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Maximum consensus estimation plays a critically important role in computer vision. Currently, the most prevalent approach draws from the class of non-deterministic hypothesize-and-verify algorithms, which are cheap but do not guarantee solution quality. On the other extreme, there are global algorithms which are exhaustive search in nature and can be costly for practical-sized inputs. This paper aims...
Time-of-flight (TOF) depth cameras provide robust depth inference at low power requirements in a wide variety of consumer and industrial applications. These cameras reconstruct a single depth frame from a given set of infrared (IR) frames captured over a very short exposure period. Operating in this mode the camera essentially forgets all information previously captured - and performs depth inference...
Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high computational cost. In this paper, a novel region-based level set method based on Bregman divergence and local binary fitting, hereafter referred to as Bregman-LBF,...
Subspace learning has been widely used in signal processing, machine learning, computer vision and so on. Matrix rank minimizing is a fundamental model. Nuclear norm is a convex relaxation for rank minimizing. In this paper, we propose a polynomial function to smoothen the nuclear norm. Lagrange multipliers method is employed to solve the problem. The optimal solution is obtained by iterative procedure...
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...
We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth data and consequently formulate a new joint contour and ICP tracking energy. We present...
This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces robustness to cast-shadows and specularities by resorting to redescending M-estimators...
Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold...
We propose to test model-based polarimetric decomposition techniques for robustness to variations in the underlying in-scene scattering mechanisms. The accuracy and robustness of the decomposition results are determined from simulated data sets. The simulation input parameters, e.g. volume scattering model, polarimetric signal-to-noise, etc., are known and thus provide the “ground truth” for comparison...
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting...
In this paper, a new routing strategy named as Distributed Multiple Path (DMP) routing strategy is proposed. To yield a high-efficiency transmission performance, the proposed DMP routing strategy utilizes the information of static network topology, as well as that of dynamic network transmission status. Specifically, for each of the routing Origin-Destination (O-D) node pairs, the DMP routing strategy...
This paper investigates the missile attitude control problem. The model used in this paper is with respect to the Euler attitude angles and their derivatives which can be measured and computed easily. A robust practical finite-time control algorithm is proposed based on the terminal sliding mode approach. It is proved that all of the signals in the closed-loop system are bounded and the tracking error...
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...
Low rank matrix approximation, in the presence of missing data and outliers, has previously shown its significance as a theoretic foundation in a wide spectrum of tabulated information processing applications. To fit low rank models, minimizing the nuclear norm of matrices is a popular scheme, the computational load of which, however, is heavy. While bilinear factorization can largely mitigate the...
This paper proposes a robust adaptive Backstepping controller for the quadrotor attitude dynamics. The attitude dynamic model is obtained to translate into a MIMO nonlinear system with generalized uncertainty. To overcome complex disturbances from various uncertainties, we design a strict feedback controller for the system. It's used to counteract the influence of the uncertainties by robust adaptive...
Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of...
The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation...
Reconstructing a 3D model of an unknown object via incremental registration of multiple appearance models is a challenging task. With availability of low cost sensors and robust algorithms, the field of visual scene reconstruction has advanced considerably. While these advances has enabled robust reconstructions of cluttered and unstructured scenes, an active 3D reconstruction of a generic handheld...
In this paper, we will study the theoretical foundations for operationalizing an agent’s knowledge of agency – that is an agent’s knowledge of its own actions and their effects in a dynamic environment. Our main concern will be to develop theoretical foundations and algorithms which will enable a grounded knowledge of agency; these will be empirically evaluated in future work.Our approach will employ...
We present a novel global registration method for deformable objects captured using a single RGB-D camera. Our algorithm allows objects to undergo large non-rigid deformations, and achieves high quality results without constraining the actor's pose or camera motion. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure...
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