HAND GESTURE RECOGNITION SYSTEM UNDER COMPLEX BACKGROUND USING SPATIO-TEMPORAL ANALYSIS

The interaction between human and machine can be achieved by implementing human gesture applications which are known as human computer interaction (HCI). It has been developed several years ago, and now we can easily find it in many applications in real world e.g. in virtual reality, games, robotics, sign language recognition, etc. Gesture recognition has the potential to offer a natural way of communication between humans and machines. The most popular methods in human gesture application are hand gesture recognition. Several of hand gesture recognition methods still have the same problem when trying to recognize hand gesture accurately because of the existence of the uncontrolled environment problem, especially complex background. This study offered a method that can handle complex background in hand gesture recognition by analyzing pattern in spatio temporal domain. The system could reduce irregular noise in complex background significantly. The experiments were conducted based on our own recorded 180 videos with total 15 gestures. Experiment result showed that the proposed method could recognize 15 hand gestures with accuracy 98.33%

Real Time Static and Dynamic Sign Language Recognition using Deep Learning

Sign language recognition systems are used for enabling communication between deaf-mute people and normal user. Spatial localization of the hands could be a challenging task when hands-only occupies 10% of the entire image. This is overcome by designing a real-time efficient system that is capable of performing the task of extraction, recognition, and classification within a single network with the use of a deep convolution network. The recognition is performed for static image dataset with a simple and complex background, dynamic video dataset. Static image dataset is trained and tested using a 2D deep-convolution neural network whereas dynamic video dataset is trained and tested using a 3D deep-convolution neural network. Spatial augmentation is done to increase the number of images of static dataset and key-frame extraction to extract the key-frames from the videos for dynamic dataset. To improve the system performance and accuracy Batch-Normalization layer is added to the convolution network. The accuracy is nearly 99% for dataset with a simple background, 92% for dataset with complex background, and 84% for the video dataset. By obtaining a good accuracy, the system is proved to be real-time efficient in recognizing and interpreting the sign language gestures

Hand shape estimation for South African sign language

>Magister Scientiae - MScHand shape recognition is a pivotal part of any system that attempts to implement Sign Language recognition. This thesis presents a novel system which recognises hand shapes from a single camera view in 2D. By mapping the recognised hand shape from 2D to 3D,it is possible to obtain 3D co-ordinates for each of the joints within the hand using the kinematics embedded in a 3D hand avatar and smooth the transformation in 3D space between any given hand shapes. The novelty in this system is that it does not require a hand pose to be recognised at every frame, but rather that hand shapes be detected at a given step size. This architecture allows for a more efficient system with better accuracy than other related systems. Moreover, a real-time hand tracking strategy was developed that works efficiently for any skin tone and a complex background

New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component

License Plate recognition plays an important role on the traffic monitoring and parking management systems. In this paper, a fast and real time method has been proposed which has an appropriate application to find tilt and poor quality plates. In the proposed method, at the beginning, the image is converted into binary mode using adaptive threshold. Then, by using some edge detection and morphology operations, plate number location has been specified. Finally, if the plat has tilt, its tilt is removed away. This method has been tested on another paper data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit Mashha

A new 2D static hand gesture colour image dataset for ASL gestures

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset