Extracting Prototypical Facial Images from Exemplars

Perception ◽  
1993 ◽  
Vol 22 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Philip J Benson ◽  
David I Perrett
Keyword(s):  
Computer Graphic ◽  
Natural Image ◽  
Computational Techniques ◽  
Graphic Method ◽  
Facial Images

A computer graphic method for extracting a natural image of an individual's facial prototype, or average appearance, from a number of different images of that individual is presented. The process improves upon previous photographic and computational techniques. Synthesis of a person's average expression and pose from a sample of images is derived in an automatic and quantitative way. Possible uses of composite faces produced in this manner in psychological investigations of facial qualities (eg attractiveness) and in applied areas such as telecommunication are pointed out.

scholarly journals Determination of Radial Segmentation of Point Clouds Using K-D Trees with the Algorithm Rejecting Subtrees

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1451 ◽  
Author(s):  
Jerzy Orlof ◽  
Paweł Ozimek ◽  
Piotr Łabędź ◽  
Adrian Widłak ◽  
Mateusz Nytko
Keyword(s):  
Computer Graphic ◽  
Point Clouds ◽  
Binary Trees ◽  
Graphic Method ◽  
Triangulated Irregular Network ◽  
Mathematical Algorithm

This paper presents an innovative computer graphic method for viewshed generation from big point clouds. The proposed approach consists in simplification of typical methods for viewshed formation that are based on sorting and binary trees. The proposed method is based on the k-d tree concept optimized with radial segmentation and a dedicated mathematical algorithm for subtree rejection. The final visualization of the viewshed is designed with a graphic method using triangulated irregular network (TIN) surfaces from the accepted subtrees.

scholarly journals Computer Graphic and Photographic Image Classification using Local Image Descriptors

2017 ◽  
Vol 67 (6) ◽  
pp. 654 ◽  
Author(s):  
Gajanan K Birajdar ◽  
Vijay H Mankar
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Graphic ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Graphic Images

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

A computer-graphic method for teaching protein chemical score concepts

1982 ◽  
Vol 14 (1) ◽  
pp. 18-20 ◽  
Author(s):  
Stephen Dubin ◽  
Chia-Lin Chu ◽  
John Weiher ◽  
Chiong Lin
Keyword(s):  
Computer Graphic ◽  
Graphic Method ◽  
Chemical Score

Strain estimation from distorted vertebrate fossils: application of the Wellman method

2006 ◽  
Vol 144 (1) ◽  
pp. 211-216 ◽  
Author(s):  
JYOTI SHAH ◽  
DEEPAK C. SRIVASTAVA
Keyword(s):  
Vertebral Column ◽  
Finite Strain ◽  
Computer Graphic ◽  
Principal Strain ◽  
Graphic Method ◽  
Two Dimensional ◽  
Independent Evidence ◽  
Small Segment ◽  
Strain Estimation

Distortion of the vertebral column in fossils can be used for the estimation of two-dimensional finite strain by a simple geometrical technique, namely the Wellman method. We demonstrate application of the Wellman method to the distorted vertebral columns of a reptile and a stem-chordate, and use the results to restore the undistorted fossil shapes by a computer graphic method. The Wellman method is particularly efficient in situations where independent evidence for the principal strain directions, or undistorted forms, are lacking. The method is purely geometrical, easy to use, and rapid. It involves relatively low error, and works even when only a small segment of the distorted vertebral column is preserved.

Three-dimensional image analysis and visualization in confocal light microscopy

1993 ◽  
Vol 51 ◽  
pp. 158-159
Author(s):  
J. K. Samarabandu ◽  
R. Acharya ◽  
D. R. Pareddy ◽  
P. C. Cheng
Keyword(s):  
Depth Perception ◽  
Computer Graphic ◽  
Three Dimensional ◽  
Cellular Organization ◽  
Data Set ◽  
Computational Burden ◽  
Interactive Operation ◽  
Cell Organization ◽  
Confocal Images ◽  
3D Digitization

In the study of cell organization in a maize meristem, direct viewing of confocal optical sections in 3D (by means of 3D projection of the volumetric data set, Figure 1) becomes very difficult and confusing because of the large number of nucleus involved. Numerical description of the cellular organization (e.g. position, size and orientation of each structure) and computer graphic presentation are some of the solutions to effectively study the structure of such a complex system. An attempt at data-reduction by means of manually contouring cell nucleus in 3D was reported (Summers et al., 1990). Apart from being labour intensive, this 3D digitization technique suffers from the inaccuracies of manual 3D tracing related to the depth perception of the operator. However, it does demonstrate that reducing stack of confocal images to a 3D graphic representation helps to visualize and analyze complex tissues (Figure 2). This procedure also significantly reduce computational burden in an interactive operation.

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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