Jiun-Jian Liaw

An effective voting method for circle detection

The drawbacks of the standard Hough transform (SHT) are the large computation and the large storage requirement. Moreover, the voting method of SHT plays an important role to affect the pattern detection accuracy. In this paper, a new voting method is proposed to reduce the computation and the storage requirements of SHT for circle detection. This method improves the efficiency of circle detection by letting each pixel only belong to one candidate of circle parameters. Synthetic images and real images are used to show the capability of the proposed method.

Textural Defect Segmentation Using a Fourier-Domain Maximum Likelihood Estimation Method

When automatically inspecting textured surface defects, the most important step is to segment the defects from the background. For complicated textures, however, defect segmentation is still a challenging problem. In this paper, we use a Fourier-domain maximum likelihood estimator (FDMLE) based on the fractional Brownian motion (FBM) model to inspect surface defects of textile fabrics. From the experiments, we obtain good results for defect segmentation, and find the methods performance is invariant under geometric transformation.

A FAST RANDOMIZED HOUGH TRANSFORM FOR CIRCLE/CIRCULAR ARC RECOGNITION

The main drawbacks of the Hough transform (HT) are the heavy requirement of computation and storage. To improve the drawbacks of the HT, the randomized Hough transform (RHT) was proposed. But the RHT is not suitable for detecting the pattern with the complex image because the probability is too low. In this paper, we propose a fast randomized Hough transform for circle/circular arc detection. We pick one point at random to be the seed point. Then, we propose a checking rule to confirm if the seed point is on the true circle. Compared with the previous techniques, the proposed method requires less computational time and is more suitable for complex images. In the experiments, synthetic and real images are used to show the effect of the proposed method.

Atmospheric Visibility Monitoring Using Digital Image Analysis Techniques

Atmospheric visibility is a standard of human visual perception of the environment. It is also directly associated with air quality, polluted species and climate. The influence of urban atmospheric visibility affects not only human health but also traffic safety and human life quality. Visibility is traditionally defined as the maximum distance at which a selected target can be recognized. To replace the traditional measurement for atmospheric visibility, digital image processing schemes provide good visibility data, established by numerical index. The performance of these techniques is defined by the correlation between the observed visual range and the obtained index. Since performance is affected by non-uniform illumination, this paper proposes a new procedure to estimate the visibility index with a sharpening method. The experimental results show that the proposed procedure obtains a better correlation coefficient than previous schemes.

Morphological and semi-quantitative characteristics of diesel soot agglomerates emitted from commercial vehicles and a dynamometer

Diesel soot aggregates emitted from a model dynamometer and 11 on-road vehicles were segregated by a micro-orifice uniform deposit impactor (MOUDI). The elemental contents and morphological parameters of the aggregates were then examined by scanning electron microscopy coupled with an energy dispersive spectrometer (SEM-EDS), and combined with a fractional Brownian motion (fBm) processor. Two mode-size distributions of aggregates collected from diesel vehicles were confirmed. Mean mass concentration of 339 mg/m3 (dC/dlogdp) existed in the dominant mode (180-320 nm). A relatively high proportion of these aggregates appeared in PM1, accentuating the relevance regarding adverse health effects. Furthermore, the fBm processor directly parameterized the SEM images of fractal like aggregates and successfully quantified surface texture to extract Hurst coefficients (H) of the aggregates. For aggregates from vehicles equipped with a universal cylinder number, the H value was independent of engine operational conditions. A small H value existed in emitted aggregates from vehicles with a large number of cylinders. This study found that aggregate fractal dimension related to H was in the range of 1.641-1.775, which is in agreement with values reported by previous TEM-based experiments. According to EDS analysis, carbon content ranged in a high level of 30%-50% by weight for diesel soot aggregates. The presence of Na and Mg elements in these sampled aggregates indicated the likelihood that some engine enhancers composed of biofuel or surfactants were commonly used in on-road vehicles in Taiwan. In particular, the morphological H combined with carbon content detection can be useful for characterizing chain-like or cluster diesel soot aggregates in the atmosphere.

A proposed circle/circular arc detection method using the modified randomized hough transform

Abstract The randomized Hough transform (RHT) is a useful tool to locate patterns in an image. In this study, we propose a modified RHT to detect circles/circular arcs efficiently. First, we segment an image into sub‐images based on edge information, then we use the proposed circular arc analysis and density check rule to modify RHT for circle/circular arc detection. In the experiments, real images are used to show the capability of the proposed method.

Fiber Recognition of PET/Rayon Composite Yarn Cross-sections Using Voting Techniques:

In this paper, we propose a new image processing method to recognize the fiber patterns in the image of PET/Rayon composite yarn cross-section. Our method consists of two voting techniques: the connected component voting (for obtaining single fiber locations) and the circle parameter voting (circle detection, for recognizing the fiber patterns). When compared with the previous approach, the new method needs fewer parameters and is more flexible. Several real images are used to show the performance of the proposed method.

The Steady Clustering Scheme for Heterogeneous Wireless Sensor Networks

To reduce energy consumption is an important topic in WSN (wireless sensor networks). One of the popular protocols for energy-efficient WSN is called LEACH (Low-Energy Adaptive Clustering Hierarchy) which is a clustering algorithm designed for homogeneous WSN. Since LEACH selects the cluster heads based on probability, the real and expectance number of cluster heads may be different. However, LEACH is not suitable for heterogeneous WSN because the low-energy nodes can be cluster heads and die quickly. To operate under heterogeneous WSN, several methods are proposed such as SEP (Stable Election Protocol) and DEEC (Distributed Energy-Efficient Clustering). In this paper, we propose a scheme for multi-level heterogeneous WSN. The proposed method is based on the SGCH (Steady Group Clustering Hierarchy). In the proposed method, all nodes are divided into several groups by the initial energy that extends the battery life time and fits for multi-level heterogeneous WSN. Simulations are used to compare the proposed method with previous technologies. We show that the proposed method prolongs the period of stability.

A Data Hiding Method Using Secret Data Division and Pixel Value Differencing

In this paper, we propose a new data hiding method. We divide cover-image into many non-overlapping blocks. Each block consists of two contiguous pixels. We separate the secret data into two parts. These two parts of secret data are hidden into two pixels of the block. The hiding capacity is depended on the difference value of the two points. If the difference value is higher, we embed more bits of secret data into the pixels. We also apply the modulus operation to embed the separated data. All cases of pixel pairs are used to show the performance and compare with the previous scheme. The experimental results show that the proposed technique has higher quality.

Design and implementation of the push-to-talk service in ad hoc VoIP network

Push-to-talk (PTT) is a walkie-talkie like service which performs an efficient and instant voice over Internet protocol (VoIP) communication in mobile ad hoc network especially for certain circumstances, such as battle field and earthquake or disaster relief. The authors have designed and implemented the PTT mechanism in ad hoc VoIP network. The PTT server and user agent combined with the pseudo session initiation protocol (SIP) server in the implementation provide the PTT service without standalone SIP server support. The authors also conduct the experimental measurements, in terms of delay and packet loss, in the test-bed to demonstrate the realisation of PTT service in ad hoc VoIP network.