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.
12395366
Image Cues Fusion for Object Tracking Based on Particle Filter
Particle filter is a powerful algorithm to deal with non-linear and non-Gaussian tracking problems. However the algorithm relying only upon one image cue often fails in challenging scenarios. To overcome this, the paper first presents a color likelihood to capture color distribution of the object based on Bhattacharry coefficient, and a structure likelihood representing high level knowledge regarding the object. Together with the widely used edge likelihood, the paper further proposes a straightforward image cues fusion for object tracking in the framework of particle filter, under assumption that the visual measurement of each image cue is independent of each other. The experiments on real image sequences have shown that the method is effective, robust to illumination changes, pose variations and complex background.
42009299
Probabilistic Object Tracking Based on Machine Learning and Importance Sampling
The paper presents a novel particle filtering framework for visual object tracking. One of the contributions is the development of a likelihood function based on one of machine learning algorithm–AdaBoost algorithm. The likelihood function can capture the structure characteristics of one class of objects, and is thus robust to clutters and noise in the complex background. The other contribution is the adoption of mean shift iteration as a proposal distribution, which can steer discrete samples towards regions which most likely contain the targets, and is therefore leading to computational efficiency in the algorithm. The effectiveness of such a framework is demonstrated with a particular class of objects–human faces.
2872365
A frequency relaxation approach for analog/RF system-level simulation
The increasing complexity of today's mixed-signal integrated circuits necessitates both top-down and bottom-up system-level verification. Time-domain state-space modeling and simulation approaches have been successfully applied for such purposes (e.g. Simulink); however, analog circuits are often best analyzed in the frequency domain. Circuit-level analyses, such as harmonic balance, have been successfully extended to the frequency domain [2], but these algorithms are impractical for simulating large systems with wide-band input and noise signals. In this paper we proposed a frequency-domain approach for analog/RF system-level simulation that is capable of capturing various second order effects (e.g. nonlinearity, noise, etc.) for both time-invariant and time-varying systems with wide-band inputs. The simulator directly evaluates the frequency domain response at each node via a relaxation scheme that is proven to be convergent under typical circuit conditions. Our experimental results demonstrate the accuracy and efficiency of the proposed simulator under various wide-band input and noise excitations.
9166431
Practical information flow control in Web-based information systems
This paper presents a practical application of language-based information-flow control, namely, a domain-specific Web scripting language designed for interfacing with databases. The primary goal is to provide strong enforcement of confidentiality and integrity policies: confidential data can be released only in permitted ways and trustworthy data must result from expected computations or conform to expected patterns. Such security policies are specified in the database layer and statically enforced for the rest of the system in an end-to-end fashion. In contrast with existing Web-scripting languages, which provide only ad hoc mechanisms for information security, the scripting language described here uses principles based on the well-studied techniques in information-flow type systems. However, because Web scripts often need to downgrade confidential data and manipulated untrusted user input, they require practical and convenient ways of downgrading secure data. To achieve this goal, the language allows safe downgrading according to downgrading policies specified by the programmer. This novel, pattern-based approach provides a practical instance of recent work on delimited release and relaxed noninterference and extends that work by accounting for integrity policies.
1157010
Downgrading policies and relaxed noninterference
In traditional information-flow type systems, the security policy is often formalized as noninterference properties. However, noninterference alone is too strong to express security properties useful in practice. If we allow downgrading in such systems, it is challenging to formalize the security policy as an extensional property of the system.This paper presents a generalized framework of downgrading policies. Such policies can be specified in a simple and tractable language and can be statically enforced by mechanisms such as type systems. The security guarantee is then formalized as a concise extensional property using program equivalences. This relaxed noninterference generalizes traditional pure noninterference and precisely characterizes the information released due to downgrading.
1602916
Testing algorithms is like testing students
In this paper, we apply methods from educational testing to measure the reliability of an IR collection.
45203834
Efficiently tolerating failures in asynchronous real-time distributed systems
We present a proactive resource allocation algorithm, called BEA, for fault-tolerant asynchronous real-time distributed systems. BEA considers an application model where trans-node application timeliness requirements are expressed using benefit functions, and anticipated workload during future time intervals are expressed using adaptation functions. Furthermore, BEA 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 aggregate real-time benefit and the ability to tolerate host failures during the time window of adaptation functions. Since determining the optimal solution is computationally intractable, BEA heuristically computes suboptimal resource allocations in polynomial-time. We show that BEA can achieve almost the same fault-tolerance ability as full replication, and accrue most of real-time benefit that full replication can accrue. In the meanwhile, BEA requires much fewer replicas than full replication, and hence is cost effective.