Search

The emergence of wireless and mobile networks has made possible the introduction of electronic commerce to a new application and research subject: mobile commerce. Understanding or constructing a mobile or an electronic commerce system is an arduous task because the system involves a wide variety of disciplines and technologies and the technologies are constantly changing. To facilitate understanding and constructing such a system, this article divides the system into six components: (i) applications, (ii) client computers or devices, (iii) mobile middleware, (iv) wireless networks, (v) wired networks, and (vi) host computers. Elements in these components specifically related to the subject are described in detail and lists of current technologies for component construction are discussed. Another important and complicated issue related to the subject is the mobile or electronic commerce application programming. It includes two types of programming: client-side and server-side programming, which will be introduced too.

We proposed an application for reversible information hiding in multiple secret images sharing based on Shamir’s method. Many researchers develop secret image sharing method with Shamir’s scheme in one image. We mixed the multiples secret images with one host image and employ the modified Shamir’s method to share the participants. Lagrange method is applied with enough numbers of the shadow images in the retrieval step. The experiment results demonstrate our method can retrieve secret images lossless.

We propose a data hiding method with color image palette. Many previous authors often embed data into the index table of the color palette or into the color palette directly. Those data hiding methods will change the color palette into a difference one. It is impossible to reconstruct the original color palette. We follow previous method to apply the K-means clustering to split the color image palette into several groups first. If a palette group has k numbers indices, the data can be embedded ¾ log2k Î bits into the pixel that its palette index falls in this group. the current embedding pixel will be replaced by the same group of pixel in the order of embedding data value which are corresponding each palette index. Next step, we collect those k {2 ¾ log2k Î in each groups to use the Euclidean distance to cluster two groups with similar group that have similar index pairs. We embed one bit in each pixel that belongs these two groups. the extraction method firstly groups of the color palette index of stego-image by k-means clustering and similar pairs like embedding steps. If the palette index of the pixel belong to groups that produced by K-means clustering, then finds what order in its group. That order is the embedded value. If the palette index of the pixel belong to groups that yielded by similar pairs, then it belongs to first group is embedded 0, else is 1. the information can be extracted completely until all embedded pixels are retrieved or retrieved to the last bit of host image. from the experimental results, the method has the more embedding capacity then it only uses k-means clustering in the same image.

We proposed a fast secret image sharing based on Haar wavelet transform and Shamir's method. Many researchers develop secret image sharing method with Shamir's algorithm in one whole image. We employ discrete Haar wavelet transform to reduce the secret image to its quarter size firstly, i.e. the 1-level LL subband. Then only apply the modified Shamir's algorithm to this LL subband to generate the shadow images. Lagrange method exploits the enough numbers of the shadow images in the retrieval step. Due to the Shamir's and Lagrange algorithms consume a lot of time during the processing of the secret image sharing. Our method only processes a quarter size of image that can decrease computation time, and still achieve to recover the secret image reversible. The experiment results demonstrate our method is fast and retrieve secret image lossless.

Using mobile handheld devices such as smart cellular phones and personal digital assistants (PDAs) to browse the mobile Internet is a trend of Web browsing. However, the small screens of handheld devices and slow mobile data transmission make the mobile Web browsing awkward. This research applies Web usage mining technologies to adaptive Web viewing for handheld devices. Web usage mining is the application of data mining techniques to the usage logs of large Web data repositories in order to produce results that can be applied to many practical subjects, such as improving Web sites/pages. A Web usage mining system must be able to perform five major functions: (i) usage data gathering, (ii) data preparation, (iii) navigation pattern discovery, (iv) pattern analysis and visualization, and (v) pattern applications. This approach improves the readability and download speed of mobile Web pages.

Mobile commerce is defined as the exchange or buying and selling of commodities, services, or information on the Internet through the use of mobile, handheld devices such as smart cellular phones and PDAs (personal digital assistants). It is widely acknowledged that mobile commerce is a field of enormous potential. However, it is also commonly admitted that the development in this field is constrained. There are considerable barriers waiting to be overcome. One of the barriers is most software engineers are not familiar with the design and development of mobile applications (Kiely, 2001). This chapter gives a study of handheld computing and programming to help software engineers better understanding this subject. Handheld computing is to use handheld devices to perform wireless, mobile, handheld operations such as personal data management and making phone calls. They can be achieved by using server or client- side handheld computing and programming: • Server-side handheld computing and programming: Server-side handheld computing is to use handheld devices to perform wireless, mobile, handheld operations, which require the supports of server-side computing. The most common applications of server-side handheld programming are the mobile Web contents. • Client-side handheld computing and programming: Client-side handheld computing is to use handheld devices to perform handheld operations, which do not need the supports of server-side computing. Most client-side handheld programming languages are a version of either C/C++ or Java. Examples of the application development of Java ME, a version of Java, and Palm OS, using a version of C, will be given.

