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Compared with the traditional front-wheel-steering (FWS) vehicles, four-wheel-independent-steering (4WIS) vehicles have better handling stability and path-tracking performance. In this paper, a novel 4WIS electric vehicle (EV) is proposed and it is viewed as a controlled object for path tracking. The nonlinear dynamical model of the 4WIS EV is built based on the nonlinear Dugoff tire model. For controller...
A secrecy transmission method with robust power control is investigated in this paper for a downlink two-tier femtocell network, where an eavesdropper attempts to wiretap the legitimate macrocell users. Considering the imperfect channel gains, a probability constraint robust optimization problem is formulated to satisfy the quality-of-service (QoS) of users. We aim to maximize the secrecy rate with...
We study the question of reconstructing a sequence of {fi, gi}i=1s from the sum of their convolution, i.e., y = ∑i=1s fi * gi. This problem is closely related to both blind deconvolution and blind demixing problem. Our goal is to find all {fi, gi}i=1s by jointly demixing each component fi * gi and performing deconvolution procedure. While the convex program is able to solve this problem effectively...
In this letter we construct frames consisting of 5d − 4 vectors that enable a both uniform and tractable recovery for the phase retrieval problem in ℂd.
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces...
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and...
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable,...
Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency. Recently, several hashing based approaches have been developed to make re-identification more scalable for large-scale gallery sets. Despite their efficiency, these works ignore cross-camera variations, which severely deteriorate the final matching accuracy. To address the above...
Principal Component Analysis (PCA) is a fundamental method for estimating a linear subspace approximation to high-dimensional data. Many algorithms exist in literature to achieve a statistically robust version of PCA called RPCA. In this paper, we present a geometric framework for computing the principal linear subspaces in both situations that amounts to computing the intrinsic average on the space...
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible...
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments...
Highly effective optimization frameworks have been developed for traditional multiview stereo relying on lambertian photoconsistency. However, they do not account for complex material properties. On the other hand, recent works have explored PDE invariants for shape recovery with complex BRDFs, but they have not been incorporated into robust numerical optimization frameworks. We present a variational...
Discovering the common (joint) and individual subspaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. The most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the...
Fine-grained activity understanding in videos has attracted considerable recent attention with a shift from action classification to detailed actor and action understanding that provides compelling results for perceptual needs of cutting-edge autonomous systems. However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely...
Recently, zero-shot action recognition (ZSAR) has emerged with the explosive growth of action categories. In this paper, we explore ZSAR from a novel perspective by adopting the Error-Correcting Output Codes (dubbed ZSECOC). Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem. In particular, we learn discriminative ZSECOC for seen...
Truncated convex models (TCM) are a special case of pair-wise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture useful image statistics. We propose a natural generalization of TCM to high-order random fields, which we call truncated max-of-convex models (TMCM). The energy function of TMCM consists...
Compressive Sensing is practical and implemented into many areas. For these applications the conventional sensing noise (e.g. AWGN) with low energy on measurements could be reduced by the robustness of Compressive Sensing. Parallel, there exist some errors, which would strongly noise or remove some parts of measurements. In this case, the recovered information would contain much noise, since the robustness...
Network systems, such as transportation systems and water supply systems, play important roles in our daily life and industrial production. However, a variety of disruptive events occur during their life time, causing a series of serious losses. Due to the inevitability of disruption, we should not only focus on improving the reliability or the resistance of the system, but also pay attention to the...
This paper establishes a robust optimization model and proposes constraint generation algorithm to solve a robust single machine scheduling problem with random release times. The performance criterion of interest is the maximum waiting time (MWT) over all jobs. Unlike the traditional stochastic programming model which requires exact distributions, our robust optimization model needs only the information...
A novel photomosaic art with three-layer information is proposed in this paper. In addition to the over-arching image can be seen from a distance and a matrix of individual images when looked closely, a QR code can be accessed by taking a picture of the whole photomosaic using public QR code scanners. In the proposed scheme, a tile image classification procedure is carefully designed to dispatch appropriate...
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