Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and...
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In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new...
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ISBN:
(纸本)9789898565419
In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.
In many multi-camera surveillance systems, there is a need to identify whether a captured person have emerged before over the network of cameras. This is the person re-identification problem. In this paper, we propose...
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In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
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Recently, constructing a good graph to represent data structures is widely used in machine learning based applications. Some existing trackers have adopted graph construction based classifiers for tracking. However, t...
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Recently, constructing a good graph to represent data structures is widely used in machine learning based applications. Some existing trackers have adopted graph construction based classifiers for tracking. However, their graph structures are not effective to characterize the inter-class separability and multi-model sample distribution, both of which are very important to successful tracking. In this paper, we propose to use a new graph structure to improve tracking performance without the assistance of learning object subspace generatively as previous work did. Meanwhile, considering the test samples deviate from the distribution of the training samples in tracking applications, we formulate the discriminative learning process, to avoid over fitting, in a semi-supervised fashion as L1-graph based regularizer. In addition, a non-linear variant is extended to adapt to multi-modal sample distribution. Experimental results demonstrate the superior properties of the proposed tracker.
It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracki...
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pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequenc...
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Reference-based image classification approach introduces a reference-set for both image representation and dictionary learning. It significantly reduces the dimensionality of represented images and shows outstanding p...
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ISBN:
(纸本)9781479923427
Reference-based image classification approach introduces a reference-set for both image representation and dictionary learning. It significantly reduces the dimensionality of represented images and shows outstanding performance even with randomly selected reference images and simple distance measure. In this paper, we improve upon existing work with two major contributions. First, we show that a more representative reference-set contributes to better classification accuracy. To this end, we carefully adapt the K-means clustering algorithm in the feature space to select a distinguished reference-set. Second, in the image classification process, we propose to represent each image by measuring its betweenness centrality in a social network composed of the representative reference-set in each class, leading to a more coherent distance measure that considers the overall connectivity between the probe image and the reference-set. Extensive experiment results demonstrate that our proposed scheme achieves better performance than existing methods.
In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segme...
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