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 analysis of two kinds of set partition operations. Using the same coding mechanism, SPACS (k,p) is constructed, aided with virtual subbands that are generated by recursive division on the LL band. SPACS belongs to tree-set partition coding algorithms if k and p take smaller values. In particular, SPACS(2,1) is the classical SPIHT. SPACS tends toward a block-set partition coding algorithm as k,p increases. Location bit, amplitude bit, and unnecessary bit are presented, which can be used to analyze the coding efficiency of SPACS. We compress 256 images with 512×512 using SPACS. The numerical results show SPACS achieves some improvements in coding efficiency over SPIHT, especially at very low bitrate. On average, to code every image, SPACS(3,1) (at an average of 3.93 bpp) needs 7792 more location bits but saves 10 218 unnecessary bits, compared with SPIHT (3.94 bpp).

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 shape modeling. ATR-Seg applies the motion model to predict the state of the target (i.e., 3D position, pose and identity), and then uses a shape-aware level set energy functional to evaluate the tracking and segmentation results. A particle filtering-based method is used for sequential inference, where the level set energy functional is treated as the likelihood function. Experimental results obtained against the SENSIAC ATR database demonstrate the advantages of the proposed method compared with the two recent techniques that require target pre-segmentation via background subtraction.

We propose a simultaneous target recognition, segmentation and pose estimation algorithm for the infrared ATR task. A probabilistic framework of level set segmentation is extended by incorporating a shape generative model that provides a multi-class and multiview shape prior. This generative model involves a couplet of a view manifold and an identity manifold for general shape modeling. Then an energy function from the probabilistic level set formulation can be iteratively optimized by a shape-constrained variational method. Due to the fact that both the view and identity variables are explicitly involved in the level set optimization, the proposed method is able to accomplish recognition, segmentation, and pose estimation. Experimental results show that the proposed method outperforms two traditional methods where target recognition and pose estimation are implemented after segmentation.

A new joint view-identity manifold (JVIM) is proposed for multiview 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) is used to learn a probabilistic JVIM which can capture both inter-class and intra-class variability of 2D target shapes under arbitrary view point jointly in one coexisted latent space. A particle filter-based ATR algorithm is developed to simultaneously infer the view and identity parameters along JVIM so that target tracking and recognition can be achieved jointly in a seamlessly fashion. The experimental results using SENSIAC ATR database demonstrate the advantages of our method both qualitatively and quantitatively compared with existing methods using template matching or separate view and identity manifolds.