Sliding Window for Radial Basis Function Neural Network Face Detection

This paper present a Radial Basis Function Neural Network (RBFNN) face detection using sliding windows. The system will detect faces in a large image where sliding window will run inside the image and identified whether there is a face inside the current window. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBFNN offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the system will be based on the rate of detection and also false negative rate

Visualization of Image Distortion on Camera Calibration for Stereo Vision Application

This paper presents a visualization of image distortion on camera calibration for stereo vision application. The 3D image plane in a group of target or image during the process of stereo pair calibration is also discussed. The extrinsic parameters of camera calibration can be viewed in 3D image or scene which contains the rotation and translation of vector. The error re-projection of a single image could determine the less error of distortion during the extraction of chessboard corner each image taken. The distortion model also generates an error coordinate system in pixel value. The 3D image will viewed the result and output of extrinsic parameters during the calibration process

Machine vision based height measuring system

This paper presents a machine vision based height measuring system. The system will measure the height of a product based on the input from a webcam. The input image from the webcam will then be processed using image processing and then calculated to give the height of the product. In the market, there are many products that need to be measured whether the length or the height. These products also have different size and shape. There are several problems when using manual method for measuring such as man power will be needed at the station, longer time needed for measuring the product and the measurement may not be so accurate. This system aims to reduce these problems by developing a measuring system based on vision system

Application of array processing for mobile communications

Digital Signal Processing (DSP) is about a mathematical equation and mathematical operations. It is described by the significations of discrete period, discrete frequency, or supplementary discrete area signals by a order of numbers or signals and the processing of all the signals that related. Digital Signal Processing applications consist of the signal processing for communication. For example is the array processing for the mobile communications. Signal processing is a extensive area of scrutiny that extends from the easiest form of 1-D signal processing to the convoluted form of M-D and array signal processing. This report presents th

IIR and FIR digital filter : a case study

This paper is about to analyze and identify the digital audio signal. Two audio signals are provided which is the original audio and the added noise audio signal. The purpose of the study is to identify and eliminate the unknown noise signals by using Finite Impulse Response and Infinite Impulse Response digital filters. The detail steps to design the both have been stated in this paper. All the results is simulated and showed in MATLAB to show the comparison between the two. Keywords- FIR, IIR, MATLAB

Face Recognition Using Fixed Spread Radial Basis Function Neural Network

This paper presents face recognition using spread fixed spread radial basis function neural network. Acquired image will be going through image processing process. General preprocessing approach is use for normalizing the image. Radial Basis Function Neural Network is use for face recognition and Support Vector Machine is used as the face detector. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF but in this paper fixed spread is used as there is only one train image for every user and to limit the output value

Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value

This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria. In this research, the best setting for RBF face detection were summarized into one table where by using center 200 and spread 4 gives the highest detection rate and the lowest FAR as well as FRR. But for detecting many faces in a single image, center 200 and spread 5 is the best setting as the system can detect all faces in the image

Review on the barcode technology of android application development for GST products database system

Starting from 1 April 2015, the Goods and Service Tax will be introduced by the Malaysia Government to be implemented in the country. Since there is no such application provided in the market that can help the struggling citizen to recognize the GST goods, hence; this study will compare the existing Goods and Service Tax system and expose the user an idea to know the items with GST. Selected review papers had been chosen as the man reference to review this specific issue that related to Goods and Service Tax. Advantages and disadvantages of the review paper will be tabulated as to compare and to be used in order to produce a user friendly Android application in the future. In conclusion, the grayscale technique was the suitable and convenient to be used in the image processing. Since, the accuracy of the technique is quite high and from all proposed techniques it is the easy one. The emphasis of the 1D barcodes is about the identification, since the scanning of the barcodes (from the android program) going to be connected to the database; this technique will be make the application to be simple and suitable for all type of user. Keywords: 1D Barcode, Android, Barcode, Database, Goods and Service Tax

JPG, PNG and BMP image compression using discrete cosine transform

This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested

A Pixel Matching Process And Multiples Roi For Stereo Images In Stereo Vision Application

This paper presents an analysis of stereo images for an application of stereo vision application. The matching process is to determine the difference of intensities of pixel between stereo images while the region of interest ROI works as a reference area to the stereo vision application. This region is a reference view of the stereo camera and stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The disparity mapping is generated by the algorithm with the reference to the left image coordinate. The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. The rectification and block matching processes are also briefly described in this paper