We propose a new method to precisely detect pupil contours in face images. Pupil contour detection is necessary for various applications using face images. It is, however, difficult to detect pupils precisely because of their weak edges or lack of edges. The proposed method is based on minimizing the energy of pattern and edge. The basic idea of this method is that the energy, which consists of the pattern and the edge energy, has to be minimized. An efficient search method is also introduced to overcome the underlying problem of efficiency in energy minimization methods. "Guide patterns" are introduced for this purpose. Moreover, to detect pupils more precisely we use an ellipse model as pupil shape in this paper. Experimental results show the effectiveness of the proposed method.

A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.

Edge linking is a fundamental computer vision task, yet presents difficulties arising from the lack of information in the image. Viewed as a constrained optimization problem, it is NP hard-being isomorphic to the classical Traveling Salesman Problem. This paper proposes a gradient ascent learning algorithm of the elastic net approach for edge linking of images. The learning algorithm has two phases: an elastic net phase, and a gradient ascent phase. The elastic net phase minimizes the path through the edge points. The procedure is equivalent to gradient descent of an energy function, and leads to a local minimum of energy that represents a good solution to the problem. Once the elastic net gets stuck in local minima, the gradient ascent phase attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. Thus, these two phases are repeated until the elastic net gets out of local minima and produces the shortest or better contour through edge points. We test the algorithm on a set of artificial images devised with the aim of demonstrating the sort of features that may occur in real images. For all problems, the systems are shown to be capable of escaping from the elastic net local minima and producing more meaningful contours than the original elastic net.

Edge detection has been an essential step in image processing, and there has been much work undertaken to date. This paper inspects a fuzzy mathematical morphology in order to reach a higher-level of edge-image processing. The proposed scheme uses a fuzzy morphological gradient to detect object boundaries, when the boundaries are roughly defined as a curve or a surface separating homogeneous regions. The automatic edge detection algorithm consists of two major steps. First, a new version of anisotropic diffusion is proposed for edge detection and image restoration. All improvements of the new version use fuzzy mathematical morphology to preserve the edge accuracy and to restore the images to homogeneity. Second, the fuzzy morphological gradient operation detects the step edges between the homogeneous regions as object boundaries. This operation uses geometrical characteristics contained in the structuring element in order to extract the edge features in the set of edgeness, a set consisting of the quality values of the edge pixels. This set is prepared with fuzzy logic for decision and selection of authentic edge pixels. For experimental results, the proposed method has been tested successfully with both synthetic and real pictures.

A distributor of digital contents desires to collect users' attributes. On the other hand, the users do not desire to offer the attributes owing to the privacy protection. Previously, an anonymous survey system for attributes statistics is proposed. In this system, asking trusted third parties' helps, a distributor can obtain the correct statistics of users' attributes, such as gender and age, while no information beyond the statistics is revealed. However, the system suffers from the inefficiency of a protocol to generate the statistics, since the cost depends on the number of all the users registering this survey system. This paper proposes an anonymous survey system, where this cost is independent from the number of all the registering users. In this accomplishment, a group signature scheme with attribute tracing is also proposed. A conventional group signature scheme allows a group member to anonymously sign a message on behalf of the group, while only a designated party can identify the signer. The proposed scheme further enables the party to trace signer's attribute.

InGaP/GaAs HBT with novel ledge coupled capacitor (LCC) structure has been proposed and demonstrated for the first time. The LCC employs an extrinsic InGaP ledge layer as a capacitor parallel to the base resistor. This configuration enables feeding RF signals directly into the base without passing them through the base resistor. With the fabricated HBT, no increase of leakage current between emitter and base electrode was observed. The maximum oscillation frequency (fmax) of the HBT was improved by 10 GHz as compared with an HBT without the LCC.

