Image Distortion Model Research Articles

Camera calibration optimization algorithm that uses a step function

Camera calibration is very important when planning machine vision tasks. Calibration may involve 3D reconstruction, size measurement, or careful target positioning. Calibration accuracy directly affects the accuracy of machine vision. The parameters in many image distortion models are usually applied to all image pixels. However, this may be associated with rather high pixel reprojection errors at image edges, compromising camera calibration accuracy. In this paper, we present a new camera calibration optimization algorithm that features a step function that splits images into center and edge regions. First, based on the increasing pixel reprojection errors according to the pixel distance away from the image center, we gave a flexible method to divide an image into two regions, center and boundary. Then, the algorithm automatically determines the step position, and the calibration model is rebuilt. The new model can calibrate the distortions at the center and boundary regions separately. Optimized by the method, the number of distortion parameters in the old model is doubled, and different parameters represent different distortions within two regions. In this way, our method can optimize traditional calibration models, which define a global model to describe the distortion of the whole image and get a higher calibration accuracy. Experimentally, the method significantly improved pixel reprojection accuracy, particularly at image edges. Simulations revealed that our method was more flexible than traditional methods.

Model-Free Lens Distortion Correction Based on Phase Analysis of Fringe-Patterns.

The existing lens correction methods deal with the distortion correction by one or more specific image distortion models. However, distortion determination may fail when an unsuitable model is used. So, methods based on the distortion model would have some drawbacks. A model-free lens distortion correction based on the phase analysis of fringe-patterns is proposed in this paper. Firstly, the mathematical relationship of the distortion displacement and the modulated phase of the sinusoidal fringe-pattern are established in theory. By the phase demodulation analysis of the fringe-pattern, the distortion displacement map can be determined point by point for the whole distorted image. So, the image correction is achieved according to the distortion displacement map by a model-free approach. Furthermore, the distortion center, which is important in obtaining an optimal result, is measured by the instantaneous frequency distribution according to the character of distortion automatically. Numerical simulation and experiments performed by a wide-angle lens are carried out to validate the method.

Iterative calibration of a multiview 3D display with linear extrinsic crosstalk using camera feedback.

In this paper, we propose a multiview 3D display calibration algorithm that compensates for misalignment between constituent layers. To minimize distortion of the 3D image due to misalignment, we employ an iterative algorithm using camera feedback. We first establish a mathematical model for the multiview display and predict distortion of a test image pattern due to misalignment. Then, we define two characteristic features of the feedback camera image that indicate the status of the alignment. Based on the mathematical model, we design a compensation algorithm that consists of cascaded one-dimensional root-finding blocks for two-dimensional optimization. Finally, we experimentally verify the image distortion model, convergence of the iterative algorithm, and image quality improvement. The accuracy of the proposed algorithm exceeds the required accuracy for linear extrinsic crosstalk compensation.

Image quality metric based on regular structure features

The objective assessment of image quality has been extensively studied in recent years, and a large number of evaluation algorithms have been presented. The existing evaluation algorithms of image quality has already exhibited satisfactory performances in many common image distortion models, such as additive noise, grayscale adjustment, low pass filter, motion blurring, lossy compression, etc. However, most of them have no effect on the geometric distortion, which is widely existed in the digital images. In view of geometric distortion, a full-reference image quality assessment method is presented in this paper. The core idea of this method is that only the geometric distortion acting on regular structure features of image can have significant impacts on the perceptive quality of the image, and the physical strength and spatial irregularity of geometric distortion are the main factors causing the decline of image quality. According to experimental data, the proposed assessment method can be well applied to geometric distortion model, the evaluation results are better than most of the existing methods, and it has showed significant correlation with MOS.

３ＤディジタルカメラＦｉｎｅＰｉｘ ＲＥＡＬ ３Ｄ Ｗ１のキャリブレーション

The 3D digital camera FinePix REAL 3D W1, which was designed to take a pair of stereo images for stereo viewing, was released by FUJIFILM Cooperation in August 2009. The aim of our study is to evaluate camera calibration methods for measuring dimensions of an object on a sub-meter scale by using a pair of stereo images acquired by the camera without any controls. Three camera calibration methods were evaluated by estimation errors of length of line segments measured without any controls. The experiment results indicate that the best calibration method for the camera would be the method that ordinary camera calibration is first executed for each imaging unit in order to estimate its image distortion model, and then alignment between two imaging units is estimated by using two sets of the exterior orientation parameters of both imaging units obtained in the ordinary camera calibration executed previously.

