XVIIIth Congress: XVIIIth Congress

Kraus, Karl; Waldhäusl, Peter

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Multivolume work

Persistent identifier:: 1667435949

Title:: XVIIIth Congress

Sub title:: Vienna, Austria 1996

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667435949

Language:: English

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1667438379

Title:: XVIIIth Congress

Scope:: 449 Seiten

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667438379

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(31,B2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist anhand des Copyrightjahrs ermittelt.

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

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: VIRTUOZO DIGITAL PHOTOGRAMMETRY SYSTEM AND ITS THEORETICAL FOUNDATION AND KEY ALGORITHMS Prof. Jianging Zhang, Prof. Zuxun Zhang, Dr. Weiming Shen and Zhihong Wang

Document type:: Multivolume work

Structure type:: Chapter

gray-level based image matching (Zhang Z., 1992), it gives consideration to spatial relationships, and global consistency has been improved immensely. Probabilistic relaxation can be used in not only feature based image matching, but also area based image matching. An outline of the latter follows. The set of candidate points on the right image for point i on the left image and corresponding similarity measurements are J = [3A 293, jm and' p (1, 33, where" 2544, ^42, ., jm,and o (i, j) canbethe correlation coefficients of two image window centring in point i on left image and point j on right image separately. The initial probability that point j is the conjugate of point i is the normalised similarity measurement: p? pae iJ m > pli) k=0 The consistent coefficient c is defined as the similarity measurement by bridge mode: c(i,j; k,l) 7 p ( (i, k), resampling (j, 1) ) where (i, k) isthe segment of the left image (j, 1) isthe segment of the right image, resampling (j, 1) is the resampled image segment of (j, 1) relativeto (i, k) and p{} is the similarity measurement by bridge mode. The correct form of the probability in each iteration is: 4,72. GS (if kl) *P, 1) D where D is the neighbour area. Then the probability in r-th iteration is: p EPI Al vary,) where a is a constant relative to the speed of the convergence. The probabilistic relaxation of global image matching is a iterative procedure. Because the probability is relative to the matching results of neighbouring points and the Bridge Mode of image matching is used for correcting the geometric distortion, the globally consistent results acquired after the processing converges. 3.4 Neural Network Method of Image Matching The neural network is another effective method used frequently in many fields including pattern recognition. Similar to relaxation, the Hopfield model of neural networking can be used in global image matching. Suppose that there are m*n target points on the left epipolar image, and the range of x-parallax is [-p, pl. A neural network with m*n* (2p+1) neurones, corresponding to all possible candidate conjugate points, is created. The initial stage of each neuron is: if point j is candidate conjugate point if point j is not candidate conjugate point The strength of the connection between neuron (i,j) and (k,l) can be defined as the same as the consistent coefficient in probabilistic relaxation: W(i,j; k,l) = p{(i,k), resampling (j,1) } where (i,k) isthe segment of left image (j,1) is the segment 428 of right image, resampling (j,1) is the resampled image segment of (j, 1) relative to (i, k) and p{} is the similarity measurement by bridge mode. The stage of each neuron will change according to the following rule: v, (t1) 7 Lif) , 9, WGjiDv, (0) *0,,»0 kei lej v, (0-049, > WG,;k,Dv, (0) *0, «0 where 0 is the threshold of neuron (i, j). The energy function is defined as: E- „Ay vire) Wid, kD, +=) (S iru 27 kil 27167 where W represents the consistent strength between neuron (i,j) and neuron (k, 1). The more consistent they are, the smaller the first term of the formula above is. The second term of the formula means that there is only one conjugate point corresponding to grid point i on the left image, that is, only one neuron, or one of the candidate points corresponding to grid point i, is active with the value v=1 and others are not active with the values v=0. In this case, the second term is a minimum. According to the rule of minimum energy in the network, iterative calculation may change the stages of the neurones until the network tends to be stable and converge, that is: v, Kt +1) =v, (D All of the points, corresponding to v=1, are the optimal global solution. From above it can be seen, through defining the consistent coefficient as the strength of the connection, that the Hopfield network can perform relaxation processing. An advantage of this technique is that relaxation processing can be performed in real time since the Hopfield network can be implemented by conventional analog circuits. Further, not only can the correct matching result be contained, but also break-lines and occlusions can be processed, based on the new concept of the zero matching with v =0. Because of these aspects, the stability of the image match has been enhanced. 4. EFFECTIVENESS OF THE VIRTUOZO DPW Effectiveness is an important factor for a commercial DPW. Although the capability of computers will improve in the future, processing in real time needs effective algorithms. Currently, if the effectiveness of a DPW is low, it is not viable. Fast algorithms are necessary. It is quite evident from speed of VirtuoZo that this is one of our most significant achievements within digital photogrammetry. In comparison with other similar systems, the effectiveness of VirtuoZo is better by a factor five. 4.1 Fast Resampling along Epipolar Line The angle and increment in the y direction for each epipolar line is required so that the coordinates of every pixel in the epipolar line can be computed with an add operation instead of multiplication and division operations. Then, keeping the relative position in the x direction, the required pixel is acquired by linear interpolation in one dimension (the y direction) with it's two nearest pixels, instead of a bilinear interpolation in two dimension with four pixels resampling along epipolar lines by image rectification. This procedure yields a factor two improvement in International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B2. Vienna 1996 "^ " fn PS fS — Je T O tc

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