Panoramic Image Mosaics Research Articles

旋转扫描序列图像的全景图拼接方法

旋转扫描序列图像的全景图拼接方法

Rover Localization and Landing-Site Mapping Technology for the 2003 Mars Exploration Rover Mission

SUMMARY The above introduced the technology and experiments for rover localization and landing sitemapping in the 2003 MER mission. At first, Mars global and landing site local reference systemsare elucidated. The initial rover position will be obtained through a triangulation usingobservations on orbital images and the very first set of surface images if common landmarks canbe found in these images. This location can then be improved and verified by UHF two-wayDoppler tracking and VLBI technology. As more ground images are acquired, landmarks seen inboth ground images and orbital images can be used to update landing site locations in the globalMars body-fixed reference system. Onboard rover localization techniques will perform roverlocalization tasks in real-time. The application of visual odometry will improve localization byovercoming problems associated with wheel odometry such as slippage and low accuracy.Finally, the bundle adjustment based rover localization method will build an image networkacquired by Pancam, Navcam and Hazcam cameras, as well as orbital images (such as Viking,MOC NA, and THEMIS images). The developed incremental and integrated bundle adjustmentmodels will supply improved rover locations and image orientation parameters, which are criticalfor generation of high quality landing site topographic mapping products. Based on the field testsperformed on Earth and Mars (MPF mission data), we expect that a 1 percent or better roverlocalization accuracy can be achieved during this mission. In addition, the bundle adjustmentresults will also enable us to produce high precision landing site topographic mapping productsincluding seamless panoramic image mosaics, DTM, and orthophotos.

An efficient method to build panoramic image mosaics

This paper describes an efficient method to build panoramic image mosaics with multiple images. Conventional algorithms used geometrical feature points and optimization to compute the projective transformation, which is the relation between two consecutive images. However, building a panoramic image was very time consuming because of the iterative computation involved. The proposed method computed the projective transformation in overlapped areas of the two given images by using four seed points. The seed point is the highly textured point in the overlapped area of the reference image, which is extracted by using phase correlation. Because the region of interest (ROI) was restricted within overlapped areas of two images, more accurate correspondences were obtained. Before selecting the seed point, the histograms of the overlapped areas were equalized to mitigate the variation of the illumination conditions. After selecting the seed point, the weighted block matching algorithm (BMA) was used to minimize image distortion caused by camera rotation. An experiment was performed employing the proposed method with various images and the results were compared with peak signal to noise ratio (PSNR). Results showed that the proposed method built high-quality panoramic image mosaics in high speed.

Fast Panoramic Image Mosaicing Using One-dimensional Flow Estimation

In this paper, we propose a fast method for panoramic image mosaicing using one-dimensional (1D) flow estimation. First, the 1D flow vectors are estimated at each pixel in x and y directions. Then the global affine parameters are estimated from 1D flows using the iterative least-squares method. The accurate parameters are obtained by iteratively repeated optimization in x and y directions. We have implemented the proposed method on a conventional low-cost PC. The experimental results show that the image mosaicing is possible in near-real time.

Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment

This paper presents a complete system for constructing panoramic image mosaics from sequences of images. Our mosaic representation associates a transformation matrix with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In particular, to construct a full view panorama, we introduce a rotational mosaic representation that associates a rotation matrix (and optionally a focal length) with each input image. A patch-based alignment algorithm is developed to quickly align two images given motion models. Techniques for estimating and refining camera focal lengths are also presented. In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimally registered image mosaic. To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we use a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras. We also present an inverse texture mapping algorithm for efficiently extracting environment maps from our panoramic image mosaics. By mapping the mosaic onto an arbitrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.

Constructing cylindrical panoramic image usingequidistant matching

An efficient algorithm for constructing cylindrical panoramic image mosaics from a near pure panning camera is proposed. The algorithm uses equidistant matching for image alignment, which uses a translational motion model. It also allows the effective focal length of the camera to be estimated efficiently by a bisection method.

Complex wavelet-based image mosaics using edge-preserving visual perception modeling

This paper presents a robust panoramic image mosaics scheme in the complex wavelet domain that employs the edge-preserving visual preceptive thresholding techniques. Complex wavelet transform guarantees both the scale and translation invariance for the image alignment, which enables us to calculate the transformation based on the correspondence in the wavelet domain and its application to the original image. The transformation obtained from the wavelet images is sufficiently accurate when applied to the original images due to the scale and translation invariance. In the wavelet domain, the coefficients can be compressed significantly (50–100 : 1 ratios) through the edge-preserving visual perception modeling while the details of the structured information can be retained. The complexity of the transformation calculation on the thresholded wavelet coefficients can be significantly reduced. Moreover, the transformation can be progressively refined through the multiresolution wavelet decomposition. It results in a mosaicking scheme with a great robustness and significantly better performance compared with the traditional mosaicking techniques with no or little loss of the visual quality of the images.

Correction to Construction of Panoramic Image Mosaics with Global and Local Alignment

An important element of this framework, which we exploit, is to perform the motion estimation between the current new input image and a warped (resampled) version of the mosaic. This allows us to estimate only incremental deformations of images (or equivalently, instantaneous motion), which greatly simplifies the computation of the gradients and Hessians required in our gradient descent algorithm (e.g., compare the Hessians computed below with those presented in Szeliski, 1996). Thus, to register two images I0(x) and I1(x′), where x′ is computed using some parametric motion model m, i.e., x′ = f(x; m), we first compute the warped image