Unmanned planetary landers to date have landed blind, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing sites to very benign terrain and limits the scientific goals of missions. We review sensor options for landing hazard detection, then identify an approach based on stereo vision and shadow analysis that appears to address the broadest set of missions with the lowest cost. We describe algorithms for slope estimation and rock detection with this approach, develop models of their performance, and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach would reduce the probability of failed landing by at least a factor of 4 compared to blind landing. Conversely, for the safety level desired for the 2009 Mars lander, this approach would increase the fraction of the planet that is accessible for landing from about 1/3 to nearly 100%.

For real-time stereo vision systems, the standard method for estimating sub-pixel stereo disparity given an initial integer disparity map involves fitting parabolas to a matching cost function aggregated over rectangular windows. This results in a phenomenon known as pixel-locking, which produces artificially-peaked histograms of sub-pixel disparity. These peaks correspond to the introduction of erroneous ripples or waves in the 3D reconstruction of truly flat surfaces. Since stereo vision is a common input modality for autonomous vehicles, these inaccuracies can pose a problem for safe, reliable navigation. This paper proposes a new method for sub-pixel stereo disparity estimation, based on ideas from Lucas-Kanade tracking and optical flow, which substantially reduces the pixel-locking effect. In addition, it has the ability to correct much larger initial disparity errors than previous approaches and is more general as it applies not only to the ground plane. We demonstrate the method on synthetic imagery as well as real stereo data from an autonomous outdoor vehicle

We present a model-based approach to detecting and describing compositions of buildings with complex rooftops. Previous approaches have dealt with either simpler models or models which lack geometric information. In spite of increasing model complexity, we maintain the computation affordable by effectively using multiple overlapping images. We obtain rooftop hypotheses in 3-D by using 3-D lines and junctions generated from multiple images. Image-derived unedited elevation data is used to assist feature matching, and to generate rough cues of the presence of 3-D structures. Experimental results are shown on complex buildings.

Machine perception can benefit from the use of features extracted from data provided by a variety of sensor modalities. Recent advances in sensor design makes it possible to incorporate multiple sensors into vision systems for increased capability. Two important issues must be considered for the integration task. The sensors must be spatially coregistered and the phenomenologies must be compatible. In this paper we address these issues as they apply to the problem of automatic modeling of building structures from aerial views. We present a methodology to incorporate cues extracted from IFSAR (Interferometric Synthetic Aperture Radar) to significantly improve the performance and the quality of the results of an existing system that relies on electro-optical panchromatic images, while reducing processing time. Quantitative evaluations are given.

3-D building detection and description is a practical application of 3-D object description, a key task of computer vision. We present an approach to detecting and describing buildings of polygonal rooftops by using multiple, overlapping images of the scene. First, 3-D features are generated by using multiple images, and rooftop hypotheses are generated by neighborhood searches on those features. For robust generation of 3-D features, we present a probabilistic approach to address the epipolar alignment problem in line matching. Image-derived unedited elevation data is used to assist feature matching, and to generate rough cues of the presence of 3-D structures. These cues help reduce the search space significantly. Experimental results are shown on some complex buildings.

An effective 3D method incorporating user assistance for modeling complex buildings is proposed. This method utilizes the connectivity and similar structure information among unit blocks in a multi-component building structure, to enable the user to incrementally construct models of many types of buildings. The system attempts to minimize the time and the number of user interactions needed to assist an existing automatic system in this task. Several examples are presented that demonstrate significant improvement and efficiency compared with other approaches and with purely manual systems.

Many applications require detecting structural changes in a scene over a period of time. Comparing intensity values of successive images is not effective as such changes don't necessarily reflect actual changes at a site but might be caused by changes in the view point, illumination and seasons. We take the approach of comparing a 3-D model of the site, prepared from previous images, with new images to infer significant changes. This task is difficult as the images and the models have very different levels of abstract representations. Our approach consists of several steps: registering a site model to a new image, model validation to confirm the presence of model objects in the image; structural change detection seeks to resolve matching problems and indicate possibly changed structures; and finally updating models to reflect the changes. Our system is able to detect missing (or mis-modeled) buildings, changes in model dimensions, and new buildings under some conditions.

We describe a system for detection and description of buildings in aerial scenes. This is a difficult task as the aerial images contain a variety of objects. Low-level segmentation processes give highly fragmented segments due to a number of reasons. We use a perceptual grouping approach to collect these fragments and discard those that come from other sources. We use shape properties of the buildings for this. We use shadows to help form and verify the hypotheses generated by the grouping process. This latter step also provides 3-D descriptions of the buildings. Our system has been tested on a number of examples and is able to work with overhead or oblique views.>

An important application of machine vision is to provide a means to monitor a scene over a period of time and report changes in the content of the scene. We have developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes. By validation we mean the confirmation of the presence of model objects in the image. Our system uses a 3-D site model of the scene as a basis for model validation, and eventually for detecting changes and to update the site model. The scenario for our present validation system consists of adding a new image to a database associated with the site. The validation process is implemented in three steps: registration of the image to the model, or equivalently, determination of the position and orientation of the camera; matching of model features to image features; and validation of the objects in the model. Our system processes the new image monocularly and uses shadows as 3-D clues to help validate the model. The system has been tested using a hand-generated site model and several images of a 500:1 scale model of the site, acquired form several viewpoints.>

The authors have developed a machine to perform the task of automatic registration of color separation films, a process manually performed by skilled professionals in the graphics arts printing industry. The development of such a machine requires overcoming significant challenges: designing a sound computer vision methodology while respecting hard timing constraints, transferring software across platforms and languages, validating the software, building the actual machine around the algorithms, testing the conformity to tolerances, educating operators on the use of such a machine, and having a system robust enough to operate around the clock with no technical supervision. The authors present a brief overview of the problem, followed by the answers they provided to the challenges above.>

The problem of registration of four-color halftone separations for color printing is addressed. A method is presented for automatic registration of color separation films. An operator manually selects two windows from the reference negative (generally cyan) with a digitizing cursor, and each window covering approximately 6 mm/sup 2/ (0.25 in/sup 2/) is digitized into a 640*640 array. On each negative and for each window, the macro edges are extracted, and the contours are approximated by line segments. The segments from corresponding windows on different negatives are then matched with the reference ones to provide an estimate of the translation between them. The two translations (from the two windows) provide the parameters of the rigid planar transform between negatives (rotation and translation) and permit the punching of registration holes into the pictures for each negative. The system has been implemented in the RegiStar machine, built to perform the mechanical tasks associated with the algorithm. It is able to handle a set of four-color separations in about 5 min, from image acquisition to punching of registration holes on the films, maintaining an accuracy of 12 mu m (0.5 mil) for binary patterns and 25 mu m (1 mil) for true halftones. This speed is obtained by using an off-the-shelf Mercury array processor attached to an IBM personal computer.>

We present a technique for computing the convolution of an image with LoG (Laplacian-of-Gaussian) masks. It is well known that a LoG of variance a can be decomposed as a Gaussian mask and a LoG of variance a1 < a. We take advantage of the specific spectral characteristics of these filters in our computation: the LoG is a bandpass filter; we can therefore fold the spectrum of the image (after low pass filtering) without loss of information, which is equivalent to reducing the resolution. We present a complete evaluation of the parameters involved, together with a complexity analysis that leads to the paradoxical result that the computation time decreases when a increases. We illustrate the method on two images.

We present a system that takes a gray level image as input, locates edges with subpixel accuracy, and links them into lines. Edges are detected by finding zero-crossings in the convolution of the image with Laplacian-of-Gaussian (LoG) masks. The implementation differs markedly from M.I.T.'s as we decompose our masks exactly into a sum of two separable filters instead of the usual approximation by a difference of two Gaussians (DOG). Subpixel accuracy is obtained through the use of the facet model [1]. We also note that the zero-crossings obtained from the full resolution image using a space constant ¿ for the Gaussian, and those obtained from the 1/n resolution image with 1/n pixel accuracy and a space constant of ¿/n for the Gaussian, are very similar, but the processing times are very different. Finally, these edges are grouped into lines using the technique described in [2].