Top PDF Hand Gesture and Neural Network Based Human Computer Interface

Hand Gesture and Neural Network Based Human Computer  Interface

Hand Gesture and Neural Network Based Human Computer Interface

Computer is used by every people either at their work or at home. Our aim is to make computers that can understand human language and can develop a user friendly human computer interfaces (HCI). Human gestures are perceived by vision. The research is for determining human gestures to create an HCI. Coding of these gestures into machine language demands a complex programming algorithm. In this project, We have first detected, recognized and pre-processing the hand gestures by using General Method of recognition. Then We have found the recognized image’s properties and using this, mouse movement, click and VLC Media player controlling are done. After that we have done all these functions thing using neural network technique and compared with General recognition method. From this we can conclude that neural network technique is better than General Method of recognition. In this, I have shown the results based on neural network technique and comparison between neural network method & general method.
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Hand Gesture Based Wheelchair Movement Control for Disabled  Person Using MEMS.

Hand Gesture Based Wheelchair Movement Control for Disabled Person Using MEMS.

Micro-electromechanical systems (MEMS) are free scale‟s enabling technology for acceleration and pressure sensors. MEMS based sensor products provide an interface that can sense, process or control the surrounding environment. Micro-Electro- Mechanical Systems, or MEMS, is a technology that in its most general form can be defined as miniaturized mechanical and electro-mechanical elements (i.e., devices and structures) that are made using the techniques of micro fabrication.MEMS- based sensors are a class of devices that builds very small electrical and mechanical components on a single chip. MEMS-based sensors are a crucial component in automotive electronics, medical equipment, hard disk drives, computer peripherals, wireless devices and smart portable electronics such as cell phones and PDAs.The functional elements of MEMS are miniaturized structures, sensors, actuators, and microelectronics, the most notable (and perhaps most interesting) elements are the micro sensors and micro actuators. Micro sensors and micro actuators are appropriately categorized as “transducers”, which are defined as devices that convert energy from one form to another. In the case of micro sensors, the device typically converts a measured mechanical signal into an electrical signal.MEMS technology provides the following advantages: cost-efficiency, low power, miniaturization, high performance, and integration. Functionality can be integrated on the same silicon or in the same package, which reduces the component count. This contributes to overall
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A REVIEW ON THE DEVELOPMENT OF INDONESIAN SIGN LANGUAGE RECOGNITION SYSTEM

A REVIEW ON THE DEVELOPMENT OF INDONESIAN SIGN LANGUAGE RECOGNITION SYSTEM

Stergiopoulou and Papamarkos (2009) conducted a study on the static hand gesture recognition based Neural Gas Self-Growing and Self-Organized (SGONG) network. An input image using a digital camera for the detection of the hand area of YCbCr color space was applied and the threshold technique was used to detect skin tones. They uses the competitive Hebbian learning algorithm, which begins studying with two neurons. As the neurons grow the grid will detect the exact shape of the hand, with the specified number of fingers raised, however, in some cases the algorithm might lead to false classification. This problem is solved by applying the average finger length ratio. This method has the disadvantage that two fingers may be classified into the same class of the finger. This problem has been overcome by choosing the most likely combinations of fingers. This system can recognize the 31 movements that have been established with a recognition rate of 90.45% and 1.5 sec.
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 Improved ASL based Gesture Recognition using HMM for System Application

Improved ASL based Gesture Recognition using HMM for System Application

Abstract – Gesture recognition is a growing field of research and among various human computer interactions; hand gesture recognition is very popular for interacting between human and machines. It is non verbal way of communication and this research area is full of innovative approaches. This project aims at recognizing 34 basic static hand gestures based on American Sign Language (ASL) including alphabets as well as numbers (0 to 9). In this project we have not considered two alphabets i.e J and Z as our project aims as recognizing static hand gesture but according to ASL they are considered as dynamic. The main features used are optimization of the database using neural network and Hidden Markov Model (HMM). That is the algorithm is based on shape based features by keeping in the mind that shape of human hand is same for all human beings except in some situations.
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Automatic hand or head gesture interface for individuals with motor impairments, senior citizens and young children

Automatic hand or head gesture interface for individuals with motor impairments, senior citizens and young children

During the past years many interface systems based on tracking head and hand gestures have already been developed, providing simple and easy ways for people to interact with computers. However, most methods are based on morphological points of the member that will be used to make the gestures. In addition, most are very specific such that only a limited range of users can truly benefit from them. The ones based on head tracking require in most cases that the user can at least move his head freely, which unfortunately cannot be done by people with more severe motor impair- ments [1, 2]. For other users which do not have such impairments, the use of a hand to control the computer can be more comfortable and easy, but then a specific method for hand recognition is required [3, 4]. There also are systems which combine head and hand ges- tures [5, 6], the number of users being more restricted. Other major problems may be the overall cost of the system, a need for prior calibration, and hardware and software installation by a specialized technician.
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Comparative Analysis of Hand Gesture Recognition Techniques

Comparative Analysis of Hand Gesture Recognition Techniques

Image Processing is a deal with Pictorial information for human interpretation and examine [1]. Gesture Recognition is in Computer science and language technology with goal of interpreting human gesture via method and algorithms. Gesture is basically use for Non-verbal communication. Adroit gesture can add to the impact of a speech Gesture Clarity your ideas or rein force them and should be well suited to the audience and occasion. Gestures are being used in HCI many since many years. Earlier, hardware based gesture recognition was more prevalent. User had to wear gloves helmet and other heavy apparatus. Sensor actuator and accelerometer were used for gesture recognition. But the whole process was difficult in real time environment. Hand Gesture can be sub divided in to two types static and Dynamic as shown in Figure 1.1.
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Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient

Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient

Reza Azad was born in Ardebil, Iran, in 1989. He is now studying B.Sc. in university of Shahid Rajaee Teacher Training, Tehran, Iran, from 2011 until now in computer software engineering technology. He takes the fourth place at Iranian university entering exam. Also he’s a member of IEEE, member of elites of the country, top and Honor student in university. Hi have 7 papers in the international conference and 4 papers in international journal. In 2013 His two papers picked out as high level in science and innovation by the CITADIM 2013 and published in international high level scientific journal also He dominated as best researcher in 2013 by the SRTTU Computer faculty. As a Reviewer in the 3rd IEEE International Conference on Computer and Knowledge Engineering. His research interests include image processing, artificial intelligence, handwritten character recognition, skin detection, face detection and recognition, human tracking, pattern recognition, virtual reality, machine learning and localization of autonomous vehicles.
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Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Automatic analysis of humans and their actions has received increasingly more attention in the last decade. One of the areas of interest is recognition of human gestures, as these are frequently used as an intuitive and convenient way of communication in our daily life. Recognition of hand gestures can be widely applied in human-computer interfaces and interaction, games, robot control, augmented reality, etc. In computer vision there are numerous approaches for hand detection, tracking and gesture recognition, although to the best of our knowledge none is really biologically inspired. Kim et al. [7] presented a method for hand tracking and motion detection based on a sequence of stereo color frames. Bandera et al. [1] presented an approach to recognize gestures which are composed of tracked trajectories of differ- ent body parts, where each individual trajectory is described by a set of keypoints. Gestures are characterized through global properties of the trajectories which are involved. Suk et al. [17] explored a method for recognizing hand gestures in a continuous video stream based on a dynamic Bayesian network.
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Visual Interpretation Of Hand Gestures For Human Computer  Interaction

Visual Interpretation Of Hand Gestures For Human Computer Interaction

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. We propose pointing gesture-based large display interaction using a depth camera. A user interacts with applications for large display by using pointing gestures with the barehand. The calibration between large display and depth camera can be automatically performed by using RGB-D camera.. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. We discuss directions of future research in gesture recognition, including its integration with other natural modes of human computer interaction.
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Human -Computer Interface using Gestures based on Neural Network

Human -Computer Interface using Gestures based on Neural Network

Abstract- Gestures are powerful tools for non-verbal communication. Human computer interface (HCI) is a growing field which reduces the complexity of interaction between human and machine in which gestures are used for conveying information or controlling the machine. In the present paper, static hand gestures are utilized for this purpose. The paper presents a novel technique of recognizing hand gestures i.e. A-Z alphabets, 0-9 numbers and 6 additional control signals (for keyboard and mouse control) by extracting various features of hand ,creating a feature vector table and training a neural network. The proposed work has a recognition rate of 99%.
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Hand gestures mouse cursor control

Hand gestures mouse cursor control

The paper describes the implementation of a human-computer interface for controlling the mouse cursor. The test reveal the fact: a low-cost web camera some processing algorithms are quite enough to control the mouse cursor on computers. Even if the system is influenced by the illuminance level on the plane of the hand, the current study may represent a start point for some studies on the hand tracking and gesture recognition field.