Improving efficiency and effectiveness of the image distortion model

The image distortion model (IDM) is a deformation model that is used for computing the (dis-)similarity between images. Therefore it evaluates displacements of individual pixels between two images within a so-called warp range and also takes into account the surrounding pixels (local context). It can be used with a kNN classifier and has shown good retrieval quality in handwritten character recognition as well as in past runs of the medical automatic annotation task of ImageCLEF workshops. However, one of its limitations is computational complexity and the resulting long query response times, that may limit its use for a wider range of applications and for modifications to further improve retrieval quality. In particular an enlarged local context and warp range are candidates for such improvements, but would even further increase computational complexity. In our approach, we therefore apply several optimizations that reduce the retrieval time without degrading the result quality. First, we use an early termination strategy for the individual distance computations which contribute a speedup of a factor of 4.3–4.9. Second, we make efficient use of multithreading. With these extensions, we are able to perform the IDM in less than 1.5 s per query on an 8-way server and 16 s on a standard Pentium 4 PC without any degradation of retrieval quality compared to the non-optimized version. We extend the possible displacements to an area of 7 × 7 pixels, using a local context of either 5 × 5 or 7 × 7 pixels. The results of the extended IDM have been submitted to the medical automatic annotation task of ImageCLEF 2007 and were ranked in the upper third. More importantly, the used techniques for reducing the execution time are not limited strictly to IDM but are also applicable to other expensive distance measures.

非計測用ディジタルカメラのキャリブレーション結果の精密さに関する検討

Although many camera calibration methods for a non-metric digital camera have been proposed, there are few reports on precision of calibration results. Therefore, we conducted a field experiment to examine precision of calibration results. We adopted a calibration method using a set of calibration points distributed on the 2-D plane. A round of camera calibration utilized a set of eight convergent images acquired from eight different directions with four different camera frame rotation angles of 0°, +90°, +180° and -90° around the optical axis of the camera. 32 rounds of camera calibration for each camera were conducted. Moreover, a numerical simulation of 3-D measurement using the obtained image distortion model was carried out to investigate the influence of the difference between the obtained image distortion models. Experimental results indicated that dispersions of image distortions between obtained image distortion models cannot be neglected. Difference of image distortions may make more than twenty times larger errors at 3-D measurement without an exterior orientation. The most part of the difference between estimated image distortions was the difference of the estimated position of the principal point, while the differences of another components of the image distortion model were small enough to be negligible.

非計測用ディジタルカメラのキャリブレーションにおける幾何歪みモデルの未知パラメータの選択に関する検討

非計測用ディジタルカメラのキャリブレーションにおける幾何歪みモデルの未知パラメータの選択に関する検討

A Study on Effectiveness of Control Points on the 2D plane for Calibration of Non-metric Camera

Many camera calibration methods for a non-metric camera have been proposed. Some of them use a set of control points distributed on the 2D plane instead of a set of control points distributed in the 3D space. However there are few reports to demonstrate effectiveness of camera calibration using a set of 2D control points. Therefore, we conducted a numerical simulation in order to examine the effectiveness of camera calibration using a set of 2D control points. In the simulation calibration results obtained by three methods using a set of 2D control points were compared with those obtained by the method using a set of 3D control points with the depth of the distribution from 1/5 to 1/20 of the object distance. Evaluation of calibration results was performed by differences of image distortions calculated at all pixels on the image between the obtained image distortion model and the setup one. The simulation results indicate that the calibration methods using a set of 2D calibration points can provide a reliable set of camera parameters.

Statistical framework for model-based image retrieval in medical applications

Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to proto- types; extraction and query-dependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, ana- tomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special empha- sis is placed on invariance, employing a probabilistic model of vari- ability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this six-class problem is an excellent result, taking into account the dif- ficulty of the task. The computed posterior probabilities are further- more used in the subsequent steps of the retrieval process. © 2003