Jyh-Charn Liu

Selective removal of impulse noise based on homogeneity level information

In this paper, we propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is used to represent the correlations between a pixel and its neighbors, and to derive the upper and lower bound of the homogeneity level. Noise detection is performed at two stages: noise candidates are first selected using the homogeneity level, and then a refining process follows to eliminate false detections. The noise detection scheme does not use a quantitative decision measure, but uses qualitative structural information, and it is not subject to burdensome computations for optimization of the threshold values. Empirical results indicate that our scheme performs significantly better than other median filters, in terms of noise suppression and detail preservation.

On the adaptive detection of blood vessels in retinal images

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.

XML Document Parsing: Operational and Performance Characteristics

Parsing is an expensive operation that can degrade XML processing performance. A survey of four representative XML parsing models-DOM, SAX, StAX, and VTD-reveals their suitability for different types of applications.

Fractal-based Automatic Localization and Segmentation of Optic Disc in Retinal Images

In this paper, we proposed a novel algorithm to detect optic disc location in retinal images. Optic disc is a bright disk area and all major blood vessels and nerves originate from it. With its high fractal dimension of blood vessel, optic disc can be easily differentiated from other bright regions such as hard exudates and artifacts. Compared with existing algorithms, ours has much lower computational cost and is more robust. With its location known, segmentation of optic disc can be done with simple local histogram analysis. The algorithm can be valuable for automated processing for early stage retinal disease.

Non-linear analysis of traffic flow

Traffic flow prediction is an important application of the ITS technology. In this paper, we applied nonlinear time-series modeling techniques to analyze a traffic data. Our objective is to investigate the deterministic properties of traffic flow using a nonlinear time series analysis technique. The experiment is performed for inductance loop data collected from the San Antonio freeway system. Our study concludes that the traffic data exhibits chaotic properties and techniques based on phase space dynamics can be used to analyze and predict the traffic flow.

Shadow attacks: automatically evading system-call-behavior based malware detection

Contemporary malware makes extensive use of different techniques such as packing, code obfuscation, polymorphism, and metamorphism, to evade signature-based detection. Traditional signature-based detection technique is hard to catch up with latest malware or unknown malware. Behavior-based detection models are being investigated as a new methodology to defeat malware. This kind of approaches typically relies on system call sequences/graphs to model a malicious specification/pattern. In this paper, we present a new class of attacks, namely “shadow attacks”, to evade current behavior-based malware detectors by partitioning one piece of malware into multiple “shadow processes”. None of the shadow processes contains a recognizable malicious behavior specification known to single-process-based malware detectors, yet those shadow processes as an ensemble can still fulfill the original malicious functionality. To demonstrate the feasibility of this attack, we have developed a compiler-level prototype tool, AutoShadow, to automatically generate shadow-process version of malware given the source code of original malware. Our preliminary result has demonstrated the effectiveness of shadow attacks in evading several behavior-based malware analysis/detection solutions in real world. With the increasing adoption of multi-core computers and multi-process programs, malware writers may exploit more such shadow attacks in the future. We hope our preliminary study can foster more discussion and research to improve current generation of behavior-based malware detectors to address this great potential threat before it becomes a security problem of the epidemic proportions.

On the efficient scheduling of non-periodic tasks in hard real-time systems

The paper presents linear time, online algorithms which guarantee and jointly schedule firm aperiodic, hard sporadic and periodic tasks in fixed priority real time systems. We develop and capitalize on a methodology which computes the spare capacity Z(a,b) exactly in time /spl Theta/(n), for arbitrary schedule intervals (a,b), which, to the best of our knowledge, is the first linear time algorithm reported in the literature. Previous state of the art methods incur pseudopolynomial time to guarantee online a single aperiodic and incur continuous overhead for slack maintenance. Our method guarantees and schedules firm tasks to receive FIFO or EDF service, incurring a one-time linear cost of /spl Theta/(n) and /spl Theta/(n+k) respectively, where k is the number of pending firm tasks.

Decision-based median filter improved by predictions

This paper presents a decision-based median filtering algorithm in which local image structures are used to estimate the original values of the noisy pixels. The decision whether a pixel is corrupted or not is based on a new decision measure which considers the differences of adjacent pixel values in the rank-ordered sequence. Once the pixels in a noisy image have been classified into uncorrupted and noise-corrupted ones, the blocks containing only the uncorrupted pixels are used to train the predictive relationship between the center pixel and its neighbors, which is represented by a function approximation f. By applying f to noise-corrupted blocks, we could generate the candidates of the original value of a noise-corrupted pixel, and estimate it using median filtering of the candidates.

Deterministic upperbounds of the worst-case execution times of cached programs

Proposes techniques to derive the worst-case execution time (WCET) of cached programs. We focus on the analysis of one single program run on a direct-mapped cache, where no external interference could occur. The analysis complexity of the WCET of (un)cached programs is NP-complete. For nested loops, we derive some sufficient conditions in deriving the deterministic bounds of their WCET. These sufficient conditions can be used to make trade-offs between tightness of the WCET bounds and their search time.<<ETX>>

A wavelet-frame based image force model for active contouring algorithms

This paper proposes a directional image force (DIF) for active contouring. DIF is the inner product of the zero crossing strength (ZCS) of wavelet frame coefficients, and the normal of a snake, by representing strength and orientation of edges at multiple resolution levels. DIF markedly improves the immunity of snakes to noise and convexity.