Hand gesture is one of the method used in sign language for non-verbal communication. It is most commonly used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems has been developed by many makers around the world but they are neither flexible nor cost-effective for the end users. Hence in this paper introduced software which presents a system prototype that is able to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Pattern recognition and Gesture recognition are the developing fields of research. Being a significant part in nonverbal communication hand gestures are playing key role in our daily life. Hand Gesture recognition system provides us an innovative, natural, user friendly way of communication with the computer which is more familiar to the human beings. By considering in mind the similarities of human hand shape with four fingers and one thumb, the software aims to present a real time system for recognition of hand gesture on basis of detection of some shape based features like orientation, Centre of mass centroid, fingers status, thumb in positions of raised or folded fingers of hand

Sign language recognition using image based hand gesture recognition techniques

Fast and robust occluded face detection in ATM surveillance

Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.

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Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

/pdf/a-review-of-infant-cry-analysis-and-classification-4gdq9ucfus.pdf

A review of infant cry analysis and classification

Nowadays, there are so many researches about the baby crying detection for many purposes. The right interpretation of the crying baby is notable for the medical objective so caregiver knows how to treat baby well. Babies within the first three months of age use Dustan Baby Language (DBL) to communicate. Based on some researches, there are five words to express their needs, such as “Neh” (I am hungry), “Eh” (burp is needed), “Owh/Oah” (fatigue), “Eair/Eargghh” (cramps), “Heh” (physical discomfort; feel hot or wet). Beside that purpose, smart home technology implements baby crying detection for monitoring the baby. Several stages to detect crying baby are preprocessing, feature extraction, and classification. Popular feature extractions for voice or sound recognition are Mel Frequency Cepstral Coefficient (MFCC) and Linear Frequency Cepstral Coefficient (LFCC). In this study, both of that feature extraction have been analyzed to know the appropriate condition when using one of those feature extractions. Classification methods (KNN classification, Vector Quantization, and Simple Neural Network) affect the accuracy in detecting and recognizing of baby crying. KNN classification with LFCC results better accuracy than using MFCC one with the sample data is female voice. Ifusing baby voice, there is no significant different accuracy in both of feature extractions.

The Study of Baby Crying Analysis Using MFCC and LFCC in Different Classification Methods

Gestures are important for communicating information among the human. Nowadays new technologies of Human Computer Interaction (HCI) are being developed to deliver user's command to the robots. Users can interact with machines through hand, head, facial expressions, voice and touch. The objective of this paper is to use one of the important modes of interaction i.e. hand gestures to control the robot or for offices and household applications. Hand gesture detection algorithms are based on various machine learning methods such as neural networks, support vector machine, and Adaptive Boosting (AdaBoost). Among these methods, AdaBoost based hand-pose detectors are trained with a reduced Haar-like feature set to make the detector robust. The corresponding context-free grammar based proposed method gives effective real time performance with great accuracy and robustness for more than four hand gestures. Rectangles are creating some problem due to that we have also implement the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.

Real time finger tracking and contour detection for gesture recognition using OpenCV

In this paper, we mainly paying attention on mechanization of Infant's Cry. For this implementation we use LFCC for feature extraction and VQ codebook for toning samples using LBG algorithm. The newborn crying samples composed from various crying baby having 0–6 months age. There are 27 babie's sound as training data, each of which represents the 7 hungry infant cries, 4 sleepy infant cries, 10 in pain infant cries, and 6 uncomfortable infant cries. The testing data is one of the traning newborn crying sample. The discovery of infant cries based the least amount distance of Euclidean distance. The, classification of the cry in four classes neh for hunger owh for sleepy, heh for discomfort, eair for lower gas. Here for classification of the cry our system is alienated into two phases. First is training phase, in which LFCC is used for feature extraction, and then VQ codebooks are engender to compress the feature vectors. Second is the testing stage in which features extraction and codebook production of samples are recurring. Here, estimation of the codebook blueprint of samples to the all the existing patterns in the database are carried based on Euclidian distance between them. LFCC efficiently take into safekeeping the lower as well as higher frequency characteristics than MFCC, hence we will get high-quality results over MFCC.

System propose for Be acquainted with newborn cry emotion using linear frequency cepstral coefficient

This paper simulates two channel WDM optical communication system to investigate the effect of FWM on Bit Error Rate for Duo-binary & binary modulation like NRZ Rectangular at different dispersion value ,core effective area of fiber & channel spacing for 100km long optical communication system. BER got improved with duobinary modulation format & by increasing core effective area which will offer a significant performance benefit in digital systems.

http://www.airccse.org/journal/ijdps/papers/1110ijdps09.pdf

Investigation of fwm effect on ber in wdm optical communication system with binary and duobinary modulation format

Image inpainting is a technique for restoring the damaged parts of an image by referring to the information from the undamaged parts to make the restored image look complete, continuous and natural. The image inpainting technique is time consuming. So the there is automatic digital method of image inpainting. That method recovers the image from the damaged region or scratched regions. In this paper, a image inpainting algorithm based on Kriging interpolation technique was proposed. Kriging interpolation technique automatically fills the damaged region and scratched regions without human intervension in an image using the information available from its surrounding regions in such a way that it uses the spatial correlation structure of point inside the kxk block. Kriging has the ability to face the challenge of keeping the structure and texture information as the size of the damage region heighten. Results shows that kriging has a high PSNR value when recovering a variety of test images from scratches and text as damaged regions. Image inpainting method based on kriging interpolation technique is proposed.

Image inpainting by Kriging interpolation technique for mask removal

This paper propose a new image resolution enhancing technique based on interpolation of high frequency sub-bands generated using lifting wavelet transform (LWT) and low resolution input image. By using stationary wavelet transform (SWT) the edges are enhanced by introducing an intermediate stage. LWT is applied in order to decompose an input image in to different high frequency (HF) sub-bands. Then the high frequency subbands and also the input image are interpolated in parallel. Then estimated high frequency subbands are being modified by using high frequency subbands which are obtained from SWT. Then all these HF subbands and estimated LR image are reconstructed by inverse lifting wavelet transform (ILWT) to generate a new high resolution image. The quantitative and visual images of the described technique show the superiority of the proposed method over conventional and state-of-the-art methods.

Premanand K. Kadbe

Papers

Real time finger tracking and contour detection for gesture recognition using OpenCV

System propose for Be acquainted with newborn cry emotion using linear frequency cepstral coefficient

Investigation of fwm effect on ber in wdm optical communication system with binary and duobinary modulation format

Image inpainting by Kriging interpolation technique for mask removal

Comparative analysis of image enhancement techniques