Using multi-frame change detection methods, we estimate which pixels include objects that are in motion relative to the background. We utilize both a sequential statistical change detection method and a sparsity-based...
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ISBN:
(纸本)9780819495310
Using multi-frame change detection methods, we estimate which pixels include objects that are in motion relative to the background. We utilize both a sequential statistical change detection method and a sparsity-based change detection method. We perform foreground estimation in videos in which the background is static as well as in images in which apparent background motion is induced by camera motion. We show the results of our techniques on the background subtraction data set from the Statistical Visual Computing Lab at the University of California, San Diego(UCSD).
Among double patterning techniques, Self-aligned double patterning (SADP) has the advantage of good mask overlay control, which has made SADP a popular double patterning method for sub-32nm technology nodes. However, ...
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ISBN:
(纸本)9780819494665
Among double patterning techniques, Self-aligned double patterning (SADP) has the advantage of good mask overlay control, which has made SADP a popular double patterning method for sub-32nm technology nodes. However, SADP process places several limitations on design flexibility. This work exploits an alternative post routing approach that has the flexibility to resolve lithography violations without the overhead of repeated rule checking. In addition, it allows for successive refinement in the definition of lithographic violations as the process node matures, and implementation of fixes as localized ECO (Engineering Change Order) operations without needing to reroute the complete design.
Bright Field (BF) electron tomography (ET) has been widely used in the life sciences to characterize biological specimens in 3D. While BF-ET is the dominant modality in the life sciences it has been generally avoided ...
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ISBN:
(纸本)9780819494306
Bright Field (BF) electron tomography (ET) has been widely used in the life sciences to characterize biological specimens in 3D. While BF-ET is the dominant modality in the life sciences it has been generally avoided in the physical sciences due to anomalous measurements in the data due to a phenomenon called "Bragg scatter" - visible when crystalline samples are imaged. These measurements cause undesirable artifacts in the reconstruction when the typical algorithms such as Filtered Back Projection (FBP) and Simultaneous Iterative Reconstruction Technique (SIRT) are applied to the data. Model based iterative reconstruction (MBIR) provides a powerful framework for tomographic reconstruction that incorporates a model for data acquisition, noise in the measurement and a model for the object to obtain reconstructions that are qualitatively superior and quantitatively accurate. In this paper we present a novel MBIR algorithm for BF-ET which accounts for the presence of anomalous measurements from Bragg scatter in the data during the iterative reconstruction. Our method accounts for the anomalies by formulating the reconstruction as minimizing a cost function which rejects measurements that deviate significantly from the typical Beer's law model widely assumed for BF-ET. Results on simulated as well as real data show that our method can dramatically improve the reconstructions compared to FBP and MBIR without anomaly rejection, suppressing the artifacts due to the Bragg anomalies.
In this paper, we propose new sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorous...
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ISBN:
(纸本)9780819495020
In this paper, we propose new sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. Moreover, our method guarantees that the maximum amount of observational time is bounded. In contrast to the previous most effective method, Threshold Random Walk Algorithm, which is explicit and analytical in nature, our proposed algorithm involve parameters to be determined by numerical methods. We have introduced computational techniques such as iterative minimax optimization for quick determination of the parameters of the new detection algorithm. A framework of multi-valued decision for detecting portscanners and DoS attacks is also proposed.
We describe how one can obtain the phase space differential equation for joint position-wavenumber distributions for pulse propagation with dispersion and attenuation. We show that there are many advantages to the pha...
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ISBN:
(纸本)9780819495358
We describe how one can obtain the phase space differential equation for joint position-wavenumber distributions for pulse propagation with dispersion and attenuation. We show that there are many advantages to the phase space equation both from the point of view of insight and practical calculation. We use the method to obtain new approximations for pulse propagation. The phase space distributions we use are the Wigner distribution and spectrogram.
In Multi-Baseline SAR tomography it is necessary to process the acquired data by advanced signal processing techniques in order to adequately compensate the bad consequences of an under-sampled configuration. These te...
