Proceedings International Workshop on Mobile Mapping Technology

li, rongxing

Bibliographic data

Monograph

Persistent identifier:: 856671290

Author:: Li, Rongxing

Title:: Proceedings International Workshop on Mobile Mapping Technology

Sub title:: April 21 - 23, 1999, Bangkok, Thailand

Scope:: 1 Online-Ressource (Getr. Zählung [ca. 400 Seiten])

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856671290

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Session 7A: Automatic Object Extraction and Recognition]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INTEGRATION OF FEATURE AND SIGNAL MATCHING FOR OBJECT SURFACE EXTRACTION. Pakom Apaphant, James Bethel.

Document type:: Monograph

Structure type:: Chapter

7A-1-1 INTEGRATION OF FEATURE AND SIGNAL MATCHING FOR OBJECT SURFACE EXTRACTION Pakom Apaphant School of Civil Engineering Purdue University U.S.A. pakom@purdue.edu James Bethel School of Civil Engineering Purdue University U.S.A. bethel @ecn.purdue.edu KEY WORDS: Dynamic Programming, Stereo Matching, Feature Extraction, Signal Matching, Object Surface Reconstruction. ABSTRACT An integrated image matching method for object surface reconstruction is developed. The proposed method is rigorously based on photogrammetric principles incorporating some aspects of image understanding and computer vision. The matching problems are addressed by the integration of signal and feature matching. The innovative strategy arises within the framework of global optimization of the match function by dynamic programming. To increase computing speed, the algorithm is designed in such a way that it can be implemented in either a parallel or a sequential computing system. To evaluate the performance of the algorithm, images of both urban and non-urban scenes have been tested. The experiments have shown promising results using this approach. 1 INTRODUCTION Complicated stereo matching problems are found in large scale images of urban scenes. Feature based matching (Medioni and Nevada, 1985), (Ayache and Faverjon, 1987), (Dhond and Aggarwal, 1989) has been used to address these problems. However the extraction process may yield ambiguous or incorrect solutions. Although successful feature matching may be achieved, the information from matched features is still not sufficient to reconstruct an entire object surface model. Signal based matching (Forstner, 1984), (Lobonc, 1996) can also be used for solving such a problem. This approach can work with images of rural scenes. Its results, however, suffer when the assumption that the terrain surface has smooth slope is violated. Many matching methods tend to be based on only one of these approaches. This trade-off is a dilemma in the matching process. In this research, the problems are addressed by the integration of signal and feature matching. Difficulties in matching can be exacerbated by badly chosen target search strategies. Using dynamic programming, object surface generation can be reduced to the problem of an optimal profile search from a cost matrix generated by a given stereo pair. Dynamic programming requires that the problem must be viewed as a multistage problem with interdependent variables. This technique generally consists of three parts which are (1) extracting the features to be matched and their descriptive attributes (2) generating a correspondence cost matrix and (3) searching for the optimal path in the cost matrix. Bernard (Benard, 1984) and Ohta and Kanade (Ohta and Kanade, 1985) can be regarded as the first groups among others who applied this concept to the stereo matching problem. Most current dynamic programming based matching techniques have been based on their frameworks (Lloyd et al., 1987), (Liu and Skerjanc, 1993), (Geiger et al., 1995), (Rojas et al., 1997). Dynamic programming has been proven to be an effective though time consuming strategy for stereo matching. Previous researchers applied this technique only for feature matching. In this research, the innovative strategy is the integration of feature and signal matching within the framework of the global optimization. Our matching process combines feature matching and signal matching, and into an integrated algorithm. The sequence of steps for feature matching would entail extracting features along epipolar lines in the images, matching the features on the left and right images. The conventional feature matching method by dynamic programming was modified to make the algorithm more robust. Then the feature coordinates are determined in the object space. After that the signal matching technique is applied to generate the elevation profiles. To increase the reliability of the searching technique, 3D information obtained from feature matching is integrated and used as constraints. The algorithm was based on line following by dynamic programming. (Bethel et al., 1998). Once all profiles in a model are determined, the surface in the object space can finally be reconstructed. To evaluate the performance of the algorithm, imageries of both urban and rural scenes have been tested. The experiments have shown promising results using this approach. 2 PRIMITIVE FEATURE EXTRACTION Several types of primitive features are extracted from images, along with their positions in epipolar space. They are low level features which contain useful information for the matching process. These features are straight lines, edges, spikes, and plateaus. They not only contain significant information but also are not too difficult to extract. For all of these primitive features, either the extraction is done directly in the ID epipolar space, or the extraction is done in the 2D image space, and subsequently intersected with the ID epipolar lines. Straight line Extraction Straight line features occur predominantly in built-up or urban areas. This feature can contain much useful information for matching. In addition to the edge location; orientation, adjacent gray levels, and edge gradient are a few descriptive attributes. Generally, a straight line extraction process starts from extracting edge information, i.e. gradient magnitude and orientation. And then some refinements may be applied for line linking and line filtering. They are usually based on straight line parameters such

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