Reliable image matching is important to many problems in computer vision, image processing and pattern recognition. Hausdorff distance and many of its variations have been employed for image matching with success. In this paper we propose an improved image matching method based on a modified Hausdorff distance with normalized gradient consistency measure. The proposed new image matching algorithm integrates the geometric Hausdorff distance with the photometric intensity gradient information to obtain a better image similarity measure. To show the improvement of the proposed algorithm, we test it with some previous image matching methods on the problem of face recognition under lighting changes. Experimental results show the proposed method produces more accurate face recognition than the previous methods.

We focus on the problem of comparing face images under different lighting conditions. A new and robust face image matching algorithm is developed based on an accumulated consistency measure of corresponding normalized gradients at face contour locations between two face images. The proposed new image matching approach is motivated by the characteristics of high image gradient along the face contour. To amend the matching problem due to lighting changes between face images, we define a new consistency measure to be the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by a maximal gradient magnitude in a local neighborhood centered at the pixel of computation. Then we compute the summation of the individual consistency measure from normalized gradients at all the contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted consistency measure. We test the proposed image matching algorithm on the Yale Face Database, which contains 15 persons captured under three very different lighting conditions. The proposed method can achieve satisfactory recognition results in the experiments.

Face recognition is a challenging problem that has attracted a number of researchers from academia and industry in recent years. One of the main challenges in face image recognition is due to illumination variations. Face matching is a crucial step for these problems. A reliable face recognition algorithm robust against illumination variation is proposed. The basic idea in our method is to accumulate the consistency measure of corresponding normalized gradients at the face contour locations from two comparing face images. The consistency measure is defined to be the inner product between two normalized gradient vectors at the corresponding locations in the two images. To compensate for lighting variations, three face images captured with very different lighting directions of each person are integrated into our face recognition method. We tested the proposed algorithm on the Yale face database B and compared with three other methods. Satisfactory recognition results are shown from face recognition experiments under different illumination conditions.

The detection of hazardous targets frequently requires a multispectral approach to image acquisition and analysis, which we have implemented in a software system called MATRE (multispectral automated target recognition and enhancement). MATRE provides capabilities of image enhancement, image database management, spectral signature extraction and visualization, statistical analysis of greyscale imagery, as well as 2D and 3D image processing operations. Our system is based upon a client-server architecture that is amenable to distributed implementation. In this paper, we discuss salient issues and requirements for multispectral recognition of hazardous targets, and show that our software fulfills or exceeds such requirements. MATRE's capabilities, as well as statistical and morphological analysis results, are exemplified with emphasis upon computational cost, ease of installation, and maintenance on various Unix platforms. Additionally, MATRE's image processing functions can be coded in vector-parallel form, for ease of implementation of SIMD-parallel processors. Our algorithms are expressed in terms of image algebra, a concise, rigorous notation that unifies linear and nonlinear mathematics in the image domain. An image algebra class library for the C + + language has been incorporated into the our system, which facilitates fast algorithm prototyping without the numerous drawbacks of descrete coding.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In Part I of this two-part series, we described the adaptive double-gated filter (DGF), a nonlinear CS detector that is sensitive to target shape and statistical properties. In particular, we derived the DGFs parameters from knowledge of target shape, size, and statistics, as well as background statistics. Several variants of the DGF were presented different for target recognition regimes (e.g., smooth targets against rough backgrounds, and vice versa). An additional variant of the DGF, known as the adaptive triple-gated-filter (TGF) was presented which aids in target registration. In this paper, we analyze the errors inherent in the DGF and TGF. Our analyses emphasize the performance of the DGF in limiting conditions, including high noise and poorly defined targets. Additionally, we discuss implementational optimization of the DGFs performance and time complexity. Examples of DGFs application to a database of field images presented, with discussion of results stated in terms of probability of hits and rate of false alarms.

Center-surround (CS) fields have long been identified in the human visual system as having properties of edge enhancement that facilitate the detection, location, and tracking of small objects. Unfortunately, the automatic digital implementation of such capabilities is not straightforward, since digital computers lack the neural functionality and connectivity inherent in the retina, optic chiasm, geniculate nuclei, and visual cortex. In this paper, we discuss efficient techniques for implementing CS detectors in terms of a class of nonlinear filters. In particular, we emphasize image enhancement schemes that cue the registration of CS detectors over the target, thus rendering our filters implementationally feasible in terms of hit rate and computational cost. Examples are given of the CS detectors that we call the adaptive Double- Gated and Triple-Gated filters (DGF and TGF). Such filters account for spatial nonstationary input. We further show how the DGF and TGF are useful for object detection in multispectral imagery. Analyses emphasize error and computational cost in practical sensing scenarios.

Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions.