We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function. Complex 3D shapes often can be summarized using a coarsely defined structure which is consistent and robust across variety of observations. However, existing synthesis techniques do not account for structure during training, and thus often generate implausible and structurally unrealistic shapes. During training, we enforce structural constraints in order to enforce consistency and structure across the entire manifold. We propose a novel methodology for training 3D generative models that incorporates structural information into an end-to-end training pipeline.

3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology. We address this problem by proposing a novel holistic human body shape descriptor called BodyPrint. To compute the bodyprint for a given body scan, we fit a deformable human body mesh and project the mesh parameters to a low-dimensional subspace which improves discriminability across different persons. Experiments are carried out on three real-world human body datasets to demonstrate that BodyPrint is robust to pose variation as well as missing information and sensor noise. It improves the matching accuracy significantly compared to conventional 3D shape matching techniques using local features. To facilitate practical applications where the shape database may grow over time, we also extend our learning framework to handle online updates.

In this letter, we introduce a subcategory-aware object detection framework to detect generic object classes with high intra-class variance. Motivated by the observation that the object appearance demonstrates some clustering property, we split the training data into subcategories and train a detector for each subcategory. Since the proposed ensemble of detectors relies heavily on subcategory clustering, we propose an effective subcategories generation method that is tuned for the detection task. More specifically, we first initialize subcategories by constrained spectral clustering based on mid-level image features used in object recognition. Then we jointly learn the ensemble detectors and the latent subcategories in an alternative manner. Our performance on the PASCAL VOC 2007 detection challenges and INRIA Person dataset is comparable with state-of-the-art, even with much less computational cost.

L1-Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum separately without taking into account the geometric structure of the data. Motivated by L1-Graph and manifold leaning, we propose Laplacian Regularized L1-Graph (LRℓ1-Graph) for data clustering. The sparse representations of LRℓ1-Graph are regularized by the geometric information of the data so that they vary smoothly along the geodesics of the data manifold by the graph Laplacian according to the manifold assumption. Moreover, we propose an iterative regularization scheme, where the sparse representation obtained from the previous iteration is used to build the graph Laplacian for the current iteration of regularization. The experimental results on real data sets demonstrate the superiority of our algorithm compared to L1-Graph and other competing clustering methods.

In this paper, we propose a novel saliency-based algorithm to detect foreground regions in highly dynamic scenes. We first convert input video frames to multiple patch-based feature maps. Then, we apply temporal saliency analysis to the pixels of each feature map. For each temporal set of co-located pixels, the feature distance of a point from its kthnearest neighbor is used to compute the temporal saliency. By computing and combining temporal saliency maps of different features, we obtain foreground likelihood maps. A simple segmentation method based on adaptive thresholding is applied to detect the foreground objects. We test our algorithm on images sequences of dynamic scenes, including public datasets and a new challenging wildlife dataset we constructed. The experimental results demonstrate the proposed algorithm achieves state-of-the-art results.

An effective and efficient image contour detector is highly desired due to its wide applications in computer vision and multimedia retrieval. However, the state-of-the-art image contour detection algorithms are very computationally intensive, and thus impractical for web-scale applications. In this work, we study the relationship between edge detection and contour detection, based on which an edge-based image contour detection algorithm is proposed. This algorithm fully makes use of cheap edge information for efficiency purpose. The experiments on benchmark data sets show that, the proposed contour detector works much faster than existing state-of-the-art algorithms while maintaining high accuracy, and thus suitable for large-scale applications.

A novel problem of object recognition with dynamically allocated sensing resources is considered in this paper. We call this problem opportunistic sensing since prior knowledge about the correlation between class label and signal distribution is exploited as early as in data acquisition. Two forms of sensing parameters — discrete sensor index and continuous linear measurement vector — are optimized within the same maximum negative entropy framework. The computationally intractable expected entropy is approximated using unscented transform for Gaussian models, and we solve the problem using a gradient-based method. Our formulation is theoretically shown to be closely related to the maximum mutual information criterion for sensor selection and linear feature extraction techniques such as PCA, LDA, and CCA. The proposed approach is validated on multi-view vehicle classification and face recognition datasets, and remarkable improvement over baseline methods is demonstrated in the experiments.

Abstract In this paper, we develop new bounds for the first order Marcum Q‐function, which are extremely tight and tighter than any of the existing bounds to the best of our knowledge. The key idea of our approach is to derive refined approximations for the 0th order modified Bessel function in the integration region of the Marcum Q‐function. The new bounds are very tight and can serve as an effective means in bit error rate (BER) performance analysis for non‐coherent demodulation in digital communication. Copyright © 2010 John Wiley & Sons, Ltd.

In this work, we propose a novel supervised matrix factorization method used directly as a multi-class classifier. The coefficient matrix of the factorization is enforced to be sparse by ℓ1-norm regularization. The basis matrix is composed of atom dictionaries from different classes, which are trained in a jointly supervised manner by penalizing inhomogeneous representations given the labeled data samples. The learned basis matrix models the data of interest as a union of discriminative linear subspaces by sparse projection. The proposed model is based on the observation that many high-dimensional natural signals lie in a much lower dimensional subspaces or union of subspaces. Experiments conducted on several datasets show the effectiveness of such a representation model for classification, which also suggests that a tight reconstructive representation model could be very useful for discriminant analysis.