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

Pemodelan Sistem Pintar Untuk Menentukan Nilai Pecahan Minyak

Tajuk kajian adalah pemodelan sistem pintar untuk menentukan pecahan minyak. Tujuan penyelidikan adalah untuk menentukan pecahan minyak di dalam saluran paip yang melibatkan aliran dua komponen iaitu minyak dan gas. Terdapat banyak jenis regim aliran yang mungkin terbentuk dalam saluran paip seperti strata, teras, anulus, gelembung dan juga homogen. Dalam kajian ini kesemua regim aliran ini direkabentuk dengan cara membina bahagian-bahagian bergeometri menggunakan aturcara dan dijadikan masukan kepada penyelaku (simulator) Tomografi Kemuatan Elektrik (TKE) untuk mendapatkan set data TKE dalam bentuk nilai pengukuran kemuatan bebas. Kajian ini menggunakan Rangkaian Neural Buatan (RNB) untuk menyelesaikan masaalah dalam menetukan pecahan minyak. Keseluruhan kerja pula direalisasikan menggunakan perisian Matlab versi 6.5.1. Penyelidikan ini tidak akan meliputi fasa pembentukan semula imej dalam menentukan pecahan minyak. Keseluruhan data dari penyelaku TKE dibahagikan kepada 3 bahagian utama untuk digunakan dalam proses pembelajaran RNB. 40% daripada sejumlah data digunakan untuk melatih, 20% sebagai data pengesahan dan 40% selebihnya dijadikan data ujian. Pembahagian data-data ini dibuat secara rawak. Senibina RNB yang digunakan adalah dari jenis Radial Basis Function (RBF). Keluaran adalah di antara nilai 0 hingga 1 yang mana menunjukkan nilai pecahan minyak dalam keratan rentas saluran paip

A Study Of Edge Preserving Filters In Image Matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much bette