576 research outputs found

    Human motion retrieval based on freehand sketch

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    In this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected-postures. Furthermore, a novel index structure based on k-d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture-by-posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre-computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 John Wiley & Sons, Ltd

    Face Recognition Using Self-Organizing Maps

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    GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

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    In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

    In-Out Intermittency in Gap Junction-Coupled Class I^* Neurons

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    In a series of papers, we have proposed a dynamical model for gap junction-coupled networks of class I^* neurons, and investigated its dynamic characters. We found various dynamic states in a model neural network with diffusively coupled class I¤ neuron models, called μ-models. Among others, hierarchies of intermittent transitions attracted attention in relation with real brain dynamics. This paper is devoted to report a mechanism of the first transition appeared in the intermittenly transitory dynamics among an all-synchronized state, various metachronal waves and a weakly chaotic state. We clarify that this intermittent transition is described as an in-out intermittency
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