Review of an Efficient Face Recognition System Using Hybrid Methodology

At one of the most successful object of images analysis and understanding, face recognition has recently received significant attention, especially during the past few years. Facial recognition System (FRS) has emerged as an attractive solution to address many contemporary needs for identification and the frication of identity claims. It brings together the promise of other biometric systems, which attempt to identity to individually distinctive features of the body. Recognizing frontal countenance of human beings by a computer system is an interesting and challenging problem. Facial recognition System has emerged as an delicious solution to address many instant needs for identification and the verification of identity claims. It brings together the port end of other biometric systems, which attempt to tie identity to individually distinctive features of the body. Facial feature extraction consists in restraining the most characteristic face appearance such as eyes, nose, and mouth regions within the face images that portray the human faces. In this research work , the two most well-known algorithms i.e. LBP algorithm is used as feature ex- tractor of the face image. LBP is used for their resistance against changing frontal facial expressions. PCA algorithm is used for dimension reduction of the countenance vector. The complete approach has been tested on databases of people under different facial expressions

Image Segmentation and Classification for Medical Image Processing

Segmentation and labeling remains the weakest step in many medical vision applications. This paper illustrates an approach based on watershed transform which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters. Two of these modules are presented: the lung cancer detection, a method for the segmentation of cancer regions from CT images, a watershed algorithm for image segmentation and brain tumor detection from MRI images. Various GLCM features along with some statistical features are used for classification using Neural network and Support Vector Machine (SVM). We describe the principles of the algorithms and illustrate their generic properties by discussing the results of both applications in 2D MRI images of Brain tumor and CT images of lung cancer

Image Processing for Medical Image Analysis: A Review

Image processing techniques are used widely in medical areas for improving the image in earlier detection and treatment stages, it is very important to discover the abnormality issues in given images, specially in various cancer, tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the main factors of this work, image quality improvement and assessment are depending on the enhancement stage where pre-processing techniques is used. The principal objectives of this course are to provide basic introduction and techniques for medical image processing and to promote for further study and research in medical image processing

Training and Placement Web Portal

This work present web portal designed for managing t raining and placement data. The objective of this project is to develop a system that can be used by placement cell of a college. The purpose is to design a system that provides functionalities to perform the activities related to placement services. It is based on complete modular architecture. This modularity of the architecture will allow us to replace or add modules in the future as a way to enhance a particular feature of particular situation. This system can be used as an application for the TPO of th e college manages the student information with regards to training and placement. In the present work some of the modules are implemented by means of managing training and placement data. Whereas module responsible for adopting student information, company information and study material require for company placement