In this paper, a method for detection of forged signatures based on spectral analysis of directional gradient density function and a weighted fuzzy classifier is proposed. The well defined outline of an incoming signature image is extracted in a preprocessing stage which includes noise reduction, automatic thresholding, image restoration and erosion process. The directional gradient density function derived from extracted signature outline is highly related to the overall shape of signature image, and thus its frequency spectrum is used as a feature set. With this spectral feature set, having a property to be invariant in size, shift, and rotation, a weighted fuzzy classifier is evaluated for the verification of freehand and random forgeries. Experiments show that less than 5% averaged error rate can be achieved on a database of 500 signature samples.

In this paper, a multi-adaptive filter is proposed for removing EOG and the 60 Hz power supply noise from EEG measured in the frontal lobe and the feedback output control method is implemented for biofeedback. The multi-adaptive filter has been implemented on the TMS320C6711 DSP system and the feedback output control algorithm has been realized by calculating the ratio of alpha wave on the TMS320C31 DSP system with real time performance. Through the experiment using the implemented multi-adaptive filter and feedback output controller, we demonstrate that the proposed adaptive filter effectively removes EOG and the 60 Hz power supply noise from the measured EEG in the frontal lobe and the feedback algorithm controls the level of stimulation by the ratio of the alpha wave.

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

The general fractal image compression provides a high compression rate, but it requires a large encoding time. In order to overcome this disadvantage, many researchers have introduced various methods that reduce the total number of domain blocks considering their block similarities or control the number of searching domain block depending on its distribution. In this paper, we propose a method that can reduce the number of searching domain blocks employing the variance coherence of intensity values and also the number of range blocks requiring the domain block search through the classification of range blocks. This proposed method effectively reduces the encoding time and also a negligible drop of the quality as compared with the previous methods requiring the search of all range blocks.

This study has proposed visual-feature extraction methods for each band in wavelet domain with both spatial frequency features and multi resolution features. In addition, it has brought forward similarity measurement method using fuzzy theory and new color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Experiments are performed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

One of the representative methods of optical flow is a gradient method which estimates the movement of an object based on the differential of image brightness. However, the method is ineffective for large displacement of the object and many improved methods have been proposed to copy with such limitations. One of these improved techniques is the multigrid processing, which is used in many optical flow algorithms. As an alternative novel technique we have been proposing an orthogonal functional expansion method, where whole displacements are expanded from low frequency terms. This method is expected to be applicable to flow estimation with large displacement and deformation including expansion and contraction, which are difficult to cope with by conventional optical flow methods. In the orthogonal functional expansion method, the apparent displacement field is calculated iteratively by a projection method which utilizes derivatives of the invariant constraint equations of brightness constancy. One feature of this method is that differentiation of the input image is not necessary, thereby reducing sensitivity to noise. In this paper, we apply our method to several real images in which the objects undergo large displacement and/or deformation including expansion. We demonstrate the effectiveness of the orthogonal functional expansion method by comparing with conventional methods including our optimally scaled multigrid optical flow algorithm.

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

Nowaday, market size of digital image in world around is looks to rapidly growth. For this, Texture mapping has traditionally been used to add realism to computer graphics images. Therefore to make our image realistic, we need to give the various kind of objects material parameter and environment lighting. To present the completed marble we use passing back algorithm and combination with channel of a material. In experimental result of this paper that application by passing back algorithm and varying the parameter such as scale, period, distortion, octaves of noise make showing the superiority of optimized rendering of spheres and perfect another marble effects.

In this paper, we are concerned with a digital fingerprinting scheme for multi-purchase where a buyer wants to buy more than a digital content. If we apply previous schemes to multi-purchase protocol, the number of execution of registration step and decryption key should be increased in proportion to that of digital contents to be purchased in order to keep unlinkability. More worse, most of fingerprinting schemes in the literature are based on either secure multi-party computation or general zero-knowledge proofs with very high computational complexity. These high complexities complicate materialization of fingerprinting protocol more and more. In this paper, we propose a multi-purchase fingerprinting scheme with lower computational complexity. In the proposed scheme, a buyer executes just one-time registration step regardless of the number of contents to be purchased. The number of decryption key is constant and independent of the number of contents to be purchased. We can also reduce the computational costs of buyers by introducing a concept of proxy-based fingerprinting protocol.

The fusion of Internet technology and applications with wireless communication provides a new business model and promises to extend the possibilities of commerce to what is popularly called mobile commerce, or m-commerce. In mobile Internet banking service through wireless local area network, security is a most important factor to consider. We describe the development of security service for mobile Internet banking on Personal Digital Assistants (PDAs). Banking Server and Authentication Server were developed to simulate banking business and to support certificate management of authorized clients, respectively. To increase security, we took hybrid approach in implementation: symmetric block encryption and public-key encryption. Hash function and random number generation were exploited to generate a secret key. The data regarding banking service were encrypted with symmetric block encryption, RC4, and the random number sequence was done with public-key encryption. PDAs communicate through IEEE 802.IIb wireless LAN (Local Area Network) to access banking service. Several banking services and graphic user interfaces, which emulatedthe services of real bank, were developed to verity the working of each security service in PDA, the Banking Server, and the Authentication Server.

This paper proposes an efficient collision detection method in the dynamic spatial subdivisions for the MMORPG engine which requires realtime interactions. An octree is a suitable structure for static scenes or terrain processing. An octree spatial subdivision enhances rendering speed of scenes. Current spatial subdivisions tend to be highly optimized for efficient traversal, but are difficult to update quickly for a changing geometry. When an object moves to the outside extent for the spatial subdivisions, the acceleration structure would normally have to be rebuilt. The OSP based on a tree is used to divide dynamically wide outside which is the subject of 3D MMORPG. TBV does not reconstruct all tree nodes of OSP and has reduced rebuilding times by TBV information of a target node. A collision detection is restricted to those objects contained in the visibility range of sight by using the information established in TBV. We applied the HBV and ray tracing for an efficient collision detection.

In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

Lately mobile handheld devices such as Personal Digital Assistant (PDA) and cellular phones are getting more popular for personal web surfing. However, today most mobile handheld devices have relatively poor web browsing capability due to their low performance so their users have to suffer longer communication latency than those of desktop Personal Computers (PCs). In this paper, we propose an effective pre-refresh mechanism for embedded web browser of mobile handheld devices to reduce this problem. The proposed mechanism uses the idle time to pre-refresh the expired web objects in an embedded web browser's cache memory. It increases the utilization of Central Processing Unit (CPU) power and network bandwidth during the idle time and consequently reduces the client's latency and web browsing cost. An experiment was done using a simulator designed by us to evaluate the efficacy of the proposed mechanism. The experiment result demonstrates that it has a good performance to make web surfing faster.