Damage Characterization Using Guided-Wave Linear Arrays and Image Compounding Techniques

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Line based camera calibration in machine vision dynamic applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.

Digital image analysis of root distribution towards improved irrigation water and soil management: Grapevine and date palm study cases

It is presented two study cases about the approach in root analysis at field and laboratory conditions based on digital image analysis. Grapevine (Vitis vinifera L.) and date palm (Phoenix dactylifera L.) root systems were analyzed by both the monolith and trench wall method aided by digital image analysis. Correlation between root parameters and their fractional distribution over the soil profile were obtained, as well as the root diameter estimation. Results have shown the feasibility of digital image analysis for evaluation of root distribution.

Road segment delineation using active testing principle and an edge-based post-processing strategy

This paper presents a semi-automated method for extracting road segments from medium-resolution images based on active testing and edge analysis. The method is based on two sequential and independent stages. Firstly, an active testing method is used to extract an approximated road centreline which is based on a sequential and local exploitation of the image. Secondly, an iterative strategy based on edge analysis and the approximated centreline is used to measure precisely the road centreline. Based on the results obtained using medium-resolution test images, the method seems to be very promising. In general, the method proved to be very accurate whenever the roads are characterized by two well-defined anti-parallel edges and robust even in the presence of larger obstacles such as trees and shadows.

A photogrammetric method for single image orientation and measurement

The aim of this paper is to present a photogrammetric method for determining the dimensions of flat surfaces, such as billboards, based on a single digital image. A mathematical model was adapted to generate linear equations for vertical and horizontal lines in the object space. These lines are identified and measured in the image and the rotation matrix is computed using an indirect method. The distance between the camera and the surface is measured using a lasermeter, providing the coordinates of the camera perspective center. Eccentricity of the lasermeter center related to the camera perspective center is modeled by three translations, which are computed using a calibration procedure. Some experiments were performed to test the proposed method and the achieved results are within a relative error of about 1 percent in areas and distances in the object space. This accuracy fulfills the requirements of the intended applications. © 2005 American Society for Photogrammetry and Remote Sensing.

Palpebral dimensions in Brazilian children: Assessment based on digital images

Purpose: To determine palpebral dimensions and development in Brazilian children using digital images. Methods: An observational study was performed measuring eyelid angles, palpebral fissure area and interpupillary distance in 220 children aged from 4 to 72 months. Digital images were obtained with a Sony Lithium movie camera (Sony DCR-TRV110, Brazil) in frontal view from awake children in primary ocular position; the object of observation was located at pupil height. The images were saved to tape, transferred to a Macintosh G4 (Apple Computer Inc., USA) computer and processed using NIH 1.58 software (NTIS, 5285 Port Royal Rd., Springfield, VA 22161, USA). Data were submitted to statistical analysis. Results: All parameters studied increased with age. The outer palpebral angle was greater than the inner, and palpebral fissure and angles showed greater changes between 4 and 5 months old and at around 24 to 36 months. Conclusion: There are significant variations in palpebral dimensions in children under 72 months old, especially around 24 to 36 months. Copyright © 2006 Informa Healthcare.

Influence of image filters on the reproducibility of measurements of alveolar bone loss

The reproducibility of measurements of alveolar bone loss on radiographs may be a problem on epidemiologic studies, as they are based on comparisons of the diagnosis of various examiners. The aim of the present research paper was to assess the inter- and intra-examiner reproducibility of measurements of the interproximal alveolar bone loss on non-manipulated digital radiographs and after the application of image filters. Five Oral Radiologists measured the distance between the cementoenamel junction (CEJ) to the alveolar crest or to the deepest point of the bony defect on 12 interproximal digital radiographs of molars and bicuspids of a dry human skull. The digital manipulation and the linear measurements were obtained with the Trophy Windows software (Throphy®). For each image, six different versions were created: 1) non-manipulated; 2) bright-contrast adjustment; 3) negative; 4) negative with brightness-contrast adjustment; 5) pseudo-colored; 6) pseudo-colored with brightness-contrast adjustment. In order to prevent interpretation bias because of the repetition of measurements, the examiners measured the radiographs in a random sequence. The two-way ANOVA test at 5% level of significance to compare the means of readings of the same operator with each filter indicated p<0.05 for the majority of operators, while the comparison between the mean values of operators using the same filter indicated p>0.05 for all filters. Based on the results, we concluded that linear measurements of interproximal alveolar bone loss on digital radiographs are highly reproducible among examiners. Nevertheless, the application of image filters significantly influenced the degree of intra-examiner reproducibility. Some filters even reduced the reproducibility of intra-examiner readings.

Pitch detection algorithms based on zero-cross rate and autocorrelation function for musical notes

This paper discusses two pitch detection algorithms (PDA) for simple audio signals which are based on zero-cross rate (ZCR) and autocorrelation function (ACF). As it is well known, pitch detection methods based on ZCR and ACF are widely used in signal processing. This work shows some features and problems in using these methods, as well as some improvements developed to increase their performance. © 2008 IEEE.

RBF circuits based on folded cascode differential pairs

We propose new circuits for the implementation of Radial Basis Functions such as Gaussian and Gaussian-like functions. These RBFs are obtained by the subtraction of two differential pair output currents in a folded cascode configuration. We also propose a multidimensional version based on the unidimensional circuits. SPICE simulation results indicate good functionality. These circuits are intended to be applied in the implementation of radial basis function networks. One possible application of these networks is transducer signal conditioning in aircraft and spacecraft vehicles onboard telemetry systems. Copyright 2008 ACM.

A regularized nonlinear diffusion approach for texture image denoising

In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature. © 2009 IEEE.

Linear scanning method based on the SAFT coarray

This work presents a method to obtain B-scan images based on linear array scanning and 2R-SAFT. Using this technique some advantages are obtained: the ultrasonic system is very simple; it avoids the grating lobes formation, characteristic in conventional SAFT; and subaperture size and focussing lens (to compensate emission-reception) can be adapted dynamically to every image point. The proposed method has been experimentally tested in the inspection of CFRP samples. © 2010 American Institute of Physics.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

A graph-based framework for thermal faceprint characterization

Thermal faceprint has been paramount in the last years. Since we can handle with face recognition using images acquired in the infrared spectrum, an unique individual's signature can be obtained through the blood vessels network of the face. In this work, we propose a novel framework for thermal faceprint extraction using a collection of graph-based techniques, which were never used to this task up to date. A robust method of thermal face segmentation is also presented. The experiments, which were conducted over the UND Collection C dataset, have showed promising results. © 2011 Springer-Verlag.