By Katsushi Ikeuchi, Daisuke Miyazaki
A huge variety of cultural historical past items world wide are deteriorating or being destroyed a result of paintings of traditional failures, akin to earthquakes and floods, and human-inflicted destruction, equivalent to warfare and vandalism. within the wake of those threats, 3D facts turns into a severe part of completely recording the shapes of those very important items in order that they can be handed right down to destiny generations.
Digitally Archiving Cultural Objects describes thorough study and strategies for protecting cultural background items by utilizing 3D electronic facts. those tools have been constructed via utilizing machine imaginative and prescient and special effects applied sciences. this knowledge can be used for simulation and recovery reasons in addition to in the direction of quite a few multimedia functions.
This accomplished publication is equipped into 4 components. half 1 describes quite a few sensors designed to acquire information. half 2 incorporates a selection of papers that describe the geometric pipeline, changing received facts right into a constant geometric version, via opting for relative kinfolk between electronic facts and connecting these facts right into a uniform illustration. half three issues photometric concerns, together with find out how to map colour photos on a geometrical version and the way to take away the impression of sun within the photos got. half four stories at the attempt to set up a electronic museum to revive and reveal the unique visual appeal of history items in addition to behavior analyses of bought info for history research.
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Additional info for Digitally Archiving Cultural Objects
The most time-consuming step in alignment is to find pairs of data points. Oishi and Ikeuchi utilize a graphics processing unit, commonly available on recent PCs, for establishing pairs of data points. As a result, they achieve ten times the rapidity of the previous Nishino and Ikeuchi algorithm. Oishi in Chapter 7 also extends the algorithm into a parallel implementation so as to be able to align a very large data set. Another issue in handling a large object is the huge number of data sets. The simultaneous algorithm, as originally designed, requires all range images to be read into memory; even when the computation is distributed over a PC cluster, the amount of memory used on each PC is not reduced.
Usually a set of range data that covers an entire object’s surface consists of multiple data sets obtained with respect to these relative coordinate systems. It is necessary to determine relative relations among these sensor coordinate systems. Some hardware, such as GPS, is available to determine the 4 DIGITALLY ARCHIVING CULTURAL OBJECTS sensor coordinate system, but such currently available hardware is not accurate enough. Alignment algorithms determine these relations by comparing data similarity among possible overlapping areas.
In Proc. 5th International Joint Conference on Artificial Intelligence, page 584, 1977.  T. Morita and T. Kanade. A sequential factorization method for recovering shape and motion from image streams. IEEE Trans. on PAMI, 19(8):858–867, 1997.  P. Neugebauer. Geometrical cloning of 3D objects via simultaneous registration of multiple range images. In Proc. of the International Conference on Shape Modeling and Application, pages 130–139, 1997.  C. Poelmann and T. Kanade. A paraperspective factorization method for shape and motion recovery.