Advances in Image Processing and Understanding: A by Dmitry Goldgof, Alan C Bovik, Chang Wen Chen

By Dmitry Goldgof, Alan C Bovik, Chang Wen Chen

This quantity of unique papers has been assembled to honour the achievements of Professor Thomas S. Huang within the sector of picture processing and picture research. Professor Huang's lifetime of inquiry has spanned a few a long time, as his paintings on imaging difficulties all started in 1960's. through the years, he has made many primary and pioneering contributions to almost each quarter of this box. Professor Huang has acquired various awards, together with the distinguished Jack Kilby sign Processing Medal from IEEE. He has been elected to the nationwide Academy of Engineering, and named Fellow of IEEE, Fellow of OSA, Fellow of IAPR and Fellow of SPIE. Professor Huang has made primary contributions to photo processing, trend reputation and computing device imaginative and prescient, together with: layout and balance attempt of multidimensional electronic filters, electronic holography; compression strategies for files and pictures; 3D movement and modelling, research and visualization of the human face, hand and physique, multi-modal human-computer interfaces; and multimedia databases. This quantity is meant to focus on his contributions by means of displaying the breadth of components during which his scholars are operating. As such, contributed chapters have been written by way of a few of his many former graduate scholars (some with Professor Huang as a co-author) and illustrate not just his contributions to imaging technology but additionally his dedication to academic endeavour. The breadth of contributions is a sign of impression of Professor Huang to the sector of sign processing, photo processing, laptop imaginative and prescient and purposes; there are chapters on studying in photo retrieval, facial movement research, cloud movement monitoring, wavelet coding, powerful video transmission, and lots of different subject matters. The appendix comprises numerous reprints of Professor Huang's so much influential papers from the 1970's to 1990's. this article is designed for photo processing researchers, together with educational college, graduate scholars and researchers, in addition to execs operating in program components.

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The remaining sections are organized as following: Section 2 introduces the PBVD model and the PBVD-based animation system. Section 3 describes a PBVD model-based tracking algorithm. Explanation-based tracking is then described in Section 4. Some experimental results are demonstrated in Section 5, followed by discussions and concluding remarks in Section 6. Figure 2, A facial mesh model derived from the Cyber Ware scanner data. Left: the mesh model. Right: the texture-mapped model. 1 The PBVD model PBVD -formulation and properties A 3D Bezier volume [10] is defined as n m I x(U, v, w)=x E £ h • w ,'=0 j=(H-=0 where x(«,v,w) is a point inside the volume, which, in our case, is a facial mesh point.

Visual Communication and Image Processing, Marcel Dekker Publisher, New York, NY, 1999. J. Weng and S. Chen. Vision-guided navigation using SHOSLIF. Neural Networks, 11:1511-1529, 1998. J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, E. Thelen, "Autonomous Mental Development by Robots and Animals," Science, 291:599-600, 2001. com THOMAS S. edu Capturing real facial motions from videos enables automatic creation of dynamic models for facial animation. In this paper, we propose an explanation-based facial motion tracking algorithm based on a piecewise B6zier volume deformation model (PBVD).

5m Fig. 8 The navigation path. The blue line is the desired navigation path and the tan lines are the navigation bounds. During the test, the SAIL robot navigated within the boundaries. heading direction as o u t p u t . It rejects samples (not used for learning) if t h e input images are too similar t o samples already learned. We tested the performance by letting t h e robot go t h r o u g h the corridor 10 times. All t h e tests were successful. T h e closest distance between the SAIL robot a n d t h e wall is a b o u t 40 cm among t h e 10 tests.

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