In recent years, the market size for internet advertising has been increasing with the expansion of the Internet. Among the internet advertising technologies, affiliate services, which are a performance-based service, use cookies to track and measure the performance of affiliates. However, for the purpose of safeguarding personal information, cookies tend to be regulated, which leads to concerns over whether normal tracking by cookies works as intended. Therefore, in this study, the recent problems from the perspectives of affiliates, affiliate service providers, and advertisers are extracted, and a framework of cookie-independent measuring engagement method using access logs is proposed and open issues are discussed for future affiliate services.

This paper presents a folded surface detection and tracking method for augmented maps. First, we model a folded surface as two connected planes. Therefore, in order to detect a folded surface, the plane detection method is iteratively applied to the 2D correspondences between an input image and a reference plane. In order to compute the exact folding line from the detected planes for visualization purpose, the intersection line of the planes is computed from their positional relationship. After the detection is done, each plane is individually tracked by the frame-by-frame descriptor update method. We overlay virtual geographic data on each detected plane. As scenario of use, some interactions on the folded surface are introduced. Experimental results show the accuracy and performance of folded surface detection for evaluating the effectiveness of our approach.

When handover events occur during the transmission of multimedia traffic, efficient handover control procedures and radio resource allocation are necessary to maintain the same QoS of transmitted multimedia traffic because the QoS may be degraded by additional delay and information loss. In this paper we propose a new handover control method for the next generation mobile multimedia networks, in which the handover setup process is done in advance of a handover request by predicting the handover cell from mobile terminal's current position. The handover procedures for real-time sessions are performed based on the handover cell information and the resource reservation condition. The radio resources in the estimated adjacent cells should be reserved and allocated to guarantee the continuity of the real-time sessions. We conduct a simulation model that is focused on the handover failure rate and packet loss rate. The simulation results show that our proposed method provides better performance than the previous methods.

Augmented reality tasks require a high-reliability tracking method. Large tracking error causes many problems during AR applications. Tracking error estimation should be integrated with them to improve the reliability of tracking methods. Although some tracking error estimation methods have been developed, they are not feasible to be integrated because of computational speed and accuracy. For this study, a tracking error estimation algorithm with screen error estimation based on the characteristic of linecode marker was applied. It can rapidly estimate tracking error. An evaluation experiment was conducted to compare the estimated tracking error and the actual measured tracking error. Results show that the algorithm is reliable and sufficiently fast to be used for real-time tracking error warning or tracking accuracy improvement methods.

Nuclear power plants (NPPs) must be maintained periodically. Their maintenance efficiency must be improved and human error must be reduced simultaneously to improve NPPs' competitive capability in electricity markets. Although Augmented Reality (AR) offers great possibilities to support NPP maintenance work, some difficulties exist for application of AR to actual work support because current AR systems cannot be implemented in NPP environments without technical improvement. There are several kinds of problems such as recognition distance, tracking accuracy, and a complex working environment when applying AR to NPP field work support. Considerable extension of tracking distance and improvement of accuracy are particularly desired because NPPs are large-scale indoor environments. This study designed a linecode marker, a new type of paper-based marker, along with recognition and tracking algorithms for it to resolve these problems. In contrast to conventional paper-based markers, such as square markers and circle markers, the linecode marker is not merely easier to set up in complex industrial environments: it also enables the use of AR in industrial plants because of its considerable tracking-performance improvement. To evaluate tracking accuracy, the trackable distance, and the tracking speed of the proposed tracking method, an evaluation experiment was conducted in a large room. The experiment results show that the tracking distance is extended extremely over that of the traditional marker-based tracking method: tracking accuracy improved to 20 cm over a 10 m distance. The running speed of the tracking can be as fast as 15 frames per second using a laptop PC.

In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.