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Local part-based human detectors are capable of handling partial occlusions efficiently and modeling shape articulations flexibly, while global shape template-based human detectors are capable of detecting and segmenting human shapes simultaneously. We describe a Bayesian approach to human detection and segmentation combining local part-based and global template-based schemes. The approach relies on the key ideas of matching a part-template tree to images hierarchically to generate a reliable set of detection hypotheses and optimizing it under a Bayesian MAP framework through global likelihood re-evaluation and fine occlusion analysis. In addition to detection, our approach is able to obtain human shapes and poses simultaneously. We applied the approach to human detection and segmentation in crowded scenes with and without background subtraction. Experimental results show that our approach achieves good performance on images and video sequences with severe occlusion.

The properties of graphene can vary as a function of the number of layers (NOL). Controlling the NOL in large area graphene is still challenging. In this work, we demonstrate a picosecond (ps) laser thinning removal of graphene layers from multi-layered graphene to obtain desired NOL when appropriate pulse threshold energy is adopted. The thinning process is conducted in atmosphere without any coating and it is applicable for graphene films on arbitrary substrates. This method provides many advantages such as one-step process, non-contact operation, substrate and environment-friendly, and patternable, which will enable its potential applications in the manufacturing of graphene-based electronic devices.

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Abstract. Training discriminative classifiers for a large number of classes is a challenging problem due to increased ambiguities between classes. In order to better handle the ambiguities and to improve the scalability of classifiers to larger number of categories, we learn pairwise dissimilarity profiles (functions of spatial location) between categories and adapt them into nearest neighbor classification. We introduce a dissimilarity distance measure and linearly or nonlinearly combine it with direct distances. We illustrate and demonstrate the approach mainly in the context of appearance-based person recognition.