In the exploration of the other planets of our solar system, images taken during a lander's descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but also provide progressively higher-resolution maps for mission planning. The paper addresses the generation of depth maps from the descent images. Our approach has two steps: motion refinement and depth recovery. During motion refinement, we use an initial motion estimate in order to avoid the intrinsic ambiguity in descending motions. The objective of the motion refinement step is to adjust the motion parameters such that the epipolar constraints are valid between adjacent frames. The depth recovery step correlates adjacent frames to match pixels for triangulation. Due to the descending motion, the conventional rectification process is replaced by a set of anti-aliasing image warpings corresponding to a set of virtual parallel planes. We demonstrate experimental results on synthetic and real descent images.

The Tactical Mobile Robot (TMR) program calls for autonomous mobility in an urban environment. Among all man-made structures which pose as barriers for a mobile robot stairs are the most obvious and ubiquitous structure an autonomous urban robot needs to be able to handle. The Urban II chassis by IS Robotics provides a mechanically simple and elegant way to enable the stair-climbing capability. This paper addresses the issue of using computer vision techniques to control the vehicle automatically when climbing stairs. The algorithm is based in detecting stair edges from a monocular sequence. It is shown that the position and orientation of the stair edges can be used to compute the 3D orientation and position of the vehicle relative to the stairs. Robust examination of those parameters is the key to reliable performance. A combination of techniques such as medium filtering, histogram peak finding, outlier rejection and weighted average are shown to be effective in practice. At the end, we briefly explain our work-in-progress in automatic corner-turning in climbing multiple flights of stairs.

This paper presents a system for creating a full 360-degree panorama from rectilinear images captured from a single nodal position. The solution to the problem is divided into three steps. The first step registers all overlapping images projectively. A combination of a gradient-based optimization method and a correlation-based linear search is found to be robust even in cases of drastic exposure differences and small amount of parallax. The second step takes the projective matrices and their associated hessian matrices as inputs, and calibrates the internal and external parameters of every image through a global optimization. The objective is to minimize the overall image discrepancies in all overlap regions while converting projective matrices into camera parameters such as focal length, aspect ratio, image center, 3D orientation, etc. The third step re-projects all images onto a panorama by a Laplacian-pyramid-based blending. The purpose of blending is to provide a smooth transition between images and eliminate small residues of misalignments resulting from parallax or imperfect pairwise registrations. The blending masks are generated automatically through the grassfire transform. At the end, we briefly explain the necessary human interface for initialization, feedback and manual options.

Correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. This paper provides an in-depth analysis of the major error sources for such a real-time stereo system in the contest of cross-country navigation of an autonomous vehicle. Three major types of errors: foreshortening error, misalignment error and systematic error are identified. The combined disparity errors can easily exceed three-tenths of a pixel, which translates to significant range errors. Upon understanding these error sources, we demonstrate different approaches to either correct them or model their magnitudes without excessive additional computations. By correcting those errors, we show that the precision of the stereo algorithm can be improved by 50%.

Image-based virtual reality is emerging as a major alternative to the more traditional 3D-based VR. The main advantages of the image-based VR are its photoquality realism and 3D illusion without any 3D information. Unfortunately, creating content for image-based VR is usually a very tedious process. This paper proposes to use a non-perspective fisheye lens to capture the spherical panorama with very few images. Unlike most of camera calibration in computer vision, self-calibration of the fisheye lens poses new questions regarding the parameterization of the distortion and wrap-around effects. Because of its unique projection model and large field of view (near 180 degrees), most of the ambiguity problems in self-calibrating a traditional lens can be solved trivially. We demonstrate that with four fisheye lens images, we can seamlessly register them to create the spherical panorama, while self-calibrating its distortion and field of view.

This paper proposes new "hypergeometric" filters for the problem of image matching under the translational and affine model. This new set of filters has the following advantages: (1) High-precision registration of two images under the translational and affine model. Because the window effects are eliminated, we are able to achieve superb performance in both translational and affine matching. (2) Affine matching without exhaustive search or image warping. Due to the recursiveness of the filters in the spatial domain, we are able to analytically express the relation between filter outputs and the six affine parameters. This analytical relation enables us to directly compute these affine parameters. (3) Generality. The approach we demonstrate here can be applied to a broad class of matching problems, as long as the transformation between the two image patches can be mathematically represented in the frequency domain.>

Many low level visual computation problems such as focus, stereo, etc. can be formulated as problems of extracting one or more parameters of a non-stationary transformation between two images. Because of the non-stationary nature, finite-width windows are widely used in various algorithms to extract spatially local information from images. While the choice of window width has a very profound impact on the quality of results of algorithms, there has been no quantitative way to measure or eliminate the negative effects of finite width windows. To address this problem, we introduce a new set of filters, moment filters. Due to their recursiveness in the Fourier and spatial domains, these filters allow the effects of finite-width windows and fore-shortening to be explicitly analyzed and eliminated.

There are two basic problems concerned with Gabor filterings that we will address in this paper. One is the window size problem, in which we will adopt a set of 2D variable window Gabor filters, and compare its performance with those of fixed window filters. We will show that the variable window scheme is more adaptive to image contents. The other problem we will address is the stability of amplitude and phase information resulting from convolving the filters with images. We will extend Fleet's 1D phase stability analysis to 2D phase and amplitude stability analysis based upon the assumption of local resemblance of filter outputs to a single sinusoid. Applications on focus quality measurement and 2D correspondence are described, and the results demonstrate improvements of performance by detecting unstable information using the criterion developed.>

The problem of obtaining depth information from focusing and defocusing is studied. In depth from focusing, instead of the Fibonacci search, which is often trapped in local maxima, the combination of Fibonacci search and curve fitting is proposed. This combination leads to an unprecedentedly accurate result. A model of the blurring effect that takes geometric blurring as well as imaging blurring into consideration in the calibration of the blurring model is proposed. In spectrogram-based depth from defocusing, a maximal resemblance estimation method is proposed to decrease or eliminate the window effect.>