset where the actors wear light-blue gloves and a dataset similar to the first one but with the camera placed close to the operator. The same tests have been conducted in a situation where also the face of the operator was detected by the algorithm, in order to improve the prediction accuracy. Our experiments show that the best model accuracy and F1-Score are achieved by the complete model without the face detection. We tested the model in real-time, achieving good performances that can lead to real-time human-robot interaction, being the inference time around 0.2 seconds.

Hand detection is one of the most important and crucial step in human computer interaction environment. This paper presents a distance technique for hand detection based on depth image information get from Kinect sensor. Distance was used as the method of this study for hand detection. First, Kinect sensor was used to obtain depth image information. Second, background subtraction and iterative method for shadow removal applied to reduce noise from the depth image. Then, it used the official Microsoft SDK for extraction process. Finally, two hands could be segmented based on different color in specific distance. The experiment result shows that hands and head can be detected in a different position with good accuracy.

Embodied image schemas are central to experientialist accounts of meaning-making. Research from several disciplines has evidenced their pervasiveness in motivating form and meaning in both literal and figurative expressions across diverse semiotic systems and art forms (e.g., Gibbs and Colston; Hampe; Johnson; Lakoff; and Mandler). This paper aims to highlight structural similarities between, on the one hand, dynamic image schemas and force schemas and, on the other, hand shapes and gestural movements. Such flexible correspondences between conceptual and gestural schematicity are assumed to partly stem from experiential bases shared by incrementally internalized conceptual structures and the repeated gestural (re-) enacting of bodily actions as well as more abstract semantic primitives (Lakoff). Gestures typically consist of evanescent, metonymically reduced hand configurations, motion onsets, or movement traces that minimally suggest, for instance, a PATH, the idea of CONTAINMENT, an IN-OUT spatial relation, or the momentary loss of emotional BALANCE. So, while physical in nature, gestures often emerge as rather schematic gestalts that, as such, have the capacity to vividly convey essential semantic and pragmatic aspects of high relevance to the speaker. It is further argued that gesturally instantiated image schemas and force dynamics are inherently meaningful structures that typically underlie more complex semantic and pragmatic processes involving, for instance, metonymy, metaphor, and frames. First, I discuss previous work on how image schemas, force gestalts, and mimetic schemas may underpin hand gestures and body postures. Drawing on Gibbs' dynamic systems account of image schemas, I then introduce an array of tendencies in gestural image schema enactments: body-inherent/self-oriented (body as image-schematic structure; forces acting upon the body); environment-oriented (material culture including spatial structures), and interlocutor-oriented (intersubjective understanding). Adopting a dynamic systems perspective (e.g.,Thompson and Varela) thus puts the focus on how image schemas and force gestalts that operate in gesture may function as cognitive-semiotic organizing principles that underpin a) the physical and cognitive self-regulation of speakers; b) how they interact with the (virtual) environment while talking; and c) intersub-jective instances of resonance and understanding between interlocutors or between an artwork and its beholder. Examples of these patterns are enriched by video and motion-capture data, showing how numeric kinetic data allow one to measure the temporal and spatial dimensions of gestural articulations and to visualize movement traces.

Nowadays people are used to get daily information
from Internet such as weather condition, news and financial
information, among others. Though, in order to receiving
these daily information, users have to repeat same mouse and
keyboard actions, inducing waste of time and inconvenience.
In order to improve these situations, we propose in this paper
a system design that can easily get daily information without
mouse and keyboard actions and make people's life more
convenient and easier. In this proposed system, we have
implemented an approach that provides daily information
retrieved from Internet, where users can operate this system
with his hands’ movements. Once selected the function by
hand gestures, the system will report action information to
users by synthetized speech. In  a typical family, since each
member has different requirements and needs, the system
utilizes face recognition to identify each user, bringing up
personalized services to each user.
In this paper, we use PCA method to recognize faces as
also hand gestures, and then a number of hand gestures and
system controls are acquired and stored into this system.
Results from a set of experiments indicate that the proposed
system in a family environment with small-scale of face
recognition show good performance as also good result in
hand gesture recognition

