Real-Time Face Recognition System Using KPCA, LBP and Support Vector Machine

With increasing security threats, Biometric systems have importance in different fields. This appears clearly exactly after the rapid development that happened in power of computing. In this paper, the Design and implementation of a real-time face recognition system are presented. In such a system, Kernel principal component analysis (KPCA) and Local binary pattern (LBP) are used as feature extraction methods with the aid of support vector machine (SVM) to work as a classifier. A comparison between traditional feature extraction methods as (PCA and LDA) and a proposal methods are performed as well as a comparison between support vector neural network and artificial neural network classifier are also implemented. Two types of experiments, On-line, and Off-line experiments are done. In the On-line experiment, a new database is created and used. While in the off-line experiment, two types of databases (ORL and YALE) are used to estimate the performance and efficiency of the system. The combinations of these methods together enhances the experimental results in compare with other methods

Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

Automatic segmentation of skin cancer images using adaptive color clustering

This paper presents the development of an adaptive image segmentation algorithm designed for the identification of the skin cancer and pigmented lesions in dermoscopy images. The key component of the developed algorithm is the Adaptive Spatial K-Means (A-SKM) clustering technique that is applied to extract the color features from skin cancer images. Adaptive-SKM is a novel technique that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The A-SKM has been included in the development of a flexible color-texture image segmentation scheme and the experimental data indicates that the developed algorithm is able to produce accurate segmentation when applied to a large number of skin cancer (melanoma) images

Экспериментальная оценка информативности признаков при анализе 2D изображений костных объектов в судебно-экспертных исследованиях

This article describes the software implementation of the system for extracting and evaluating information features from 2D images of bone fractures and bone objects for classifying fractures and identifying the alleged instrument that caused the injury. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, Gabor filters, Laws energy texture characteristics for 2D images are considered. The analysis carried out on basis of information content estimation to select the features that are most suitable for solving the problem of bone fractures classification. The results will be used for development of methods for complex forensic examination of complex polygonal surfaces of solid objects for automated system for analyzing digital images.Вработеприводитсяописаниепрограммнойреализациисистемывыделенияиоценкиинформативныхпризнаков по фотографиям переломов костных объектов для классификации переломов и идентификации предполагаемого орудия, которым нанесена травма. В качестве признаков использовались текстурные характеристики Харалика, локальные бинарные образцы, фильтры Габора, энергетические текстурные характеристики Лавса для 2D изображений поверхностей переломов. Проведенная оценка информативности позволила выбрать признаки, наиболее подходящие для решения задачи классификации переломов. Результаты будут использованы для судебно-экспертного исследования сложных поверхностей переломов с применением автоматизированной системы анализа цифровых изображений

Оценка информативности параметров поверхностей металлических объектов по 2D­ и 3D­данным для классификации изломов

This article describes evaluation the information content of metal objects surfaces for classification of fractures using 2D and 3D data. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, macrogeometric descriptors of metal objects digitized by a 3D scanner are considered. The analysis carried out on basis of information content estimation to select the features that are most suitable for solving the problem of metals fractures classification. The results will be used for development of methods for complex forensic examination of complex polygonal surfaces of solid objects for automated system for analyzing digital images.В работе проведена оценка информативности параметров поверхностей металлических объектов по 2Dи 3D-данным для классификации изломов. В качестве параметров рассмотрены текстурные характеристики Харалика, локальные бинарные образцы, макрогеометрические дескрипторы поверхностей металлических объектов, оцифрованных 3D-сканером. Проведенный анализ на основе оценки информативности позволил выбрать признаки, наиболее подходящие для решения задачи классификации изломов металлов. Результаты будут использованы для разработки комплекса методов судебно-экспертного исследования сложных полигональных поверхностей твердотельных объектов с применением автоматизированной системы анализа цифровых изображений

Automatic annotation of X-ray images: a study on attribute selection

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space