Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
An Efficient Foot Ulcer Determination System for Diabetic Patients
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
In this proposed work, the authors have designed a structure for the accurate hand gesture recognition using MATLAB. Computation based hand classifier is recommended for the dynamic gesture recognition. Now-a-days, it provides platform for those people who are unable to communicate like a normal person. The Principal Component Analysis based algorithm is capable of doing this work. This is done by creating an invariant subspace, which creates a vocabulary in such a manner that any abnormal person can learn and express himself easily. For doing so, using the movement of hands, its shape and position are obtained as an information which can be recognized for the gesture recognition. One of the methods is Hidden Markov Models (HMMs) capable of recognizing these combinations.
Alertness of the driver is very important to reduce accidents that occur. Sometimes due to long journey, the drivers often get tired and suffer from the problems of sleepiness. So it is important to alert the driver when they are drowsy. In this work, a technique has been developed to monitor the drivers during their whole journey, which is done on the basis of detecting their eyes or body posture. Several methods have been already designed for the detection of drowsiness of the driver. The algorithm is designed using MATLAB. This method includes a sensor or camera which will capture the image and the captured images are continuously monitored and send to the buzzer. The default template has already designed and fed as a reference source, so that the captured image can be easily matched with the reference image.
Hand gesture based sign language is a different way of communication among the Deaf-mute and physically impaired people performed using specific hand gestures. Deaf-mute people face struggle in expressing their feelings to other people, which creates a communication gap between normal and deaf-mute people. This paper, based on hand gesture based Devnagari Sign language Recognition approaches aim to provide a communication way for the Deafmute Community over the society. Therefore, the authors have used static hand gesture based sign language recognition for Deaf-mute communication system. Many researchers use only single American Sign Language or Indian Sign Language for creating their database. In this paper, recent research of sign language is reviewed based on manual communication and body language. A hand gesture based Devnagari Sign Language for recognizing Hindi characters using hand gestures of Deaf-mute people is developed. Devnagari Sign Language recognition system is typically explained with five steps, i.e. acquisition, segmentation, pre-processing, feature extraction, and classification.
One of the most widely accepted search patterns for motion estimation is Diamond Search (DS). DS patterns formed are better than Full Search, Three step search, New Three Step Search, etc., but over the years, many other hybrid patterns, such as Hexagon Diamond, Cross Diamond Search, Modified Small Cross Diamond Search, etc., have been developed. These hybrid algorithms outperform DS either in terms of PSNR or search pixel. A new Improved Diamond Search pattern has been proposed in this paper. Improved DS is center biased. Changes in the search direction within the conventional DS have been made to introduce the Improved DS. This paper performs a comparison between the DS and Improved DS by implementing them in the different pixel space of the block of the frame. The test result gives PSNR better in case of Improved DS than that of DS showing that Improved DS is more efficient than the DS.
In this paper, the authors have proposed a novel approach for noise estimation and reduction from a degraded image. The algorithm is designed using modified decision based trim median filter. This method helps to estimate the actual salt and pepper noise in any degraded image. They have also calculated the presence of Gaussian noise in some noisy images. In this paper, two filters were used; one is trim median filter used for estimation and removal of noise from the image corrupted with Salt and Pepper noise and the other is Gaussian filter which is used for Gaussian noise estimation and reduction. This is a better algorithm because it is based on a modified decision based system. GUI model has been used for this system which helps us to control and estimate the above mentioned noises in an image.>
Nowadays, face recognition has become very much popular to recognize a person with their face in order to avoid crime, etc. This mechanism is based on the division of the face, processing into three phases, i.e. face detection, feature extraction based on the input face image, and face recognition. In this paper, the authors have compared three algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Patterns (LBP). The rate of accuracy of face recognition has also been compared. The advantages and disadvantages of these algorithms will help in obtaining a solution, so that a better face recognition system can be designed.