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Information and Communication Technology as a Provider of Food Security: Design of an Expert System to Assist in Communication where Non-Audible Communication is Expedient

Information and Communication Technology as a Provider of Food Security: Design of an Expert System to Assist in Communication where Non-Audible Communication is Expedient

To work out the logical consequence of all the rules in the knowledge base, a platform independent tool, C++ was adopted. Because the NN-modelled ES requires little information and can retrain itself with any current situation to take further decisions, an algorithm was developed to concentrate menus or services into requests or responses and display them as speech made by our targeted customers. As remarked in [8], since neural networks do not require any thinking pattern to be specified, explicitly only two sets of data are required from the ideal customer – service relationship exhibited in any food joint. These are all the inputs to the system and the correct output corresponding to the input values.
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Portuguese sign language recognition via computer vision and depth sensor

Portuguese sign language recognition via computer vision and depth sensor

Our technique on Isolated Moving Gestures, particularly Hand Movements was also presented in detail. Our proposed innovative technique "3D Path Analysis" and an adaptation of Hidden Markov Models (HMM) were formalized and explained and the corresponding experimental results were given. To analyze these Moving Gesture Recognition algorithms, we have prepared a training set and a test set, for both. The training set was composed of 14 different random words selected from the dictionary of Portuguese Sign Language (Figure 27). In the case of the words “nós", and “nosso” the movements are very similar with very few differences between them. For each word the author recorded 6 samples, resulting in a final training set database of 84 samples. The test set consisted in repeating the same gesture 10 times. During the experiments, we have recorded the response of the system (see Table 4).
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T HE ROLE OF INFORMATION FORMAT IN FINANCIAL

T HE ROLE OF INFORMATION FORMAT IN FINANCIAL

Most of the initial work focused on mapping brain areas that are active when a subject is performing a particular cognitive task (Glimcher & Fehr, 2014). However, many of these studies relied on reverse inference, a form of resoning that is not deductively valid and that Poldrack (2016, p. 59) nicely puts as follows: “(1) In the present study, when task comparison A was presented, brain area Z was active. (2) In other studies, when cognitive process X was putatively engaged, then brain area Z was active. (3) Thus, the activity of area Z in the present study demonstrates engagement of cognitive process X by task comparison A.” This logic would only be valid if we knew that brain area Z was active if, and only if, cognitive process X was engaged. However this seems to be rarely the case (Poldrack, 2006, 2011, Birnberg & Ganguly, 2012). The use of reverse inference can be useful as a starting point and provide some information, however it should be used with great care, especially when the selectivity of the brain region is not established or is known to be weak (Poldrack et al, 2006). Novel statistical analyses have since emerged allowing research to make more useful and careful use of this type of inference. For example, bayesian approaches can be used to estimate the likelihood of a cognitive process from a pattern of brain activity taking into account not only the number of previous studies that reported that activity pattern when using a particular task, but also those that reported the same pattern when not employing that type of task (Poldrack et al., 2011).
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Deep neural networks for image quality: a comparison study for identification photos

Deep neural networks for image quality: a comparison study for identification photos

In order to have a model consistently performing correct predictions, it is necessary to train it first. Using a supervised learning approach, the model is fed with training data from which the correct output for each training sample is already known. For each training sample, the model will compute an output (i.e. forward- pass), which is then compared with the sample’s correct output. A cost function is used to measure the difference between the model predictions and the desired outputs, which reflects how well the model is doing. As previously mentioned, adjusting the weights and biases of the model will result in different predictions, therefore it’s necessary to find out exactly what adjustments need to be made, to lower the cost. To do that, back-propagation [41] is used to compute the model gradients, by repeatedly using the chain rule. Based on the choice of the optimizer (e.g. SGD, Adam [28]), these gradients will be used to update the weights and biases of the model, and consequently lower the cost.
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Neural Network Based Parking via Google Map Guidance

