一种反射折射摄像机的简易标定方法

Central catadioptric cameras are widely used in virtual reality and robot navigation,and the camera calibration is a prerequisite for these applications.In this paper,we propose an easy calibration method for central catadioptric cameras with a 2D calibration pattern.Firstly,the bounding ellipse of the catadioptric image and field of view (FOV) are used to obtain the initial estimation of the intrinsic parameters.Then,the explicit relationship between the central catadioptric and the pinhole model is used to initialize the extrinsic parameters.Finally,the intrinsic and extrinsic parameters are refined by nonlinear optimization.The proposed method does not need any fitting of partial visible conic,and the projected images of 2D calibration pattern can easily cover the whole image,so our method is easy and robust.Experiments with simulated data as well as real images show the satisfactory performance of our proposed calibration method.     

Radiance-based color calibration for image-based modeling with multiple cameras

Photo-consistency estimation is an important part for many image-based modeling techniques.This paper presents a novel radiance-based color calibration method to reduce the uncertainty of photo-consistency estimation across multiple cameras.The idea behind our method is to convert colors into a uniform radiometric color space in which multiple image data are corrected.Experimental results demonstrate that our method can achieve comparable color calibration effect without adjusting camera parameters and is more robust than other existing method.Additionally,we obtain an auto-determined threshold for photo-consistency check,which will lead to a better performance than existing photo-consistency based reconstruction algorithms.     

一种基于自然图像统计模型的图像合成检测方法

Nowadays, digital images can be easily tampered due to the availability of powerful image processing software. As digital cameras continue to replace their analog counterparts, the importance of authenticating digital images, identifying their sources, and detecting forgeries is increasing. Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature. Image compositing is the most common form of digital tampering. Assuming that image compositing operations affect the inherent statistics of the image, we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain. The generalized Gaussian model (GGD) is employed to describe the marginal distribution of wavelet coefficients of images, and the parameters of GGD are obtained using maximum-likelihood estimator. The statistical features include GGD parameters, prediction error, mean, variance, skewness, and kurtosis at each wavelet detail subband. Then, these feature vectors are used to discriminate between natural images and composite images using support vector machine (SVM). To evaluate the performance of our proposed method, we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset, and achieved a detection accuracy of 92% and 79%, respectively. The detection performance of our method is better than that of the method using camera response function on the same dataset.     

Estimating fisheye camera parameters from homography

This paper presents a method to linearly estimate fisheye camera model parameters from the homography induced by the space plane between two fisheye images.The homography is firstly calculated by using four feature points,instead of three points on the same line,in the fisheye image.And the constraint on the model parameters of fisheye camera can be derived from the homography under the assumption that fisheye camera model is a polynomial model.Then the model parameters for different order polynomials are computed.The proposed technique requires only multiple fisheye images to include a planar scene and need not a priori knowledge of 3D coordinate of the planar scene.Experimental results with synthetic data and real fisheye images demonstrate the validity of our method.The method can also be extended to fisheye image of other planar scene excluding the planar calibration object.     

Halftoning based algorithms for image hiding

Digital halftoning is an important process to convert a continuous-tone image into a binary image with pure black and white pixels. This process is necessary when printing a monochrome or color image by a printer with limited number of ink colors. The main contribution of this paper is to present a halftoning method that conceals a binary image into two binary images. More specifically, three distinct gray scale images are given, such that one of them should be hidden in the other two gray scale images. Our halftoning method generates three binary images that reproduce the tone of the corresponding original three gray scale images. Quite surprisingly, the secret binary image can be seen by overlapping the other two binary images. In other words, the secret binary image is hidden in two public binary images. Also, it is very hard to guess the secret images using only one of the two public images, and both of these two public images are necessary to get the secret image. Another contribution of this paper is to extend our halftoning method to hide one image and more than one image into more than two images. The resulting images show that our halftoning method hides and recovers the original images. Hence, our halftoning technique can be used for watermarking as well as amusement purpose.     

