In this paper, we propose a new fast Hough transform to improve the processing time and line detection of Hough transform that is widely used in various vision systems. First, for the fast processing time, we reduce the number of features by using multi-statistical methods and also reduce the dimension of angle through six separate directions. Next, for improving the line detection, we effectively detect the lines of various directions by designing the line detection method which detects line in proportion to the number of features in six separate directions. The proposed method was evaluated with previous methods and obtained the excellent results. The processing time was improved in about 20% to 50% and line detection was performed better in various directions than conventional methods with experimental images.

We can implement the virtual touch recognition system by mounting the virtual touch algorithm into an embedded device connected to a depth camera. Since the computing performance is limited in embedded system, the real-time processing of recognizing the virtual touch is difficult when the resolution of the depth image is large. In order to resolve the problem, this paper improves the algorithms of binarization and labeling that occupy a lot of time in all processing of virtual touch recognition. It processes the binarization and labeling in only necessary regions rather than all of the picture. By appling the proposed algorithm, the system can recognize the virtual touch in real-time as about 31ms per a frame in the depth image that has 640×480 resolution.

In this paper, we propose the adaptation filtering for correcting the movement path of the recognized object by the depth information. When we recognize the object by the depth information, the path error should be occurred because of the noises in the depth information. The path error is corrected by appling the lowpass filtering, but the lowpass filtering is not efficient when the changes of the object's movement are rapid. In this paper, we apply the adaptation filtering that it gives weights adaptively as the difference between the predicted location and the measured location. To apply the adaptation filtering, we can see that the proposed method can correct accurately the path error of the radical change from simulation results.

Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. In addition, various signal processing techniques have been studied to extract feature vectors which are less sensitive to the location of the receiver. In this paper, we synthesized active echo signals using 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to echo signals to extract signal features. For the performance verification, classification experiments were performed using backpropagation and probabilistic neural network classifiers based on single aspect and multi-aspect method. As a result, we obtained a better recognition result using proposed feature extraction and multi-aspect based method.

Various color spaces such as RGB, HSV, log-chromaticity have been used in the field of person re-identification. However, not enough studies have been done to find suitable color space for the re-identification. This paper reviews color invariance of color spaces by diagonal model and explores the suitability of each color space in the application of person re-identification. It also proposes a method for person re-identification based on a histogram refinement technique and some fusion strategies of color spaces. Two public datasets (ALOI and ImageLab) were used for the suitability test on color space and the ImageLab dataset was used for evaluating the feasibility of the proposed method for person re-identification. Experimental results show that RGB and HSV are more suitable for the re-identification problem than other color spaces such as normalized RGB and log-chromaticity. The cumulative recognition rates up to the third rank under RGB and HSV were 79.3% and 83.6% respectively. Furthermore, the fusion strategy using max score showed performance improvement of 16% or more. These results show that the proposed method is more effective than some other methods that use single color space in person re-identification.

Android application is vulnerable to reverse engineering attack. And by this, it is easy to extract significant module from source code and repackage it. To prevent this problem, dynamic class loading technique, which is able to exclude running code from distributed source code and is able to load running code dynamically during runtime can be used. Recently, this technique was adapted on variety of fields and applications like updating pre-loaded android application, preventing from repacking malicious application, etc. Despite the fact that this technique is used on variety of fields and applications, there is fundamental lack on the study of potential weakness or related secure coding. This paper would deal with potential weaknesses during the implementation of dynamic class loading technique with analysing related international/domestic standard of weaknesses and suggest a secure way for the implementation of dynamic class loading technique. Finally, we believe that this technique described here could increase the level of trust by decreasing the weakness related to dynamic class loading technique.

TPSN(Timing-sync Protocol for Sensor Networks), the representative of time synchronization protocol, has been already developed to provide time synchronization among nodes in wireless sensor networks. Even though the TPSN's method has been referenced by so many other time synchronization schemes for resource-constrained networks like wireless sensor networks or low power personal area networks, it has some inefficiency in terms of power consumption and network-wide synchronization time (or called convergence time). The main reason is that each node in TPSN needs waiting delay to solve the collision problem due to simultaneous transmission among competing nodes, which causes more power consumption and longer network convergence time for a network-wide synchronization. In this paper an improved scheme is proposed by changing message exchange method among nodes. The proposed scheme not only shortens network-wide synchronization time, but also reduce collision traffic which lead to needless power consumption. The proposed scheme's performance has been evaluated and compared with an original scheme by simulation. The results are shown to be better than the original algorithm used in TPSN.

Museums have recently been expanding their roles as education providers to use websites to provide a variety of information and services including educational programs. In order to better perform their roles as education providers, it is important for museums to commit themselves to making their websites accessible and easy for everyone to use, in compliance with web accessibility. 20 of museum websites were chosen to study the state of web accessibility of domestic museum websites, and were evaluated by using an automated web accessibility evaluation tool called the K-WAH 4.4. And then, we analyze the compliance rates of web accessibility based on 6 Requirements in the KWCAG 2.1. As a Result, only two museums satisfy the compliance rates on 6 Requirements and national & public museums are higher than private museums in compliance rates. So, to improve web accessibility, we comply with accessibility guidelines and design web contents. After witch we should improve web accessibility by modifying insufficient requirements immediately through periodical evaluations.