Association mining extracts common relationships among a finite number of categorical data objects in a set of transactions. However, if the data objects are not categorical and potentially unlimited, it is impossible to employ the association mining approach. On the other hand, clustering is suitable for modeling a large number of non-categorical data objects as long as there exists a distance measure among them. Although it has been used to classify data objects in a data set into groups of similar objects based on data similarity, it can be used to extract the properties of similar data objects commonly appearing in a set of transactions. In this paper, a new clustering method, CLOCK, is proposed to find common knowledge such as frequent ranges of similar objects in a set of transactions. The common knowledge of data objects in the transactions can be represented by the occurrence frequency of similar data objects in terms of a transaction as well as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a summarized profile. As a result, any data difference between a newly collected transaction and the common knowledge of past transactions can be easily identified.

So far, a lot of analyses have been performed on the edge-guide mode isolator with one short edge. However, the detailed characteristics such as the influence of shape of a metal strip and the thickness of a ferrite substrate have not been revealed. This paper clarifies the influence of the structure on the frequency response both experimentally and numerically. The numerical analysis is performed by the FDTD method. The numerical results indicate that the frequency response does not depend on the thickness of ferrite substrate but does on the shape of the metal strip. Furthermore, based on the numerical results, the experiment is carried out on the prototype isolator. All the results provide a theory of the optimum design on the isolator.

In this paper, we propose a fast full-search block matching algorithm for motion estimation, based on binary edge information. The binary edge information allows a faster search by reducing the computational complexity. It also reduces error, which is generated by the block located on the boundary of moving objects. After we transform the input image into an edge-based image using Sobel masks, we convert the result into a binary edge image using median-cut quantization. We then perform block matching using the binary edge image. If there exists blocks such that the error of the binary block matching exceeds threshold, we only perform edge intensity-based block matching within those blocks. We improve computational efficiency by eliminating an unnecessary searching process in no-motion regions. Simulation results have shown that the proposed method reduces the computational complexity and provides similar PSNR performance to the Full Search Block Matching Algorithm (FS-BMA)

We present a design and implementation of a QoS control mechanism in an Adaptive Multimedia Communication System (AMCS) using multiagent-based computing technology. In this paper, we first define functional requirements for AMCS. Subsequently we describe the design and implementation of AMCS with a knowledge-based multiagent framework to fulfill the functional requirements. Moreover we evaluate the adaptability of the prototype systems of AMCS with the operational situations observed in its experiments. From the result of the experiments, we conclude that the multiagent-based design and implementation is reasonable for construction of AMCS.

In this paper, we propose a scalable Extended Differentiated-Services (EDS) architecture to guarantee edge-to-edge explicit rate allocation. In presence of flows with explicit rate allocation, to share bandwidth fairly, a new fairness definition is proposed. Based on EDS and the proposed fairness definition, a scalable fair Edge-to-Edge Congestion Control Algorithm with Explicit Rate Allocation (ECC-ERA) is presented to solve the bandwidth assurance problem facing Differentiated Service architecture, where EDS uses congestion control packets to carry the flow-related states and congestion control information. By designing efficiency control and fairness control separately, the ECC-ERA can achieve good scalability to link capacity, round-trip time and number of flows. It will be shown that EDS plus ECC-ERA outperforms the general Diff-Serv bandwidth guarantee approaches. The main advantages of EDS+ECC-ERA are as follows: (1) it not only can guarantee explicit rate allocation, but also can guarantee near-zero packet loss in core routers, high utilization, lower and smoother queueing delay, better fairness and better protection from unresponsive traffic. (2) Neither resource pre-reservation nor sophisticated scheduling mechanisms are required. The simple FIFO at core routers is enough. (3) EDS plus EC-ERA is very efficient and can be used as end-to-end QoS building block.

The current paper presents an effective deblocking algorithm for block-based coded images using singularity detection in a wavelet transform. Blocking artifacts appear periodically at block boundaries in block-based coded images. The local maxima of a wavelet transform modulus detect all singularities, including blocking artifacts, from multiscale edges. Accordingly, the current study discriminates between a blocking artifact and an edge by estimating the Lipschitz regularity of the local maxima and removing the wavelet transform modulus of a blocking artifact that has a negative Lipschitz regularity exponent. Experimental results showed that the performance of the proposed algorithm was objectively and subjectively superior.

