By Patrick A. Mikulastik, Hellward Broszio, Thorsten Thormählen, Onay Urfalioglu (auth.), Long-Wen Chang, Wen-Nung Lie (eds.)
The convention of the first IEEE Paci?c-Rim Symposium on photo and Video know-how (PSIVT 2006) used to be held at Hsinchu, Taiwan, Republic of China, on December 11–13, 2006. This quantity comprises papers chosen for presentation at this convention. the purpose of this convention was once to assemble theoretical advances and useful implementations contributing to, or being excited by, photo and video know-how. PSIVT 2006 featured a complete software together with tutorials, keynote and invited talks, oral paper displays, and posters. We obtained 450 s- missions from 22 international locations and authorized 141 papers between these (i.e., de?ning an popularity price of 31.3%). The purpose used to be to set up PSIVT as a t- caliber sequence of symposia. judgements have been di?cult occasionally, yet we are hoping that the ?nal result's applicable to all concerned. along with keynotes and invited talks, PSIVT 2006 o?ered seventy six oral displays and58posters,accordingtotheproperregistrationofthesepapersbythede?ned closing date. We deeply get pleasure from the aid of the reviewers, who generously spent their time to make sure a solid reviewing procedure. invaluable reviews have been supplied by means of reviewers, frequently fairly certain, they usually definitely o?ered authors possibilities to enhance their paintings not just for this convention, but in addition for destiny research.
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Additional info for Advances in Image and Video Technology: First Pacific Rim Symposium, PSIVT 2006, Hsinchu, Taiwan, December 10-13, 2006. Proceedings
In order to distinguish the straight lines from the curvilinear ones, we develop a scheme to identify the different line types. The basic mechansim is to determine the most probable radius of a segment. First, we perform the line tracking to split all the edges into several line segments. A sample of the extracted edge pixels are shown in Fig. 4a. Fig. 4b demonstrates a sample result of the split line segments. Afterwards, we merge the adjacent line segments by the criterion of length and angle.
B. K. P. Horn. Recovering baseline and orientation from essential matrix. http://www. pdf, 1990. 5. T. Huang. Motion and structure from feature correspondences: a review. Proc. IEEE, 82:252–268, 1994. 6. R. Klette, K. Schl¨ uns, and A. Koschan. Computer Vision – Three-dimensional Data from Images. Springer, Singapore, 1998. 7. C. L. Lawson and R. J. Hanson. Solving Least Squares Problems. Prentice Hall, Englewood, 1974. 8. H. Longuet-Higgins. A computer algorithm for reconstructing a scene from two projections.
3 Optimization with Prior Knowledge Since computations of fundamental and projection matrix are sensitive to noise, it is necessary to apply a method for reducing the eﬀect of noise (to stabilize SfM). We utilize information about the given 3D scene, such as knowledge about collinearity or coplanarity. 1 G. Liu, R. Klette, and B. Rosenhahn Knowledge About Collinearity It is not hard to detect collinear points on man-made objects, such as buildings or furniture. , no lens distortion or noise), then collinear points in object space are mapped onto one line in the image plane.
Advances in Image and Video Technology: First Pacific Rim Symposium, PSIVT 2006, Hsinchu, Taiwan, December 10-13, 2006. Proceedings by Patrick A. Mikulastik, Hellward Broszio, Thorsten Thormählen, Onay Urfalioglu (auth.), Long-Wen Chang, Wen-Nung Lie (eds.)