Derong Chen

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A new joint view-identity manifold (JVIM) is proposed for multi-view shape modeling that is applied to automated target tracking and recognition (ATR). This work improves our recent work where the view and identity manifolds are assumed to be independent for multi-view multi-target modeling. A local linear Gaussian process latent variable model (LL-GPLVM)(More)
We propose a new joint view-identity manifold (JVIM) for multi-view and multi-target shape modeling that is well-suited for automated target tracking and recognition (ATR) in infrared imagery. As a shape generative model, JVIM features a novel manifold structure that imposes a conditional dependency between the two shape-related factors, view and identity,(More)
We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the(More)
A new probabilistic model called ATR-Seg for automated target tracking, recognition and segmentation is proposed that incorporates a shape constrained level set with a shape generative model along with motion model. The shape model involves a view-independent identity manifold and infinite identity-dependent view manifolds for multi-view and multi-target(More)
This paper constructs a set partition coding system (SPACS) to combine the advantages of different types of set partition coding algorithms. General tree (GT) is an important conception introduced in this paper, which can represent tree set and square set simultaneously. With the help of GT, SPIHT is generalized to construct degree- k SPIHT based on the(More)
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