Perceptually and probabilistically guided multimedia

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Over the next five years I will develop biologically and probabilistically motivated multimedia systems that include the following novel advancements: (i) Probabilistic and perceptual factors in 3D medical image analysis, extending techniques, such as random walks and Gaussian shape modeling, to better represent specific medical conditions related to infant brain, spinal cord etc., and thereby improve segmentation techniques. (ii) Incorporate the perception of silhouettes in the process of 3D point cloud simplification. (iii) Extend wireless 3D transmission strategies developed for mesh data structures to work with point-cloud representations. (iv) Calibration and synchronization of multi-camera environments using simple, widely available objects (such as a globe) and combining sensor data to create a dynamic integrated 3D scene. (v) Designing optimized peripheral vision stereo displays. (vi) Perceptually guided Motion Capture (MoCap) data compression, with the goal of making real-time decompression feasible even on hand-held cellular devices. The significance of my work will lie in: (a) Robust 3D medical image segmentation and ground truth generation algorithms. (b) Reducing bandwidth, memory requirements and optimizing perceptual quality in 3D multimedia transmission and visualization; with applications in panoramic 3D TVs. (c) Robust 3D transmission strategies under packet loss; resulting in improved 3D edutainment over wireless devices. (d) Robust calibration and intelligent coordination of a network of cameras, allowing better 3D free-viewpoint video capture. (e) Developing better interactive 3D environments in Edutainment.