By Horst Bunke, Abraham Kandel, Mark Last
A sharp bring up within the computing strength of recent desktops has brought on the improvement of strong algorithms that could learn complicated styles in quite a lot of info inside of a short while interval. for that reason, it has develop into attainable to use trend reputation suggestions to new initiatives. the most aim of this booklet is to hide many of the most modern program domain names of development acceptance whereas featuring novel thoughts which were constructed or personalized in these domains.
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Additional info for Applied Pattern Recognition (Studies in Computational Intelligence)
Bowyer, D. Eggert, A. Fitzgibbon, and R. Fisher. An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7):673–689, 1996 21. -W. -W. Lee. Reconstruction of partially damaged face images based on a morphable face model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(3):365–372, 2003 22. X. Jiang, M. Binkert, B. Achermann, and H. Bunke. Towards detection of glasses in facial images. Pattern Analysis and Applications, 3(1):9–18, 2000 23.
One class of methods are variants of the morphbased approaches [6, 49]. They can only generate expressions between two given images of the same person and their ability of generating arbitrary expressions is thus more than limited. If merely one image of a person is available, these approaches are not applicable at all. Another popular class of techniques is known as expression mapping (performance-driven animation) [32, 43]. Its principle is quite simple: Given an image A of a person’s neutral face and another image A of the same person’s face with a desired expression, the movement of facial features from A to A is geometrically performed on a second person’s neutral image B to produce its facial image B with the expression.
Z. Pan, G. Healey, M. Prasad, and B. Tromberg. Face recognition in hyperspectral images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 2003 26 N. Bourbakis and P. Kakumanu 52. A. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition. In Proceedings of IEEE International Conference CVPR, pages 84–91, 1994 53. J. Phillips, P. J. M. Blackburn, E. Tabassi, M. Bone. Face recognition vendor test: Evaluation report, 2003 54. L. Phung, A.