A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensics

Non-intrusive digital image forensics(NIDIF)is a novel approach to authenticate the trustworthiness of digital images.It works by exploring varieties of intrinsic characteristics involved in the digital imaging,editing,storing processes as discriminative features to reveal the subtle traces left by a malicious fraudster.The NIDIF for the lossy JPEG image format is of special importance for its pervasive application.In this paper,we propose an NIDIF framework for the JPEG images.The framework involves two complementary identification methods for exposing shifted double JPEG(SD-JPEG)compression artifacts,including an improved ICA-based method and a First Digits Histogram based method.They are designed to treat the detectable conditions and a few special undetectable conditions separately.Detailed theoretical justifications are provided to reveal the relationship between the detectability of the artifacts and some intrinsic statistical characteristics of natural image signal.The extensive experimental results have shown the efectiveness of the proposed methods.Furthermore,some case studies are also given to demonstrate how to reveal certain types of image manipulations,such as cropping,splicing,or both,with our framework.     

Text extraction and enhancement of binary images using cellular automata

Text characters embedded in images represent a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values, and complex backgrounds. Existing methods cannot handle well those texts with different contrast or embedded in a complex image background. In this paper, a set of sequential algorithms for text extraction and enhancement of image using cellular automata are proposed. The image enhancement includes gray level, contrast manipulation, edge detection, and filtering. First, it applies edge detection and uses a threshold to filter out for low-contrast text and simplify complex background of high-contrast text from binary image. The proposed algorithm is simple and easy to use and requires only a sample texture binary image as an input. It generates textures with perceived quality, better than those proposed by earlier published techniques. The performance of our method is demonstrated by presenting experimental results for a set of text based binary images. The quality of thresholding is assessed using the precision and recall analysis of the resultant text in the binary image.     

Illumination Morphing: Generating a Smooth Change of Illumination between Two Color Images

The paper proposes a method for generating a sequence of images with smooth change of illumination from two input images with different lighting conditions. The idea of the proposed method is based on image morphing. While conventional image morphing changes object shapes between two input images, here we focus on changing the illumination between two images. The proposed method uses isoluminance curves as a feature primitive. Isoluminance curves acquired from images are warped based on the correspondence of the curves between two images, and transformed luminance distributions are generated from the warped isoluminance curves. The proposed method called "illumination morphing" is able to generate smooth transition of luminance between two color images. The method does not need even the information about the light sources and 3D object models. The proposed method is a promising technique for many applications requiring a scene with variety of lighting effects, such as movies, TV games, and so on.     

Design and implementation of vision-based transport for a mobile robot via Kalman filter

A vision-based scheme for object recognition and transport with a mobile robot is proposed in this paper. First, camera calibration is experimentally performed with Zhenyou Zhang’s method, and a distance measurement method with the monocular camera is presented and tested. Second, Kalman filtering algorithm is used to predict the movement of a target with HSI model as the input and the seed filling algorithm as the image segmentation approach. Finally, the motion control of the pan-tilt camera and mobile robot is designed to fulfill the tracking and transport task. The experiment results demonstrate the robust object recognition and fast tracking capabilities of the proposed scheme.     

Adaptive walking control of quadruped robots based on central pattern generator （CPG） and reflex

This paper presents a central pattern generator (CPG) and vestibular reflex combined control strategy for a quadruped robot. An oscillator network and a knee-to-hip mapping function are presented to realize the rhythmic motion for the quadruped robot. A two-phase parameter tuning method is designed to adjust the parameters of oscillator network. First, based on the numerical simulation, the influences of the parameters on the output signals are analyzed, then the genetic algorithm (GA) is used to evolve the phase relationships of the oscillators to realize the basic animal-like walking pattern. Moreover, the animal’s vestibular reflex mechanism is mimicked to realize the adaptive walking of the quadruped robot on a slope terrain. Coupled with the sensory feedback information, the robot can walk up and down the slope smoothly. The presented bio-inspired control method is validated through simulations and experiments with AIBO. Under the control of the presented CPG and vestibular reflex combined control method, AIBO can cope with slipping, falling down and walk on a slope successfully, which demonstrates the effectiveness of the proposed walking control method.     

