We focus on improving the effectiveness of content-based 3D shape retrieval. Motivated by retrieval performance of several ndividual 3D model feature vectors, we propose a novel method, called prior knowledge based automatic weighted combination, to improve the retrieval effectiveness. The method dynamically determines the weighting scheme for different feature vectors based on the prior knowledge. The experimental results show that the proposed method provides significant improvements on retrieval effectiveness of 3D shape search with several measures on a standard 3D database. Compared with two existing combination methods, the prior knowledge weighted combination technique has gained better retrieval effectiveness.

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

Aiming at the mobile cooperation in the mobile computing environment, a novel model, CA-AMCM (Adaptive Mobile Cooperation Model based on Context-Awareness), was proposed. Firstly, we give the sets of correlation context, the principle and definition about the relation each other among contexts. Then, we describe the abstraction of context information, which is necessary to group cooperation in mobile computing. We express context with ontology theory and deduct with first-order predication. After that, CA-AMCM model was constructed based on it. At last, we apply the model to the MECP (Mobile Embedded Cooperation Platform). The practice indicates that the model provides a powerful means for advancing group cooperation intelligence.

Multi-sensor image fusion is a challenging research field, which is a issue to be further investigated and studied. Self-Generating Neural Networks (SGNNs) are self-organization neural network, whose network structures and parameters need not to be set by users, and its learning process needs no iteration. An approach of image fusion using a SGNN is proposed in this paper. The approach consists of pre-processing of the images, clustering pixels using SGNN and fusing images using fussy logic algorithms. The approach has advantages of being wieldy to be used by users and having high computing efficiency, The experimental results demonstrate that the MSE (mean square error) of this approach decreases 30%-60% than those by Laplacian pyramid and discrete wavelet transform approaches.

A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.