Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising.

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition.

In computer vision and multimedia analysis, it is common to use multiple features (or multimodal features) to represent an object. For example, to well characterize a natural scene image, we typically extract a set of visual features to represent its color, texture, and shape. However, it is challenging to integrate multimodal features optimally. Since they are usually high-order correlated, e.g., the histogram of gradient (HOG), bag of scale invariant feature transform descriptors, and wavelets are closely related because they collaboratively reflect the image texture. Nevertheless, the existing algorithms fail to capture the high-order correlation among multimodal features. To solve this problem, we present a new multimodal feature integration framework. Particularly, we first define a new measure to capture the high-order correlation among the multimodal features, which can be deemed as a direct extension of the previous binary correlation. Therefore, we construct a feature correlation hypergraph (FCH) to model the high-order relations among multimodal features. Finally, a clustering algorithm is performed on FCH to group the original multimodal features into a set of partitions. Moreover, a multiclass boosting strategy is developed to obtain a strong classifier by combining the weak classifiers learned from each partition. The experimental results on seven popular datasets show the effectiveness of our approach.

The R1947X mutation of NF1 causing autosomal dominant neurofibromatosis type 1 in a Chinese family.

Neurofibromatosis type 1 is a common autosomal dominant disorder with a high rate of penetrance. It is caused by the mutation of the tumor suppressor gene NF1, which encodes neurofibromin. The main function of neurofibromin is down-regulating the biological activity of the proto-oncoprotein Ras by acting as a Ras-specific GTPase activating protein. In this study, we identified a Chinese family affected with neurofibromatosis type 1. The known gene NF1 associated with NF1 was studied by linkage analysis and by direct sequencing of the entire coding region and exon-intron boundaries of the NF1 gene. The R1947X mutation of NF1 was identified, which was co-segregated with affected individuals in the Chinese family, but not present in unaffected family members. This is the first report, which states that the R1947X mutation of NF1 may be one of reasons for neurofibromatosis type 1 in Chinese population.