Artur Nowakowski

Lens Radial Distortion Calibration Using Homography of Central Points

We present a novel method for radial lens distortion calibration which results in high accuracy of compensation. It is based on single image of planar chessboard pattern and uses the extracted distorted grid of points. Due to homographic approach, no special alignment of the camera with regard to the calibration object is required. Undistorted grid is determined from the central points of the image and used to find the radial distortion model using linear least square method (LSM). The model is used for dense compensation by bilinear interpolation or for sparse compensation by Newton iterative scheme.

Fast computation of thresholding hysteresis for edge detection

The fast implementation of thresholding hysteresis for edge detection is presented. Hysteresis, the optional final part of edge detection algorithms, aims at finding these parts of real edges, which were not detected, because their strength is depreciated by the noise in image. Hysteresis can be effectively computing using connected components analysis from graph theory. Using algorithm presented in [2] called in this paper as UNION-FIND the computation of hysteresis can be done with referring to the gradient information only in two image passes.

Analysis of Brown camera distortion model

Contemporary image acquisition devices introduce optical distortion into image. It results in pixel displacement and therefore needs to be compensated for many computer vision applications. The distortion is usually modeled by the Brown distortion model, which parameters can be included in camera calibration task. In this paper we describe original model, its dependencies and analyze orthogonality with regard to radius for its decentering distortion component. We also report experiments with camera calibration algorithm included in OpenCV library, especially a stability of distortion parameters estimation is evaluated.

Filter-less Gray Patterns Detection in 3D Modeling by Structured Light

We present a novel technique of image data extraction for calibration and modeling purposes. It employs structured light approach using sequence of Gray stripes patterns for points localization and indexation. Code images for the indexation are build based on simple classification of pixel color. Points co-ordinates are extracted from intersection of code image edges without using time consuming filtering with moving window operator. UNION-FIND algorithm is used for removing spurious edge points appearing due to noise presence. Technique results in high accuracy and reliability of extracted points.

Camera calibration by linear decomposition

This paper presents an algorithm for camera calibration, applying digital images to calculate camera parameters, position and orientation. A linear decomposition technique is proposed to solve nonlinear pixel equations in which camera parameters are involved.

Calibration of radial lens distortion using a Gaussian model

Abstract. We present a lens distortion model based on the Gaussian function. The model is a potential source function of the popular Brown distortion model and requires the practical estimation of one Boolean and one real parameter. We also present a general technique for lens distortion identification, which consists of three steps: image data acquisition, localization and indexation/matching of image features, and the estimation of distortion parameters. The method uses a structured light technique, in which bar patterns are indexed by a Gray code. Acquired images are automatically processed using a multistep approach that localizes and indexes calibration points. An iterative analysis of differences between localizations of distorted points and their undistorted counterparts is proposed to estimate distortion model parameters. To compute undistorted localizations, the method estimates a homography matrix that is based on both undistorted data and iterative processing of distorted coordinates, which improves compensation accuracy. Experiments with three cameras show that the indexing strategy significantly decreases compensation error (from 0.26 to 0.09 pixels). The newly introduced Gaussian model is shown to be slightly more accurate and considerably more stable than the popular Brown model.

Stroke Tissue Pattern Recognition Based on CT Texture Analysis

The main objective of this paper is a texture-based solution to the problem of acute stroke tissue recognition on computed tomography images. Our proposed method of early stroke indication was based on two fundamental steps: (i) segmentation of potential areas with distorted brain tissue (selection of regions of interest), and (ii) acute stroke tissue recognition by extracting and then classifying a set of well-differentiating features. The proposed solution used various numerical image descriptors determined in several image transformation domains: 2D Fourier domain, polar 2D Fourier domain, and multiscale domains (i.e., wavelet, complex wavelet, and contourlet domain). The obtained results indicate the possibility of relatively effective detection of early stroke symptoms in CT images. Selected normal or pathological blocks were classified by LogitBoost with the accuracy close to 75 % with the use of our adjusted cross-validation procedure.

Colour in calibration points indexing

The paper presents the way that colour can serve solving the problem of calibration points indexing in a camera geometrical calibration process. We propose a technique in which indexes of calibration points in a black-and-white chessboard are represented as sets of colour regions in the neighbourhood of calibration points. We provide some general rules for designing a colour calibration chessboard and provide a method of calibration image analysis. We show that this approach leads to obtaining better results than in the case of widely used methods employing information about already indexed points to compute indexes. We also report constraints concerning the technique. Nowadays we are witnessing an increasing need for camera geometrical calibration systems. They are vital for such applications as 3D modelling, 3D reconstruction, assembly control systems, etc. Wherever possible, calibration objects placed in the scene are used in a camera geometrical calibration process. This approach significantly increases accuracy of calibration results and makes the calibration data extraction process easier and universal. There are many geometrical camera calibration techniques for a known calibration scene [1]. A great number of them use as an input calibration points which are localised and indexed in the scene. In this paper we propose the technique of calibration points indexing which uses a colour chessboard. The presented technique was developed by solving problems we encountered during experiments with our earlier methods of camera calibration scene analysis [2]-[3]. In particular, the proposed technique increases the number of indexed points points in case of local lack of calibration points detection. At the beginning of the paper we present a way of designing a chessboard pattern. Then we describe a calibration point indexing method, and finally we show experimental results. A black-and-white chessboard is widely used in order to obtain sub-pixel accuracy of calibration points localisation [1]. Calibration points are defined as corners of chessboard squares. Assuming the availability of rough localisation of these points, the points can be indexed. Noting that differences in distances between neighbouring points in calibration scene images differ slightly, one of the local searching methods can be employed (e.g. [2]). Methods of this type search for a calibration point to be indexed, using a window of a certain size. The position of the window is determined by a vector representing the distance between two previously indexed points in the same row or column. However, experiments show that this approach has its disadvantages, as described below. * E-mail: A.Nowakowski@ire.pw.edu.pl Firstly, there is a danger of omitting some points during indexing in case of local lack of calibration points detection in a neighbourhood (e.g. caused by the presence of non-homogeneous light in the calibration scene). A particularly unfavourable situation is when the local lack of detection effects in the appearance of separated regions of detected calibration points. It is worth saying that such situations are likely to happen for calibration points situated near image borders. Such points are very important for the analysis of optical nonlinearities, and a lack of them can significantly influence the accuracy of distortion modelling. Secondly, such methods may give wrong results in the case of optical distortion with strong nonlinearities when getting information about the neighbouring index is not an easy task. Beside this, the methods are very sensitive to a single false localisation of a calibration point. Such a single false localisation can even result in false indexing of a big set of calibration points. To avoid the above-mentioned problems, we propose using a black-and-white chessboard which contains the coded index of a calibration point in the form of colour squares situated in the nearest neighbourhood of each point. The index of a certain calibration point is determined by colours of four nearest neighbouring squares (Fig.1). An order of squares in such foursome is important. Because the size of a colour square is determined only by the possibility of correct colour detection, the size of a colour square can be smaller than the size of a black or white square. The larger size of a black or white square is determined by the requirements of the exact localisation step which follows the indexing of calibration points [3]. In this step, edge information is extracted from a blackand-white chessboard. This edge information needs larger Artur Nowakowski, Wladyslaw Skarbek Institute of Radioelectronics, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warszawa, A.Nowakowski@ire.pw.edu.pl Received February 10, 2009; accepted March 27, 2009; published March 31, 2009 http://www.photonics.pl/PLP