Performance analysis of ANN based YCbCr skin detection algorithm

Skin detection from acquired images has various areas of applications especially in automatic facial and human recognition system. The performance analysis of artificial neural network based –YcbCr skin recognition and three other techniques is evaluated in this work. Results obtained show that the use of YCbCr color model performs better than RGB colour model and the use of artificial neural network further improves the accuracy of the system

Artificial neural network based autoregressive modeling technique with application in voice activity detection

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech

Real-time human detection for video surveillance

Recent research in computer vision has increasingly focused on building systems for observing humans and understanding their appearance, movements, and activities, providing advanced interfaces for interacting with humans, and creating realistic models of humans for various purposes (Ogale, 2006). For the last decades, video analysis and understanding has been one of the main active fields in computer vision and image analysis where applications relying on this field are various, like video surveillance, object tracking and traffic monitoring. (Allili et aI, 2007). The ability of computer vision to recognize human from the image and works similar to human eye has made computer vision to be used widely in various applications especially in video analysi

Human Upper Body Pose Region Estimation

The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset

Dynamic approach for real-time skin detection

Human face and hand detection, recognition and tracking are important research areas for many computer interaction applications. Face and hand are considered as human skin blobs, which fall in a compact region of colour spaces. Limitations arise from the fact that human skin has common properties and can be defined in various colour spaces after applying colour normalization. The model therefore, has to accept a wide range of colours, making it more susceptible to noise. We have addressed this problem and propose that the skin colour could be defined separately for every person. This is expected to reduce the errors. To detect human skin colour pixels and to decrease the number of false alarms, a prior face or hand detection model has been developed using Haar-like and AdaBoost technique. To decrease the cost of computational time, a fast search algorithm for skin detection is proposed. The level of performance reached in terms of detection accuracy and processing time allows this approach to be an adequate choice for real-time skin blob tracking

Modeling of Human Upper Body for Sign Language Recognition

Sign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems

Comparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniques

Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system

MRI reconstruction using discrete Fourier transform: a tutorial

The use of Inverse Discrete Fourier Transform (IDFT) implemented in the form of Inverse Fourier Transform (IFFT) is one of the standard method of reconstructing Magnetic Resonance Imaging (MRI) from uniformly sampled K-space data. In this tutorial, three of the major problems associated with the use of IFFT in MRI reconstruction are highlighted. The tutorial also gives brief introduction to MRI physics; MRI system from instrumentation point of view; K-space signal and the process of IDFT and IFFT for One and two dimensional (1D and 2D) data

Development of a New Concept for Fire Fighting Robot Propulsion System

An additional cost to human loss and property destruction during fire disaster is fire fighters injuries and death. The recent statistics of 63,350 fire fighters injuries that occurred during the year 2014 confirms that firefighting still presents great risks of personal injury to the fire fighters [1]. The lack of details on information about the victims trapped in fire and situation in the fire zone increase the risk to fire fighters [2, 3]. To reduce these fatalities fire fighting robots (FFRs) emerged as possible solutions therefore they are developed and researched on. The FFRs are designed for either prevention or emergency (same as intervention) tasks of fire and are applied indoor or outdoor. However, the prime movers of the majority of the FFRs are electrically powered [4] which made them to be suitable for preventive task alone and inappropriate for the emergency task. Their inappropriateness is due to the vulnerability in high temperature environment that characterised fire emergency. Thus, alternative propulsion systems for the mobility of fire fighting robots in emergency setting are evolving. Furthermore, literature survey reveals that water powered hydraulic propulsion system has been the only alternative to the drawbacks of dc motors in the hot environment. The mechanism was implemented on snake fire fighting robot for tunnel fire application [5]. In the mechanism, hydraulic motor was used to actuate the snake joints for mobility while water provides power for the hydraulic motors. However, the snake robot was designed for outdoor application. Consequently, the need for an autonomous fire fighting robot with a novel propulsion system becomes imminent