Registro semiautomático entre imagens infravermelhas e RGB coletadas por um par de câmaras digitais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Orientação de imagens CBERS-2B usando o modelo rigoroso de colinearidade com dados orbitais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extração de feições retas com precisão subpixel: estudo comparativo entre três métodos

In the fields of Machine Vision and Photogrammetry, extracted straight lines from digital images can be used either as vector elements of a digital representation or as control entities that allow the determination of the camera interior and exterior orientation parameters. Applications related with image orientation require feature extraction with subpixel precision, to guarantee the reliability of the estimated parameters. This paper presents three approaches for straight line extraction with subpixel precision. The first approach considers the subpixel refinement based on the weighted average of subpixel positions calculated on the direction perpendicular to the segmented straight line. In the second approach, a parabolic function is adjusted to the grey level profile of neighboring pixels in a perpendicular direction to the segmented line, followed by an interpolation of this model to estimate subpixel coordinates of the line center. In the third approach, the subpixel refinement is performed with a parabolic surface adjustment to the grey level values of neighboring pixels around the segmented line. The intersection of this surface with a normal plane to the line direction generates a parabolic equation that allows estimating the subpixel coordinates of the point in the straight line, assuming that this is the critical point of this function. Three experiments with real images were made and the approach based on parabolic surface adjustment has presented better results.

Fototriangulação com parâmetros adicionais para câmaras digitais: uma avaliação experimental

Considering the growing use of digital cameras in Photogrammetric projects, especially in aerial survey, this paper presents tests and analyses of bundle block adjustment with additional parameters, using different mathematical models, and blocks of images acquired by the SAAPI digital acquisition system. Three blocks of images were processed by the LPS (Leica Photogrammetry Suite) software, in which five groups of additional parameters (AP) can be used: Bauer, Jacobsen, Ebner, Brown and Lens distortion. These AP's models were employed in the bundle block adjustment, and the results were analyzed based on the accuracy of the checking points and on the changes in these additional parameters. The obtained results showed that the Lens Distortion model allowed the best results.

A influência das injunções de orientação relativa na calibração de um sistema dual de câmeras digitais

Image acquisition systems based on multi-head arrangement of digital frame cameras, such as the commercial systems DMC, UltraCam, besides others, are attractive alternatives enabling larger imaging area when compared to a single frame camera. Considering that in these systems, cameras are tightly attached to an external mount, it is assumed that relative position and orientation between cameras are stable during image acquisition and, consequently, these constraint can be included in the calibration step. This constraint is acceptable because estimates of the relative orientation (RO) parameters between cameras, from previously estimated exterior orientation parameters, present higher and significant deviations than the expected physical variations, due to error propagation. In order to solve this problem, this work presents an approach based on simultaneous calibration of two or more cameras using constraints that state that the relative rotation matrix and the distance between the cameras head are stable. Experiments with images acquired by an arrangement of two Hasselblad H2D cameras were accomplished, without and with the mentioned constraints. The experiments showed that the calibration process with RO constraints allows better results than the approach based on single camera calibration, provided that the estimation has included only images with good target distribution.

Detection and thinning of edges in previously known directions

This work presents a study on the generation of digital masks aiming at edge detection with previously known directions. This solution is important when edge direction is available either from a direction histogram or from a prediction based on camera and object models. A modification in the non-maximum suppression method of thinning is also presented enabling the comparison of local maxima for any edge directions. Results with a synthetic image and with crops of a CBERS satellite images are presented showing an example with its application in road detection, provided that directions are previously known.

Sincronismo de dispositivos para o georreferenciamento de imagens digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A recursive approach to space resection using straight lines

An approach using straight lines as features to solve the photogrammetric space resection problem is presented. An explicit mathematical model relating straight lines, in both object and image space, is used. Based on this model, Kalman Filtering is applied to solve the space resection problem. The recursive property of the filter is used in an iterative process which uses the sequentially estimated camera location parameters to feedback to the feature extraction process in the image. This feedback process leads to a gradual reduction of the image space for feature searching, and consequently eliminates the bottleneck due to the high computational cost of the image segmentation phase. It also enables feature extraction and the determination of feature correspondence in image and object space in an automatic way, i.e., without operator interference. Results obtained from simulated and real data show that highly accurate space resection parameters are obtained as well as a progressive processing time reduction. The obtained accuracy, the automatic correspondence process, and the short related processing time show that the proposed approach can be used in many real-time machine vision systems, making possible the implementation of applications not feasible until now.

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.

Classificação automática de padrões projetados por um sistema de luz estruturada

One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.

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.

Um modelo para orientação de imagens orbitais baseado em feições retas

The aim of this paper is to present a model for orientation of pushbroom sensors that allows estimating the polynomial coefficients describing the trajectory of the platform, using linear features as ground control. Considering that pushbroom image acquisition is not instantaneous, six EOP (Exterior Orientation Parameters) for each scanned line must be estimated. The sensor position and attitude parameters are modeled with a time dependent polynomial. The relationship between object and image space is established through a mathematical model based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The equivalence property between planes was adapted to consider the pushbroom geometry. Some experiments with simulated data corresponding to CBERS scene (China-Brazil Earth Resource Satellite) were accomplished in order to test the developed model using straight lines. Moreover, experiments with points ground with the model based on collinearity equations adapted to the pushbroom geometry were also accomplished. The obtained results showed that the proposed model can be used to estimate the EOP of pushbroom images with suitable accuracy.

Um estudo sobre o osu do modelo de cor RGB no proceso de correspondência por correlação

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)