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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...
Appropriate attacker models are generally known to be a fundamental prerequisite for any security evaluation of complex systems or networks. This paper deals with worst-case attacker models targeted to cause maximum damage in an overlay network by deliberately disturbing links within the underlying transport network topology. The flexibility of rerouting in underlay and overlay networks leads to complex...
In this paper, we propose a video summarization system for volleyball videos. Our system automatically detects rally scenes as self-consumable video segments and evaluates rally-rank for each rally scene to decide priority. In the priority decision, features representing the contents of the game are necessary; however such features have not been considered in most previous methods. Although several...
With the arrival of big data era, data mining techniques have been widely used to build models for cyber security applications such as spam filtering, malware or virus detection, and intrusion detection. This project proposes a novel approach that uses randomness to improve robustness of data mining models used in cyber security applications against attacks that try to evade detection by adapting...
Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS) and corresponding components such as Automated Vehicular Surveillance (AVS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multiclass classification...
Impaired cerebral autoregulation is often the cause of cerebral hemorrhage in preterm infants. The place of cerebral bleeding is most frequently the germinal matrix being a highly vascularized layer of neuronal and glial precursors. A passive, linear, dependency of the cerebral blood flow on the arterial mean pressure leads to damage of fragile blood vessels of the germinal matrix. A mathematical...
Considering uncertainty in continuous production processes is key to compute short-time optimal schedules which can be trusted in practice. This paper proposes a two-step stochastic approach to the robust scheduling of several evaporation plants. This approach considers the possibility of reacting in the future once the uncertainty materializes. Each evaporator has different features (capacity, equipment,...
In this paper, we present a complete change detection system named multimode background subtraction. The universal nature of system allows it to robustly handle multitude of challenges associated with video change detection, such as illumination changes, dynamic background, camera jitter, and moving camera. The system comprises multiple innovative mechanisms in background modeling, model update, pixel...
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