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  • 1
    Electronic Resource
    Electronic Resource
    Multipass hierarchical stereo matching for generation of digital terrain models from aerial images (1998)
    Springer
    Machine vision and applications 10 (1998), S. 280-291 
    Details
    ISSN: 1432-1769
    Keywords: Key words:Computer vision – Stereo matching – Surface reconstruction – DTM generation – Interactive editing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. This paper presents a new multi-pass hierarchical stereo-matching approach for generation of digital terrain models (DTMs) from two overlapping aerial images. Our method consists of multiple passes which compute stereo matches with a coarse-to-fine and sparse-to-dense paradigm. An image pyramid is generated and used in the hierarchical stereo matching. Within each pass, the DTM is refined by using the image pyramid from the coarse to the fine level. At the coarsest level of the first pass, a global stereo-matching technique, the intra-/inter-scanline matching method, is used to generate a good initial DTM for the subsequent stereo matching. Thereafter, hierarchical block matching is applied to image locations where features are detected to refine the DTM incrementally. In the first pass, only the feature points near salient edge segments are considered in block matching. In the second pass, all the feature points are considered, and the DTM obtained from the first pass is used as the initial condition for local searching. For the passes after the second pass, 3D interactive manual editing can be incorporated into the automatic DTM refinement process whenever necessary. Experimental results have shown that our method can successfully provide accurate DTM from aerial images. The success of our approach and system has also been demonstrated with a flight simulation software.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s001380050079
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  • 2
    Electronic Resource
    Electronic Resource
    Asymptotic bayesian surface estimation using an image sequence (1991)
    Springer
    International journal of computer vision 6 (1991), S. 105-132 
    Details
    ISSN: 1573-1405
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new approach is introduced to estimating object surfaces in three-dimensional space from a sequence of images. A 3D surface of interest here is modeled as a function known up to the values of a few parameters. Surface estimation is then treated as the general problem of maximum-likelihood parameter estimation based on two or more functionally related data sets. In our case, these data sets constitute a sequence of images taken at different locations and orientations. Experiments are run to illustrate the various advantages of using as many images as possible in the estimation and of distributing camera positions from first to last over as large a baseline as possible. In order to extract all the usable information from the sequence of images, all the images should be available simultaneously for the parameter estimation. We introduce the use of asymptotic Bayesian approximations in order to summarize the useful information in a sequence of images, thereby drastically reducing both the storage and the amount of processing required. This leads to a sequential Bayesian estimator for the surface parameters, where the information extracted from previous images is summarized in a quadratic form. The attractiveness of our approach is that now all the usual tools of statistical signal analysis, for example, statistical decision theory for object recognition, can be brought to bear; the information extraction appears to be robust and computationally reasonable; the concepts are geometric and simple; and essentially optimal accuracy should result. Experimental results are shown for extending this approach in two ways. One is to model a highly variable surface as a collection of small patches jointly constituting a stochastic process (e.g., a Markov random field) and to reconstruct this surface using maximum a posteriori probability (MAP) estimation. The other is to cluster together those patches constituting the same primitive object through the use of MAP segmentation. This provides a simultaneous estimation and segmentation of a surface into primitive constituent surfaces.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00128152
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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