Fabiana Rodrigues Leta

Numerical modeling of facial aging.

Facial aging is a biological phenomenon. Skin properties change with time, and gravity and facial expressions exert mechanical deformation. Knowledge of these alterations may suggest ways to reverse them by identifying the corresponding distortional forces. The aim of this study was to determine a pattern of change for parameters of the face during the aging process, based on the numerical fitting of measures from a sample of patients. The first aspect of this study was to define adequate facial parameters and means of measuring them. Subsequently, each parameter was defined individually, and these data were analyzed as a set. The sample for the research was restricted to a group of 40 white female patients with a history of limited exposure to the sun, with ages ranging from 25 to 65. The reason for choosing this sample was the availability of frontal pattern photographs at different ages. The parameters for each patient were measured at two different ages. A strong correlation was found between age and behavior of the parameters. This aging model can be verified qualitatively by comparing photographs of a patient with manipulated photographs simulating aging. The quantitative verification of the model was done through the comparison of the measured and the predicted parameters.

Applications of computer vision techniques in the agriculture and food industry: a review

Over the last decades, parallel to technological development, there has been a great increase in the use of visual inspection systems. These systems have been widely implemented, particularly in the stage of inspection of product quality, as a means of replacing manual inspection conducted by humans. Much research has been published proposing the use of such tools in the processes of sorting and classification of food products. This paper presents a review of the main publications in the last ten years with respect to new technologies and to the wide application of systems of visual inspection in the sectors of precision farming and in the food industry.

Algoritmo de alocação de recursos discretos com análise de envoltória de dados

The resource allocation is one of the main problems in Operational Research. The use of Data Envelopment Analysis (DEA) in this field is a new feature with a great potential, mainly when combined with integer programming problems. This paper presents an algorithm to allocate integer resources using a step-by-step DEA algorithm. We applied the proposed approach to a real case study, which consists in allocating teacher positions in some departments of Universidade Federal Fluminense. We compare the results with those obtained by the official commission.

Macroscopic Rock Texture Image Classification Using an Hierarchical Neuro-Fuzzy System

This paper explores the use of an hierarchical neurofuzzy model for image classification of macroscopic rock texture. The relevance of this study is to help geologists in diagnosing and planning the oil reservoir exploitation. The same approach can be also applied to metals, in order to classify the different types of materials based on their grain texture. We present an image classification for macroscopic rocks, based on these texture descriptors and on a neuro-fuzzy approach.

Evaluación de Parámetros de Rugosidad usando Análisis de Imágenes de Diferentes Microscopios Ópticos y Electrónicos

A methodology for analyzing the roughness based on surface characteristics of the images obtained from optical and electronic microscopes, is presented. The features that describe textures and are also used to classify them derive from the Haralick descriptors, which are based on cooccurrence matrices. The primary roughness patterns are evaluated and classified according to several features which use the values of these descriptors. The values extracted from the patterns are fed to artificial neural network of the multi-layer perceptron type. It is concluded that it is possible to start implementing the control of metal parts for industrial quality control of manufactured products through this system of roughness recognition.

A study of the facial aging - a multidisciplinary approach

This paper describes a mathematical and graphical model for face aging. It considers the possibility of predicting the aging process by offering an initial quantification of this process as it applies to the face. It is concerned with physical measurements and a general law of time dependence. After measuring and normalizing a photograph of a person, one could predict, with a known amount of error, the appearance of that person at a different age. The technique described has served its purpose successfully, with a representative amount of patient data behaving sufficiently near the general aging curve of each parameter. That model uses a warping technique to emulate the aging changes on the face of women. Frequently the warping methods are based on the interpolation between images or general mathematical functions to calculate the pixel attributes. The implemented process considers the age features of selected parts of a face such as the face outline and the shape of the lips. These age features were obtained by measuring the facial regions of women that have been photographed throughout their lives. The present work is first concerned with discussing a methodology to define the aging parameters that can be measured, and second with representing the age effects graphically.

Optimization of the use of skin expanders.

Skin expansion is a physiological process that is defined as the ability of the human skin to increase its superficial area in response to stress or to a given deformation. Skin expanders are silicon bags that are implanted underneath the skin. Because the skin presents creep or relaxation, the resulting stress decreases after a time due to the imposed deformation. Skin expansions are used to reconstruct burned areas and breasts after a mastectomy or to hide scars.

Computational system to detect defects in mounted and bare PCB Based on connectivity and image correlation

In this paper we present an image analysis system for printed circuit board (PCB) automated inspection. In the last years PCB manufacturing industry has been advanced in inspection automation systems, especially to solve smaller tolerance requirements. A PCB consists in a circuit and electronic components assembled in a surface. There are three main process involved in its manufacture, where the inspection is necessary. The main process consists in the printing itself. Another important procedure is the components placement over the PCB surface. And the third is the components soldering. In the proposed inspection system we consider the board printing and components placements defects. We first compare a PCB standard image with a PCB image, using a simple subtraction algorithm that can highlight the main problem-regions. Then we used connection analysis in the printed circuit to find fatal and potential errors, like breaks, circuit shorts, missing components. Besides, using digital image correlation techniques, the system detects component errors, like absence, change, and wrong position. In other to develop this methodology in real PCB, we propose to magnify the problem-regions and start to find the errors in a set of PCB sections, which are smaller than the main PCB image.

Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method) for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses), basalt (four subclasses), diabase (five subclasses), and rhyolite (five subclasses). These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.

Detección de Posición Angular de Embarcaciones, utilizando Técnicas de Visión Computacional y Redes Neurales Artificiales

This paper presents a system for detecting angular position of targets, using feature extraction techniques in digital imaging and artificial neural networks. Military ships images graphically generated by three-dimensional solid modeling software are used. Several tests using artificial neural networks applied to the set of geometric features were performed. The results show the important contribution of recognition algorithms in determining the ship angular position, regardless of their distance from the observer. The results encourage future applications for tracking targets using infrared images.