This dissertation presents the theory and application of Gesture Form Analysis, which systematises necessary processes of spatial conceptualisation for understanding a specific gesture type (pointing, drawing, representing etc.). Eight gesture form operations are proposed, which are combined in a modular fashion. They are applied in the analysis of gestures of the dissertation corpus and of other literature. The gesture type Pointing At Location is composed of the operations Articulator Profiling (e.g., straight extended arm, hand and index), Shape Profiling (the predominant axis, a line, is profiled), Continuation (the line is continued, indicating a direction), and Intersection (the line intersects with the surface of an object in a point: the location pointed at). The gesture type Drawing—while otherwise containing the same operations as Pointing At Location—requires adding the operation of Trace Leaving (by moving the arm, and hence all the other forms created by the above operations, the point leaves a trace in form of a line). The geometrical operations of Gesture Form Analysis (and the constituent forms specified in parentheses) consider specifically the spatial modality of gesture, taking a first step towards a strictly form-based gesture semantics. The systematic analysis of operations across gesture types results in a gesture typology, in which the dimensions of all constituent forms are defined; thus being open to computational application. The fundamental theoretical distinction of gesture form being non-equivalent to the mere physical form of the articulators (articulator form) is supported by an object-description study in an optical motion-capture system. The methods include operationalising three-dimensional gesture data, for example by a plugin for the video-annotation software Elan.

Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm, which is portable to carry various places and which that can be easily programmed to do any work similar to that of a human hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated Tthe active spatio-temporal feature and the corresponding pose parameter for various hand movements are estimated, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawnis evaluated in this paper. This helps inBy analyzing the movement of a human hand and its fingers, this improves ability to which can be made to control a robotic arm with high recognition rate and less error rate.

With rapid development of 3-D applications & virtual environments in computer systems the need for a new type of interaction device arises. In other words, evolution of user interfaces shapes the change in the Human-Computer Interaction (HCI). “Hand Gesture” concept in human computer interaction, which has become popular in recent years can be used to develop such an interaction device. This paper presents an overview of real time , vision based hand gesture recognition system that works precisely on a relatively small-restricted gesture space for single user robot control, track a player’s hand or body position to control movement and orientation of interactive game , and human-computer interaction.

Hand gesture is an innovative technique of interaction between human and computer. As compared to existing techniques, hand gesture technique has the advantage of being easy to use. By using this technique the traditional way of using mouse and keyboard will be change because one can then interact with the computer with hand gestures. In this technique, ultrasonic sensor is used to classify the hand movement in real-time. Ultrasonic sensor measures the distance of hand by using sound waves. The main idea of our approach is to speed up the interaction with computer, using general purpose hardware like personal computer, Arduino UNO board and low cost sensor like ultrasonic sensor. In this way any user can easily interact with the computer using hand gestures.

— Sign language recognition is one of the most growing fields of research today and it is the most natural way of communication for the people with hearing problems. A hand gesture recognition system can provide an opportunity for deaf persons to communicate with normal people without the need of an interpreter or intermediate. We are going to build a systems and methods for the automatic recognition of Marathi sign language. Through that we are providing teaching classes for the purpose of training the deaf sign user in Marathi. The system does require hand to be properly aligned to the camera and does not need any special colour markers, glove or wearable sensors. A large set of samples has been used in proposed system to recognize isolated words from the standard Marathi sign language which are taken in front of camera by different deaf sign user. In our proposed system, we intend to recognize some very basic elements of sign language and to translate them to text and vice versa. Keywords— Sign Language, Deaf Sign User, Sign and Text Database, Canny's Algorithm.

