Hand Gesture Recognition System Using Histogram and Neural Network

In this paper, consider the problem facing by distance between hand and the web cam and corresponding image noise in a Hand gesture recognition for human computer interaction (HCI) using a web cam.In this paper a survey of various recent hand gesture recognition systems background information is presented, along with key issues and major challenges of hand gesture recognition system are presented. In this paper consider histogram and neural network approaches for hand detection. At the end of this paper focus on different hand gesture approaches, algorithm, prototype model, technologies and its applications. The present approaches can be mainly divided into Data-Glove Based, Computer Vision Based approach and Drawing gesture. Hand gesture is a method of non-verbal communication for human beings. Using gesture applications human can interact with computer efficiently without any input devices. DOI: 10.17762/ijritcc2321-8169.160413

Review on hand gesture recognition

The aim of this chapter is to present a review on the development of vision systems based on hand gesture. Vision-Based Human to Computer Interaction (HCI) systems has the ability of carrying a wealth of information in a natural way and at a low cost. Therefore hand recognition becomes a widely studied topic with a wide range of applications such as SL translators, gesture recognition for control, augmented reality, surveillance, medical image processing, and etc. Hand recognition with no constraint on the shape is an open issue because the human hand is a complex articulated object consisting of many connected parts and joints. Considering the global hand pose and each finger joint, human hand motion has roughly 27 degree offreedom (DOF

Symbiosis in computational vision systems

AbstractWhile the goal of computational vision systems is the totally automated understanding of images, it is not necessary for this goal to be achieved before practical vision systems can be developed. In particular, systems that require some amount of human intervention can be applied to real problems with beneficial results. In “symbiotic” vision systems the computer brings something to bear on the problem that either replaces or supplements a subtask that would otherwise be done by a human expert.In symbiotic systems there is a large range of possibilities for the type and amount of interaction that the human expert must provide. This can range from minor aid to the computer system, when it reaches an ambiguity that it cannot resolve, to major control of the processing in complicated regions of the image that are of primary interest. Symbiotic vision systems must allow the user to access the system at the point in the range that is suitable. In addition, the system must have facilities to both present information and accept “advice” from the expert in a way that is natural and convenient.Two example vision systems will illustrate different ways in which these problems have been solved. The first, MISSEE, uses a cycle of perception combined with a schema-based system architecture to provide a flexible framework in which the user can select the amount of interaction he wishes to undertake. The second, which carries out the normal moveout phase of seismic data processing, has a more limited focus, but provides a natural means of communication

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

Integrated Shape and Texture Features for Robust Pedestrian Detection

Over the last decade, detection of human beings became one of the most significant tasks in computer vision due to its extended applications that include human computer interaction, visual surveillance, person identification, event detection, gender classification, robotics, automatic navigation, and safety systems, etc. However this task is rather challenging because of the fluctuation in appearance of the human body as well as the cluttered scenes, pose, occlusion, and illumination variations. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy we propose a new descriptor that fuses the local phase information, image gradient, and texture features as a single descriptor and is denoted as fused phase, gradient and texture features (FPGT). The gradient and the phase congruency concepts are used to capture the shape features, and a center-symmetric local binary pattern (CSLBP) approach is used to capture the texture of the image. The fusing of these complementary features yields the ability to localize a broad range of the human structural information and different appearance details which allow to more robust and better detection performance. The proposed descriptor is formed by computing the phase congruency, the gradient, and the CSLBP value of each pixel with respect to its neighborhood. The histogram of oriented phase and histogram of oriented gradient, in addition to CSLBP histogram are extracted for each local region. These histograms are concatenated to construct the FPGT descriptor. Principal components analysis (PCA) is performed to reduce the dimensionality of the resultant features. Several experiments were conducted to evaluate the detection performance of the proposed descriptor. A support vector machine (SVM) classifier is used in these experiments to classify the FPGT features. The results show that the proposed algorithm has better detection performance in comparison with the state of the art feature extraction methodologies.https://ecommons.udayton.edu/stander_posters/1916/thumbnail.jp

Advanced Visualization and Interaction Systems for Surgical Pre-operative Planning