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ISBN:
(纸本)9780819497604
In Multi-Baseline SAR tomography it is necessary to process the acquired data by advanced signal processing techniques in order to adequately compensate the bad consequences of an under-sampled configuration. These techniques have to properly work on an environment characterized to have point targets, distributed targets and both of theme. This paper considers the Convex Optimization (CVX) tomographic solution in order to process multi-baseline data-sets collected in a Fourier under-sampled configuration in the above mentioned environment. The CVX and the Second Order Cone Programming Solution (SOCPs) have been tested by a generic log-barrier algorithm, through a successfully computational bottleneck Newton calculation. These techniques are validated on point targets, distributed targets and realistic forested environments.
We study the problem of automatic delineation of an anatomic object in an image, where the object is solely identified by its anatomic prior. We form such priors in the form of fuzzy models to facilitate the segmentat...
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ISBN:
(纸本)9780819494436
We study the problem of automatic delineation of an anatomic object in an image, where the object is solely identified by its anatomic prior. We form such priors in the form of fuzzy models to facilitate the segmentation of images acquired via different imaging modalities (like CT, MRI, or PET), in which the recorded image properties are usually different. Our main interest is in delineating different body organs in medical images for automatic anatomy recognition (AAR). The AAR system we are developing consists of three main components: (Cl) building body-wide groupwise fuzzy anatomic models;(C2) recognizing the body organs geographically and then delineating them by employing the models;(C3) generating quantitative descriptions. This paper focuses on (C2) and presents a unified approach for model-based segmentation within which several different strategies can be formulated, ranging from model-based hard/fuzzy thresholding to model-based graph cut, fuzzy connectedness, and random walker methods and algorithms. This is an important theoretical advance. The presented experiments clearly prove, that a fully automatic segmentation system based on the fuzzy models can indeed provide the reliable segmentations. However, the presented experiments utilize only the simplest versions of the methodology presented in the theoretical part of the paper. The full experimental evaluation of the methodology is still a work in progress.
We are interested in establishing the correspondence between neuron activity and body curvature during various movements of C. Elegans worms. Given long sequences of images, specifically recorded to glow when the neur...
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ISBN:
(纸本)9780819494436
We are interested in establishing the correspondence between neuron activity and body curvature during various movements of C. Elegans worms. Given long sequences of images, specifically recorded to glow when the neuron is active, it is required to track all identifiable neurons in each frame. The characteristics of the neuron data, e.g., the uninformative nature of neuron appearance and the sequential ordering of neurons, renders standard single and multi-object tracking methods either ineffective or unnecessary for our task. In this paper, we propose a multi-target tracking algorithm that correctly assigns each neuron to one of several candidate locations in the next frame preserving shape constraint. The results demonstrate how the proposed method can robustly track more neurons than several existing methods in long sequences of images.
Broadband tunable external cavity quantum cascade lasers (EC-QCL) have emerged as attractive light sources for mid-infrared (MIR) "finger print" molecular spectroscopy for detection and identification of che...
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ISBN:
(纸本)9780819494009
Broadband tunable external cavity quantum cascade lasers (EC-QCL) have emerged as attractive light sources for mid-infrared (MIR) "finger print" molecular spectroscopy for detection and identification of chemical compounds. Here we report on the use of EC-QCL for the spectroscopic detection of hazardous substances, using stand-off detection of explosives and sensing of hazardous substances in water as two prototypical examples. Our standoff-system allows the contactless identification of solid residues of various common explosives over distances of several meters. Furthermore, results on an EC-QCL-based setup for MIR absorption spectroscopy on liquids are presented, featuring a by a factor of ten larger single-pass optical path length of 100 mu m as compared to conventional Fourier transform infrared spectroscopy instrumentations.
Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. We argue that trac...
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Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. We argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by one-class support vector machine (SVM) is bounded by a closed hyper sphere, we propose a tracking method utilizing one-class SVMs that adopt histograms of oriented gradient and 2bit binary patterns as features. Thus, it is called the one-class SVM tracker (OCST). Simultaneously, an efficient initialization and online updating scheme is proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods that tackle the problem using binary classifiers on providing accurate tracking and alleviating serious drifting. (c) 2013 SPIE and IS&T
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