Dalam mempelajari bangun ruang seringkali pelajar Sekolah Dasar mengalami kesulitan pemahaman terhadap materi yang diberikan. Maka diperlukan suatu alat peraga interaktif dan menarik dalam bentuk aplikasi multimedia Pengenalan Bangun Ruang supaya dapat meningkatkan minat belajar dan pemahaman siswa-siswi dalam mempelajari materi bangun ruang. Penulis menggunakan teknologi Leap Motion Controller untuk mengimplementasikan Hand Gesture Recognition yaitu pengembangan dari Hand Motion Tracking. Dengan demikian, aplikasi dapat dikontrol dengan menggunakan gerakan tangan tanpa keyboard ataupun mouse. Penulis menemukan bahwa variasi dan persentasi ketepatan gestur pada aplikasi-aplikasi sebelumnya belum optimal. Dengan menggunakan metode Multimedia Development Life Cycle (MDLC), penelitian ini bertujuan menambah variasi jenis gestur yang digunakan serta meningkatkan persentasi ketepatan dalam mengenal gestur. Hasil dari penelitian adalah aplikasi dengan fitur 10 jenis definisi gestur, yaitu: Tap, Swipe, Clap, Double Outward Swipe, Circular, Fly Control, Pinch, Release, Push dan Pull. Ketepatan penerjemahan gestur mencapai 91.6% pada frame rate 30 fps dan eksekusi CPU sebesar 45,53 ms.

Gesture Recognition pertains to the process which includes understanding and interpreting meaningful movements of the body parts such as arms, face, hands or head. It allows us to talk to the machines and use them with means other than electrical devices. The technique also provides us an innovative, efficient and user-friendly way of interaction to which we are, sort of, more familiar. To convey information, the use of gestures can be quite useful in communication. This way gesture recognition plays a crucial role in our daily life. Gesture Recognition has uncountable uses, such as sign-language, machinery, robotics, computer games, virtual reality and much more. This proves that gesture recognition can be the key research area for Scientists. Hand gestures are of 2 types, static and dynamic. The three main phases of the technique on which the whole approach works are object detection, object tracking and recognizing the gesture. We developed a fast and simple calculator based on hand gesture recognition system. It uses gestures as inputs and performs calculations according to these gestures. The objective of the paper is to make the interaction between humans and computers as natural as the interaction between humans.

An increasing number of today's consumer devices such as mobile phones or tablet computers are equipped with various sensors. The extraction of useful information such as gestures from sensor-generated data based on mainstream imperative languages is a notoriously difficult task. Over the last few years, a number of domain-specific programming languages have been proposed to ease the development of gesture detection. Most of these languages have adopted a declarative approach allowing programmers to describe their gestures rather than having to manually maintain a history of event data and intermediate gesture results. While these declarative languages represent a clear advancement in gesture detection, a number of issues are still unresolved. In this paper we present relevant criteria for gesture detection and provide an initial classification of existing solutions based on these criteria in order to foster a discussion and identify opportunities for future gesture programming languages.

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

Abstract— We propose a new technique for recognition of dumb person hand gesture in real world environment. In this technique, the hand image containing the gesture is preprocessed and then hand region is segmented by convergent the RGB color image to L.a.b color space. Only few statistical features are used to classify the segmented image to different classes. Artificial Neural Network is trained in sequential manner using one against all. When the system gets trained, it becomes capable of recognition of each class in parallel manner. The result of proposed technique is much better than existing techniques.

Keywords-Gesture Recognition; Segmentation; Morphology Modified Directional Weighted Median Filter (MDWMF); Random value Impulse Noise (RVIN)

The fundamental aim of the project is to develop a robotic arm that can be handled by a normal layman to complete the specific task according to the needs of the customers. In this proposed method, the system was designed and implemented, a prototype was developed to demonstrate the proposed system. The performance of the developed proposed system is evaluated by testing the accuracy of the gesture response, testing the range of the radio communication device, accuracy test in multiple ranges, resistance of the flex sensor in different angels, Recognition Rate and Recognition Time. It is observed that the implemented system has more movement freedom with the navigation platform and a day and night vision compared with the existing products. On the existing systems, most of them lacks on the ability to navigate around the operating space, meanwhile the implemented system contains a gesture-controlled navigation platform. Also, most of the existing systems lack on providing a monitoring feature to monitor the process of the robot from a distance where they only designed the system to be operated within the range of vision while the implemented system offers the feature of operating it remotely.

