35689052
Visual contour tracking based on sequential importance sampling/resampling algorithm
The condensation algorithm can deal with non-Gaussian, nonlinear visual contour tracking in a unified way. Despite its simple implementation and generality, it has two main limitations. The first limitation is that in sampling stage the algorithm does not take advantage of the new measurements. As a result of the inefficient sampling strategy, the algorithm needs a large number of samples to represent the posterior distribution of state. The next is in the selection step, resampling may introduce the problem of sample impoverishment. To address these two problems, we present an improved visual tracker based on an importance sampling/resampling algorithm. Gaussian density of each sample is adopted as the sub-optimal importance proposal distribution, which can steer the samples towards the high likelihood by considering the latest observations. We also adopt a criterion of effective sample size to determine whether the resampling is necessary or not. Experiments with real image sequences show that the performance of new algorithm improves considerably for tracking in visual clutter.
1068580
Visual contour tracking based on particle filters
Abstract The Condensation algorithm, developed for visual tracking, is a variant of particle filter. In the sampling stage of Condensation, no use is made of the information from the current frame in the image sequence. As a consequence, the algorithm requires a large number of particles and is computationally expensive. In this paper, a Kalman particle filter (KPF) and an unscented particle filter (UPF) are applied to contour tracking to try to overcome the problem. These algorithms use the Kalman filter or unscented Kalman filter to incorporate information from the current frame. This sampling strategy can effectively steer the set of particles towards regions with high likelihood, and therefore can considerably reduce the number of particles needed for tracking. Performance comparisons show that the KPF is an improvement over Condensation, while the UPF has a much higher computational cost for equal tracking error.
18829609
Proposal of Novel Histogram Features for Face Detection
This paper presents novel features for face detection in the paradigm of AdaBoost algorithm. Features are multi-dimensional histograms computed from a set of rectangles in the filtered images, and they represent marginal distributions of these rectangles. The filter banks consist of intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and frequency properties of human faces at different scales and different orientations. The best features selected by AdaBoost, pairs of filter and rectangle, can thus be interpreted as boosted marginal distributions of human faces. The result of preliminary experiments demonstrate that the selected features are much more powerful to describe the face pattern than the simple features of Viola and Jones and some variants which can only capture several moments of ONE dimensional histogram in intensity images.
13006853
Novel likelihood estimation technique based on boosting detector
This paper presents novel likelihood estimation to be used for particle filter based object tracking. The likelihood estimation is built upon cascade object detector trained with Gentle AdaBoost (GAB), in order to capture the probability of existence of object. Two strategies are adopted to construct the likelihood functions: probability-intra-stage (PIS) corresponding to real output of each weak classifier in the same stage, and probability-outer-stage (POS) corresponding to the depth reached in the cascade detector. Five kinds of likelihood functions are thus proposed based on the trained GAB detector. Our experiment shows the likelihood functions are able to characterize probabilistically the existence of object accurately, having much higher confidence value in object regions than that in background, and that the integral strategy of PIS and POS is the best choice.
43248584
Histogram Features-Based Fisher Linear Discriminant for Face Detection
The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. By introducing a novel “integral histogram image”, we can compute histograms rapidly. Then we turn to Fisher linear discriminant for each template to project histograms from d–dimensional to one-dimensional subspace. Best features, used to describe face pattern, are selected by AdaBoost learning. The results of preliminary experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by Viola and Jones and some variants.
14905620
Unscented Kalman filter for visual curve tracking
Abstract Visual contour tracking in complex background is a difficult task. The measurement model is often nonlinear due to clutter in images. Traditional visual trackers based on Kalman filters employ simple linear measurement models, and often collapse during the tracking process. This paper presents a new contour tracker based on unscented Kalman filter that is superior to extended Kalman filter both in theory and in many practical situations. The new tracker employs a more accurate nonlinear measurement model, without computation of a Jacobian matrix. During each time step, the tracker makes multiple measurements in terms of the set of appropriately chosen sample points, thus obtaining the best observation according to the measurement density. The resulting algorithm is able to obtain a more exact estimate of the state of the system, while having the same order of complexity as that of an extend Kalman Filter. The experiments show that the new algorithm is superior to those based on Kalman filters.
16507795
DPR, LPR: proactive resource allocation algorithms for asynchronous real-time distributed systems
We present two proactive resource allocation algorithms, called DPR and LPR, for satisfying the timeliness requirements of real-time tasks in asynchronous real-time distributed systems. The algorithms are proactive in the sense that they allow application-specified and user-triggered resource allocation by allowing anticipated task workloads to be specified for future time intervals. When proactively triggered, the algorithms allocate resources to maximize the aggregate deadline-satisfied ratio for the future time interval under the anticipated workload. While DPR uses the earliest deadline first scheduling algorithm as the underlying algorithm for process scheduling and packet scheduling, LPR uses a modified least laxity first scheduling algorithm. We show that LPR is computationally more expensive than DPR. Further, our experimental studies reveal that LPR yields a higher deadline-satisfied ratio than DPR.
1488910
Modeling interconnect variability using efficient parametric model order reduction
Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this paper we present an efficient yet accurate parametric model order reduction algorithm for addressing the variability of IC interconnect performance. The efficiency of the approach lies in a novel combination of low-rank matrix approximation and multi-parameter moment matching. The complexity of the proposed parametric model order reduction is as low as that of a standard Krylov subspace method when applied to a nominal system. Under the projection-based framework, our algorithm also preserves the passivity of the resulting parametric models.
14712997
Proactive QoS negotiation in asynchronous real-time distributed systems
We present a fast, proactive, quality of service (QoS) negotiation algorithm called Best Effort Negotiation (or BEN), for asynchronous real-time distributed systems. BEN considers an application model where trans-node application timeliness and fault-tolerance requirements are expressed using benefit functions, and anticipated workload and system failure rates during future time intervals are expressed using adaptation functions and reliability functions, respectively. Furthermore, BEN considers an adaptation model where subtasks of application tasks are replicated at run-time for tolerating failures as well as for sharing workload increases. Given such models, the objective of the algorithm is to maximize the sum of aggregate real-time and fault-tolerance benefits during the time window of adaptation functions. Since determining the optimal solution is computationally intractable, BEN heuristically computer sub-optimal resource allocations in polynomial-time. To determine how well BEN performs, we describe another algorithm called HLC, that is inspired by the well-known Hill Climbing heuristic. We show that HLC is significantly slower than BEN. However, our experimental studies reveal that the performance of BEN, in general, is as good as that of HLC.
1602916
Testing algorithms is like testing students
In this paper, we apply methods from educational testing to measure the reliability of an IR collection.
605614
Noise macromodel for radio frequency integrated circuits
Noise performance is a critical analog and RF circuit design constraint, and can impact the selection of the IC system-level architecture. It is therefore imperative that some model of the noise is represented at the highest levels of abstraction during the design process. In this paper we propose a noise macromodel for analog circuits and demonstrate it by way of implementation in a system level simulator based on MATLAB. We also explain our process of macromodel extraction via reformulation of frequency-domain noise analysis results, and the corresponding steps of model order reduction. The results demonstrate the efficacy of this macromodel for frequency domain system level simulation.