Neural Network Based Parking via Google Map Guidance

Intelligent transportation systems (ITS) focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS) uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.
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Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy

Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy

The improved PCNN image segmentation algorithm based on fuzzy entropy Compared with the traditional neural network, the pulse coupled neural network does not need the training process to realize the image segmentation, but the key factor is used to select the reasonable segmentation parameters [2]. The traditional PCNN image segmentation algorithm generally uses the simplified PCNN neural network, because the parameters of the non-simplified PCNN network are too complex and the efficiency is very low, so it is not suitable for image segmentation. However, there are still many parameters that must be reasonably selected in the PCNN image segmentation algorithm, and these parameters can be used to obtain better segmentation results [12]. The traditional PCNN image segmentation algorithm uses the closed value attenuation function, because the image segmentation is characterized by the gray difference between the background and the target. That is to say, the difference in pixel brightness between them is great. So, there is no need to use the threshold value attenuation function index for image segmentation. It also increases the computation time and complexity of the algorithm.
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Building a Neural Computer

Building a Neural Computer

There are some programming constraints that must be respected when building PRF descriptions but those are not captured by the EBNF grammar described in section 4.1. Mainly, we cannot apply rules (C, R and M) to an arbitrary number of parameters (axioms, rules, identifiers) . Each expression results in a PRF with a well defined arity and in most rules the number of parameters accepted in a rule and their arities depend by turn on each others arities. A summary of these constrains is given in the tables below, where capital X denotes x 1 ,…,x n .

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Towards The Development of an Index to Measure the Performance of Multi-Productivity Areas

Towards The Development of an Index to Measure the Performance of Multi-Productivity Areas

consistency to find BP that represents the best performance a contractor can possibly achieve. Liu et al. (2011) studied how work flow variation and labor productivity are related in construction practice. They found that productivity is not improved by completing as many tasks as possible regardless of the plan, nor from increasing workload, work output, or the number of work hours expended. In contrast, productivity does improve when work flow is made more predictable. Thomas and sudhakumar (2013) conducted a study on daily productivity of subcontract labor and directly employed labor for masonry works on a project. The results revealed that the subcontract labor achieved on an average 33% higher productivity than the directly employed labor. Idiake and Bustani (2014) examined the analysis of labor productivity data of block work activity from sixty one construction sites. The construction work composed of ongoing single story buildings in the study area Abuja metropolis. The variables :cumulative productivity, baseline productivity, coefficient of variation and project waste index were computed. The results showed that 44% variation in crew performance is accounted for by variability in labor productivity. Karmale and Biswas (2015) studied the variability of construction labor productivity in building construction project and demonstrated the conceptual benchmarking principles for construction labor productivity. The study showed that the productivity rates of the construction workers vary from one project to another, taking into consideration the type of the activity to be carried out and the surrounding work environment. Recently, Hiyassat et al. (2016) described and analyzed the factors that affect construction labour productivity by conducting a questionnaire survey containing 27 questions (variables) on engineers and foremen who work for contractors. They statistically analyzed the returned responses by calculating the average, standard deviation of each variable. It was concluded that the top three ranked dimensions were „Productivity increases as experience increases‟, „Financial incentives increase productivity‟, and „Trust and communications between management and workers increase productivity‟.
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Mobile robot electronic system with a network and microcontroller based interface

Mobile robot electronic system with a network and microcontroller based interface

The two resistors are called “pull-up resistors”, they need to be present on the clock line (SCL) and on the data line (SDA). They are used to do the interface between different types of logic devices and they ensure that the circuit assumes the default value when no other component forces the line input state. Since the chips are design often open-collector, the chip can only pull the lines low and they float to VDD otherwise; this way, the master can sense if a simultaneously transmitting is happening, letting the pin float and sensing the line, if the line is still at VDD, probably, no transmission is being done from other device. [W17]
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