Self-calibration of hybrid central catadioptric and perspective cameras

Hybrid central catadioptric and perspective cameras are desired in practice, because the hybrid camera system can capture large field of view as well as high-resolution images. However, the calibration of the system is challenging due to heavy distortions in catadioptric cameras. In addition, previous calibration methods are only suitable for the camera system consisting of perspective cameras and catadioptric cameras with only parabolic mirrors, in which priors about the intrinsic parameters of perspective cameras are required. In this work, we provide a new approach to handle the problems. We show that if the hybrid camera system consists of at least two central catadioptric and one perspective cameras, both the intrinsic and extrinsic parameters of the system can be calibrated linearly without priors about intrinsic parameters of the perspective cameras, and the supported central catadioptric cameras of our method can be more generic. In this work, an approximated polynomial model is derived and used for rectification of catadioptric image. Firstly, with the epipolar geometry between the perspective and rectified catadioptric images, the distortion parameters of the polynomial model can be estimated linearly. Then a new method is proposed to estimate the intrinsic parameters of a central catadioptric camera with the parameters in the polynomial model, and hence the catadioptric cameras can be calibrated. Finally, a linear self-calibration method for the hybrid system is given with the calibrated catadioptric cameras. The main advantage of our method is that it cannot only calibrate both the intrinsic and extrinsic parameters of the hybrid camera system, but also simplify a traditional nonlinear self-calibration of perspective cameras to a linear process. Experiments show that our proposed method is robust and reliable.     

Calibration of Central Catadioptric Cameras Using a DLT-Like Approach

In this study, we present a calibration technique that is valid for all single-viewpoint catadioptric cameras. We are able to represent the projection of 3D points on a catadioptric image linearly with a 6×10 projection matrix, which uses lifted coordinates for image and 3D points. This projection matrix can be computed from 3D–2D correspondences (minimum 20 points distributed in three different planes). We show how to decompose it to obtain intrinsic and extrinsic parameters. Moreover, we use this parameter estimation followed by a non-linear optimization to calibrate various types of cameras. Our results are based on the sphere camera model which considers that every central catadioptric system can be modeled using two projections, one from 3D points to a unitary sphere and then a perspective projection from the sphere to the image plane. We test our method both with simulations and real images, and we analyze the results performing a 3D reconstruction from two omnidirectional images.     

Identical Projective Geometric Properties of Central Catadioptric Line Images and Sphere Images with Applications to Calibration

Central catadioptric cameras are imaging devices that use mirrors to enhance the field of view while preserving a single effective viewpoint. Lines and spheres in space are all projected into conics in the central catadioptric image planes, and such conics are called line images and sphere images, respectively. We discovered that there exists an imaginary conic in the central catadioptric image planes, defined as the modified image of the absolute conic (MIAC), and by utilizing the MIAC, the novel identical projective geometric properties of line images and sphere images may be exploited: Each line image or each sphere image is double-contact with the MIAC, which is an analogy of the discovery in pinhole camera that the image of the absolute conic (IAC) is double-contact with sphere images. Note that the IAC also exists in the central catadioptric image plane, but it does not have the double-contact properties with line images or sphere images. This is the main reason to propose the MIAC. From these geometric properties with the MIAC, two linear calibration methods for central catadioptric cameras using sphere images as well as using line images are proposed in the same framework. Note that there are many linear approaches to central catadioptric camera calibration using line images. It seems that to use the properties that line images are tangent to the MIAC only leads to an alternative geometric construction for calibration. However, for sphere images, there are only some nonlinear calibration methods in literature. Therefore, to propose linear methods for sphere images may be the main contribution of this paper. Our new algorithms have been tested in extensive experiments with respect to noise sensitivity.     

Calibrating effective focal length for central catadioptric cameras using one space line

In camera calibration, focal length is the most important parameter to be estimated, while other parameters can be obtained by prior information about scene or system configuration. In this paper, we present a polynomial constraint on the effective focal length with the condition that all the other parameters are known. The polynomial degree is 4 for paracatadioptric cameras and 16 for other catadioptric cameras. However, if the skew is 0 or the ratio between the skew and effective focal length is known, the constraint becomes a linear one or a polynomial one with degree 4 on the effective focal length square for paracatadioptric cameras and other catadioptric cameras, respectively. Based on this constraint, we propose a simple method for estimation of the effective focal length of central catadioptric cameras. Unlike many approaches using lines in literature, the proposed method needs no conic fitting of line images, which is error-prone and highly affects the calibration accuracy. It is easy to implement, and only a single view of one space line is enough with no other space information needed. Experiments on simulated and real data show this method is robust and effective.     

Plane-Based Calibration for Linear Cameras

Linear or 1D cameras are used in several areas such as industrial inspection and satellite imagery. Since 1D cameras consist of a linear sensor, a motion (usually perpendicular to the sensor orientation) is performed in order to acquire a full image. In this paper, we present a novel linear method to estimate the intrinsic and extrinsic parameters of a 1D camera using a planar object. As opposed to traditional calibration scheme based on 3D-2D correspondences of landmarks, our method uses homographies induced by the images of a planar object. The proposed algorithm is linear, simple and produces good results as shown by our experiments.     

基于单幅图片的相机完全标定

现有相机标定方法的标定过程比较繁琐,不利于标定相机的广泛使用。为此,从摄像机镜头畸变矫正着手,结合标定板信息及消失点约束,提出一种基于单张图片的相机标定方法。利用非线性迭代得到相机镜头的畸变系数,通过线性求解得出相机的内参,直接计算得到相机的外参,从而实现仅需拍摄单张标定板图片的相机完全标定。实验结果表明,该方法在标定板与视平面夹角小于45°的情况下均能成功标定,并且重投影误差小于0.3像素。     

Catadioptric camera calibration using geometric invariants

Central catadioptric cameras are imaging devices that use mirrors to enhance the field of view while preserving a single effective viewpoint. In this paper, we propose a novel method for the calibration of central catadioptric cameras using geometric invariants. Lines and spheres in space are all projected into conics in the catadioptric image plane. We prove that the projection of a line can provide three invariants whereas the projection of a sphere can only provide two. From these invariants, constraint equations for the intrinsic parameters of catadioptric camera are derived. Therefore, there are two kinds of variants of this novel method. The first one uses projections of lines and the second one uses projections of spheres. In general, the projections of two lines or three spheres are sufficient to achieve catadioptric camera calibration. One important conclusion in this paper is that the method based on projections of spheres is more robust and has higher accuracy than that based on projections of lines. The performances of our method are demonstrated by both the results of simulations and experiments with real images.     

A calibration method for paracatadioptric camera from sphere images

For paracatadioptric camera, the estimation of intrinsic parameters from sphere images is still an open and challenging problem. In this paper, we propose a calibration method for paracatadioptric camera based on sphere images, which only requires that the projected contour of parabolic mirror is visible on the image plane in one view. We have found that, under central catadioptric camera, a sphere is projected to two conics on the image plane, which are defined as a pair of antipodal sphere images. The conic that is visible on the image plane is called the sphere image, while the other invisible conic is called the antipodal sphere image. In the other aspect, according to the image formation of central catadioptric camera, these two conics can also be considered as the projections of two parallel circles on the viewing sphere by a virtue camera. That is to say, if three pairs of antipodal sphere images are known, central catadioptric camera can be directly calibrated by the calibration method based on two parallel circles. Therefore, the problem of calibrating central catadioptric camera is transferred to the estimations of sphere images and their antipodal sphere images. Based on this idea, we first initialize the intrinsic parameters of the camera by the projected contour of parabolic mirror, and use them to initialize the antipodal sphere images. Next, we study properties of several pairs of antipodal sphere images under paracatadioptric camera. Then, these properties are used to optimize sphere images and their antipodal sphere images, so as to calibrate the paracatadioptric camera. Experimental results on both simulated and real image data have demonstrated the effectiveness of our method.     

基于空间共线点的单光心反射折射摄像机标定

一条空间直线的单光心反射折射图像是一个二次曲线段, 大多数利用直线进行单光心反射折射摄像机标定的方法都需要对直线的像进行二次曲线拟合, 曲线拟合的精度严重影响着标定的精度. 然而, 一条空间直线的像仅占整个二次曲线的一小段, 这使得曲线拟合的效果非常差. 本文利用空间三个共线点的反射折射投影给出了摄像机内参数的一个非线性约束. 当反射镜面为抛物面时, 在主点已知的情况下, 该约束变为线性约束. 如其他参数已知, 该约束变为关于有效焦距的多项式约束. 由此, 本文提出了三种不同条件下的标定算法, 算法中无需对直线的像进行二次曲线拟合, 无需场景的任何信息, 标定精度较高. 实验验证了算法的有效性.