This paper proposes a technique for indexing, clustering and retrieving images based on their edge features. In this technique, images are decomposed into several frequency bands using the Haar wavelet transform. From the one-level decomposition sub-bands an edge image is formed. Next, the higher order auto-correlation function is applied on the edge image to extract the edge features. These higher order autocorrelation features are normalized to generate a compact feature vector, which is invariant to shift, image size. We used direction cosine as measure of distance not to be influenced by difference of each image's luminance. Then, these feature vectors are clustered by a self-organizing map (SOM) based on their edge feature similarity. The performed experiments show higher precision and recall of this technique than traditional ways in clustering and retrieving images in a large image database environment.

Computation of scale space images requires numerical integration of partial differential equations, which demands large computational costs especially in nonlinear cases. In this paper, we present a computational scheme for nonlinear scale spaces based on iterated filtering of original images. This scheme is found to be a special case of numerical integration with a particularly adapted integration steplength. We show the stability of the iteration with local windows and that with global ones and analyze the deformation of edge waveforms in the filtering. Computational costs are evaluated experimentally for both local and global windows and finally we apply the nonlinear multi-scale smoothing to contrast enhancement of images.

Discrete cosine transform (DCT) coding has been proven to be an efficient means of image compression coding. A lot of efforts have been made to improve the coding efficiency of DCT based coding. This paper presents an adaptive DCT coding based on geometrical edge representation. This scheme is designed to properly exploit the correlation between edge direction and distribution of DCT coefficients. Edges are extracted from original images first. Then, sub-optimal block-size and scanning order are determined at each block based on the extracted edges. In this way an adaptive DCT scheme taking account of local characteristics of image can be achieved. It is shown through the simulations that the proposed algorithm outperforms a conventional coding scheme in terms of coding efficiency by 10-15%.

Assume that each edge e of a graph G is assigned a list (set) L(e) of colors. Then an edge-coloring of G is called an L-edge-coloring if each edge e of G is colored with a color contained in L(e). In this paper, we prove that any series-parallel simple graph G has an L-edge-coloring if |L(e)| max{3,d(v),d(w)} for each edge e = vw, where d(v) and d(w) are the degrees of the ends v and w of e, respectively. Our proof yields a linear algorithm for finding an L-edge-coloring of series-parallel graphs.

In the last three decades of the 20th Century, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems for business automation, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. Although we have witnessed many new technological promises, we have also encountered a number of practical limitations that hinder a widespread deployment of applications and services. On one hand, fast progress was observed in statistical speech and language modeling. On the other hand only spotty successes have been reported in applying knowledge sources in acoustics, speech and language science to improving speech recognition performance and robustness to adverse conditions. In this paper we review some key advances in several areas of speech recognition. A bottom-up detection framework is also proposed to facilitate worldwide research collaboration for incorporating technology advances in both statistical modeling and knowledge integration into going beyond the current speech recognition limitations and benefiting the society in the 21st century.

Changing vehicle structures and backgrounds makes it very difficult to correctly extract a license plate region from a vehicle image. In this paper, we propose a simple method to extract the license plate region using edge properties of wavelet subband. The High Frequency Subband (HFS) of an image has edge information for each direction. Edge information is concentrated in each direction of the Headlight-Radiator-Headlight (H-R-H) and the license plate region compared to other regions in the vehicle image. This paper shows a license plate region extraction method using these edge properties and our experimental results with various vehicle images.

Assume that each edge e of a graph G is assigned a list (set) L(e) of colors. Then an edge-coloring of G is called an L-edge-coloring if each edge e of G is colored with a color contained in L(e). It is known that any series-parallel simple graph G has an L-edge-coloring if either (i) |L(e)| max{4,d(v),d(w)} for each edge e=vw or (ii) the maximum degree of G is at most three and |L(e)| 3 for each edge e, where d(v) and d(w) are the degrees of the ends v and w of e, respectively. In this paper we give a linear-time algorithm for finding such an L-edge-coloring of a series-parallel graph G.

We suggest a new probe message structure and an efficient probe-based deadlock detection and recovery algorithm that can be used in distributed database systems. We determine the characteristics of the probe messages and suggest an algorithm that can reduce the communication cost required for deadlock detection and recovery.