The visualization of 3D models of the patient’s body emerges as a priority in surgery. In this paper two different visualization and interaction systems are presented: a virtual interface and a low cost multi-touch screen. The systems are able to interpret in real-time the user’s movements and can be used in the surgical pre-operative planning for the navigation and manipulation of 3D models of the human body built from CT images. The surgeon can visualize both the standard patient information, such as the CT image dataset, and the 3D model of the patient’s organs built from these images. The developed virtual interface is the first prototype of a system designed to avoid any contact with the computer so that the surgeon is able to visualize models of the patient’s organs and to interact with these, moving the finger in the free space. The multi-touch screen provides a custom user interface developed for doctors’ needs that allows users to interact, for surgical pre-operative planning purposes, both with the 3D model of the patient’s body built from medical images, and with the image dataset

A creative intelligent object classification system using Google's™ images import search function

Published ArticleLimits of artificial intelligent, expert systems are defined by the specific hardware limitation of the specific system. Limits can be overcome, or addressed, by giving an intelligent system web access; therefore giving it access to Google's™ vast hardware, search functions and databases. Reverse image searches can be done directly in Google's™ image search bar since October 2011. This reverse image search function is used by the proposed system to do object recognition. Computational creativity, or the ability of a program or computer to show human-level creativity and interaction, is achieved by means of a voice communication of the object identification result to the user. The proposed system interprets the result by doing a definition web search and communicating this to the user. The results show that with the novel interpretation software, it should be possible to use Google™ as an artificial intelligent, computational creative system. This proposed system thus has the ability to do object classification by accessing Google's™ vast hardware, search functions and databases, thereafter would the proposed system search a suitable definition for the classification. All of this information is communicated to the user using voice. These techniques could be used on an automatic guided vehicle, robots or expert system

Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human users to access the system resources? One solution is by designing a CAPTCHA (Completely Automated Public Turing Tests to tell Computers and Humans Apart), a program that can generate and grade tests that most humans can pass but computers cannot. It is used as a tool to distinguish humans from malicious bots. They are a class of Human Interactive Proofs (HIPs) meant to be easily solvable by humans and economically infeasible for computers. Text CAPTCHAs are very popular and commonly used. For each challenge, they generate a sequence of alphabets by distorting standard fonts, requesting users to identify them and type them out. However, they are vulnerable to character segmentation attacks by bots, English language dependent and are increasingly becoming too complex for people to solve. A solution to this is to design Image CAPTCHAs that use images instead of text and require users to identify certain images to solve the challenges. They are user-friendly and convenient for human users and a much more challenging problem for bots to solve. In today’s Internet world the role of user profiling or user identification has gained a lot of significance. Identity thefts, etc. can be prevented by providing authorized access to resources. To achieve timely response to a security breach frequent user verification is needed. However, this process must be passive, transparent and non-obtrusive. In order for such a system to be practical it must be accurate, efficient and difficult to forge. Behavioral biometric systems are usually less prominent however, they provide numerous and significant advantages over traditional biometric systems. Collection of behavior data is non-obtrusive and cost-effective as it requires no special hardware. While these systems are not unique enough to provide reliable human identification, they have shown to be highly accurate in identity verification. In accomplishing everyday tasks, human beings use different styles, strategies, apply unique skills and knowledge, etc. These define the behavioral traits of the user. Behavioral biometrics attempts to quantify these traits to profile users and establish their identity. Human computer interaction (HCI)-based biometrics comprise of interaction strategies and styles between a human and a computer. These unique user traits are quantified to build profiles for identification. A specific category of HCI-based biometrics is based on recording human interactions with mouse as the input device and is known as Mouse Dynamics. By monitoring the mouse usage activities produced by a user during interaction with the GUI, a unique profile can be created for that user that can help identify him/her. Mouse-based verification approaches do not record sensitive user credentials like usernames and passwords. Thus, they avoid privacy issues. An image CAPTCHA is proposed that incorporates Mouse Dynamics to help fortify it. It displays random images obtained from Yahoo’s Flickr. To solve the challenge the user must identify and select a certain class of images. Two theme-based challenges have been designed. They are Avatar CAPTCHA and Zoo CAPTCHA. The former displays human and avatar faces whereas the latter displays different animal species. In addition to the dynamically selected images, while attempting to solve the CAPTCHA, the way each user interacts with the mouse i.e. mouse clicks, mouse movements, mouse cursor screen co-ordinates, etc. are recorded nonobtrusively at regular time intervals. These recorded mouse movements constitute the Mouse Dynamics Signature (MDS) of the user. This MDS provides an additional secure technique to segregate humans from bots. The security of the CAPTCHA is tested by an adversary executing a mouse bot attempting to solve the CAPTCHA challenges