A Comparison of Wavelet, Curvelet and Contourlet based Texture Classification Algorithms for Characterization of Bone Quality in Dental CT

Abstract: The objective of this paper is to design and implement classifier framework to assist the surgeon for preoperative assessment of bone quality from Dental Computed Tomography images. This article focuses on comparing the discriminating power of several multiresolution texture analysis methods to evaluate the quality of the bone based on the texture variations of the images obtained from the implant site using wavelet, curvelet and contourlet.The approach consists of three steps: automatic extraction of the most discriminative texture features from regions of interest, creation of a classifier that automatically grades the bone depends on the quality. Since this is medical domain, the validation against the human experts is carried out. The results indicate that the combination of the statistical and multiscale representation of the bone image gives adequate information to classify the different bone groups compared to gray level features at single scale

Adaptive face modelling for reconstructing 3D face shapes from single 2D images

Example-based statistical face models using principle component analysis (PCA) have been widely deployed for three-dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA-based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been

Um estudo sobre biometria de pessoas baseada no padrão de veias do dedo

Biometric identification is the study of physiological and behavioral attributes of an indivi dual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This work presents a detailed review on finger vein recognition algorithms. Such tools include image acqui sition, preprocessing, feature extraction and matching methods to extract and analyze result patterns.Identificação biométrica ´e o estudo de atributos fisiológicos e comportamentais de um indivíduo para superar problemas de segurança. O reconhecimento das veias do dedo é uma técnica biométrica usada para analisar os padrões de veias das pessoas para uma autenticação adequada. Este trabalho apresenta uma revisão detalhada sobre algoritmos de reconhecimento de veias do dedo. Essas ferramentas incluem aquisição de imagens, pré processamento, extração de recursos e métodos de correspondência para extrair e analisar padrões de resultados

Artificial intelligence machine vision grading system

Thesis (M. Tech.) -- Central University of Technology, Free State, 201