A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also defined.

The use of hand gesture recognition has been steadily growing in various human-computer interaction applications. Under realistic operating conditions, it has been shown that hand gesture recognition systems exhibit recognition rate limitations when using a single sensor. Two dual-sensor approaches have thus been developed in this dissertation in order to improve the performance of hand gesture recognition under realistic operating conditions. The first approach involves the use of image pairs from a stereo camera setup by merging the image information from the left and right camera, while the second approach involves the use of a Kinect depth camera and an inertial sensor by fusing differing modality data within the framework of a hidden Markov model. The emphasis of this dissertation has been on system building and practical deployment. More specifically, the major contributions of the dissertation are: (a) improvement of hand gestures recognition rates when using a pair of images from a stereo camera compared to when using a single image by fusing the information from the left and right images in a complementary manner, and (b) improvement of hand gestures recognition rates when using a dual-modality sensor setup consisting of a Kinect depth camera and an inertial body sensor compared to the situations when each sensor is used individually on its own. Experimental results obtained indicate that the developed approaches generate higher recognition rates in different backgrounds and lighting conditions compared to the situations when an individual sensor is used. Both approaches are designed such that the entire recognition system runs in real-time on PC platform.

This issue (DOI: 10.13140/RG.2.2.13132.92807) includes the following articles; P1151705543, author="Tieling Chen", title="Detail Preserving Sorted Difference Filter", P1151738585, author="Vladimir A. Kulyukin", title="GreedyHaarSpiker: An Algorithm for In Situ Detection of Highway Lane Boundaries with 1D Haar Wavelet Spikes", P1151714561, author="Poorva Waingankar and Sangeeta Joshi", title="Video Compression using Efficient Encoding Techniques for Low Bit Rate Applications", P1151733581, author="Ayush Purohit and Shardul Singh Chauhan", title="A Precise Technique for Hand Gesture Recognition", P1151748594, author="D. Boopathy and M. Sundaresan", title="Securing Images on Cloud using Multidimensional Approach", P1151747592, author="M. Anyayahan and M. Balinas and A. La Madrid and M. Laurel and C. Lopez and R. Tolentino", title="Rotational invariant Real Time Text Recognition",

This paper presents a recognition system, which can be helpful for a blind person. Hand gesture recognition system and face recognition system has been implemented in this paper using which various tasks can be performed. Dynamic images are being taken from a dynamic video and is being processed according to certain algorithms. In the Hand gesture system Skin color detection has been done in YCbCr color space and to discover hand convex defect character point of hand is used where different features like fingertips, angle between fingers are being extracted. According to gesture Recognized, various tasks can be performed like turning on the fan or lights. While in face recognition, Haar Cascade Classifiers and LBPH recognizer are being used for face detection and recognition respectively. With the help of OpenCV, The research has been implemented. Various hand gestures and human faces have been detected and identified using this system. The hand gesture was recognized with an accuracy of 95.2% was achieved and facial recognition was done with an accuracy of 92%.

Dynamic gesture recognition has been studied
actually for it big application in several areas such as virtual
reality, games and sign language. But some problems have to
be solved in computer applications, such as response time and
classification rate, which directly affect the real-time usage.
This paper proposes a novel algorithm called Convex Invariant
Position Based on Ransac which improved the good results in
dynamic gesture recognition problem. The proposed method is
combined with a adapted PSO variation to reduce features and
a HMM and three DTW variations as classifiers

Today world is running behind the computer industries and pattern recognition is one of the important and vast fields of computer intelligence. Gesture recognition is one of the applications of Pattern recognition and further hand gesture recognition; hand gesture recognition system can be used as an interface between human hand and computer. Our technique provides a human hand interface with computer which can recognize static gestures from American Sign Language. Since 24 gestures from American Sign Language (ASL) are static so, we was able to recognize them. Our objective is to develop a hand gesture recognition system which can recognize most of the static characters from ASL with a good accuracy which can only work offline and is mainly dependent on database.

This article describes the development of DSP as the core of the face recognition system, on the basis of understanding the background, significance and current research situation at home and abroad of face recognition issue, having a in-depth study to face detection, Image preprocessing, feature extraction face facial structure, facial expression feature extraction, classification and other issues during face recognition and have achieved research and development of DSP-based face recognition system for robotic rehabilitation nursing beds. The system uses a fixed-point DSP TMS320DM642 as a central processing unit, with a strong processing performance, high flexibility and programmability.

Hand gesture recognition can substitute the use of text-entry interface for human computer interaction. However, it is a challenging task to develop a virtual text-entry interface covering a large number of gesture-based characters. In this paper, 18 new ASCII printable characters have been introduced along with some of the previously introduced characters [A–Z alphabets, 0–9 numbers and four arithmetic operators (add, minus, multiply, divide)]. In addition to some of the efficient existing features , three new features of 15 dimensions have been incorporated to enhance the performance of the system, which are normalized distance between direction extreme, close figure test and direction change ratio. These features are measured for single-stroke as well as multistroke gestures. An experimental analysis has been carried out for selection of optimal features using the statistical analysis techniques such as one-way analysis of variance test, Kruskal–Wallis test, Friedman test in combination with incremental feature selection technique. Furthermore, a comparative study has been carried out for classification of 58 gestures with the new list of features. A comparative analysis has been performed using five classifiers, namely SVM, kNN, Naı¨ve Bayes, ANN and ELM. It has been observed that maximum accuracy achieved using the combination of existing and proposed features is 96.95%, as compared to 94.60% accuracy achieved using existing features for classification of 58 gestures.

This paper describes the development of a low-cost mini-robot that is controlled by visual gestures. The prototype allows a person with disabilities to perform visual inspections indoors and in domestic spaces. Such a device could be used as the operator's eyes obviating the need for him to move about. The robot is equipped with a motorised webcam that is also controlled by visual gestures. This camera is used to monitor tasks in the home using the mini-robot while the operator remains quiet and motionless. The prototype was evaluated through several experiments testing the ability to use the mini-robot's kinematics and communication systems to make it follow certain paths. The mini-robot can be programmed with specific orders and can be tele-operated by means of 3D hand gestures to enable the operator to perform movements and monitor tasks from a distance.

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A da-taset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Gesture recognition represents the silent language that can be done with robots as well as they done to us, this overseas language ensures that everyone can understand the meaning of the gesturing as well as can reply and interact with. Because of that this silent language has chosen for deaf people in which can make their communication easier between each of them as well as with other people. In this paper we have brought to the table two different outstanding gesture recognition systems, those two techniques achieved high ratio of recognition percentage as well as that are invariant-free techniques, especially rotation perturbation that hinders the achievement of high level recognition percentage, the first method is the recognition of hand gesture with the help of dynamic circle template and second one using variable length chromosome generic algorithm, these two methods has been applied to different people and the main objective was to reduce the database size used for training.

The recently developed depth sensors such as Kinect have provided new potentials for human-computer interaction (HCI) and hand gestures are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is proposed to segment hand blobs using both depth and color data frames. This method applies a body segmentation and an image thresholding techniques to depth data frame using skeleton data and concurrently it uses SLIC super-pixel segmentation method to extract hand blobs from color data frame with the help of skeleton data. Finally, two segmented blobs are combined to improve the final result by assuming that hands are located in front of the body. The proposed method has low computation time and shows significant result when the basic assumptions are fulfilled.

Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where sample data were considered with different illumination changes, different skin color and varying distance from camera to signer position.

A recent boom has been seen in 3D virtual worlds for entertainment, and this in turn has led to a surge of interest in their educational applications. Although booming development  has  been  seen,  most  of  them  only  strengthen the traditional teaching methods using a new platform without changing the nature of how to teach and learn. Modern computer science technology should be applied in STEM education for the purpose of rising learning efficiency and interests. In this paper, we focus on the reasoning, design, and implementation of a 3D virtual learning system that merges STEM experiments into virtual laboratory and brings entertainment to knowledge learning. An advanced hand gesture interface was introduced to enable flexible manipulation on virtual objects with two hands. The recognition ability of single hand grasping-moving-rotating activity (SH-GMR) allows single hand to move and rotate a virtual object at the same time. We implemented several virtual experiments in the VR environment to demonstrate  to  the  public  that  the  proposed  system  is  a  powerful  tool  for STEM education. The benefits of this system are evaluated followed by two virtual experiments in STEM field.

The hand gesture input systems will be adopted for use in many applications anytime and anywhere. In this research, a stating notification when and where a user can start using hand gestures for input are focused. However existing devices and systems could be used only in certain situations because of safety issues and the limitations of the devices.

In this paper, a Wireless Nail Gesture System is suggestted and we developed a proof-of-concept model of
the Wireless Nail Gesture System that can be installed on artificial nails using an LED and a coil. The LED light notifies whether the finger is within a RF(Radio Frequency) gesture space that is the magnetic field generated by a transmission antenna. The RF supplies the power and the signal. The greatest benefit of this system is that users can wear the device anytime and anywhere. In addition, by measuring the impedance
of the transmission antenna, the system distinguishes three states: (1) outside the gesture space, (2) ON gesturewithin the gesture space and (3) OFF gesture within the gesture space. For example, to indicate the ON gesture, the user covers the LED on the fingernail with another finger. 

A usability study of thesuggested system determined how the notification system affected the users’ success rate and speed at searching for a hand gesture space. If the system does not notify the user to start, the task success rate drops to 85%. If the system notifies the user to start, the task success rate is 100% in all sessions. The experiment also revealed that the start notification reduces the task time by 0.7s from 22.1s.

Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.

This paper presents a comparison of two real-time hand gesture recognition systems. One system utilizes a binocular stereo camera set-up while the other system utilizes a combination of a depth camera and an inertial sensor. The latter system is a dual-modality system as it utilizes two different types of sensors. These systems have been previously developed in the Signal and Image Processing Laboratory at the University of Texas at Dallas and the details of the algorithms deployed in these systems are reported in previous papers. In this paper, a comparison is carried out between these two real-time systems in order to examine which system performs better for the same set of hand gestures under realistic conditions.

This paper presents a framework for spotting and recognizing continuous human gestures. Skeleton based features are extracted from normalized human body coordinates to represent gestures. These features are then used to construct spatio-temporal template based Random Decision Forest models. Finally, predictions from different models are fused at decision-level to improve overall recognition performance. Our method has shown competitive results on the ChaLearn 2014 Looking at People: Gesture Recognition dataset. Trained on a dataset of 20 gesture vocabulary and 7754 gesture samples, our method achieved a Jaccard Index of 0.74663 on the test set, reaching 7th place among contenders. Among methods that exclusively used skeleton based features, our method obtained the highest recognition performance.

The main aim of Sixth Sense Technology has always been the to reduce the distance between our physical world and digital world. This has been done in numerous ways. The purpose of this paper is to present one such ways of bringing close the two worlds. Numerous techniques for color recognition and detection have been proposed during several past years. A few papers have also been published which compares different approaches of color detection and virtual mouse functions. This paper proposes a method of using mouse pointers and all of its functions through hand gestures and color recognition with cheap and minimal hardware components.