• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.128-134
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• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.127-134
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• 2020

In this paper, we propose a map alignment method for maps generated by point-line monocular SLAM. In the proposed method, the information of feature lines as well as feature points extracted from multiple maps are fused into a single map. To this end, the proposed method first searches for similar areas between maps via Bag-of-Words-based image matching. Thereafter, it calculates the similarity transformation between the maps in the corresponding areas to align the maps. Finally, we merge the overlapped information of multiple maps into a single map by removing duplicate information from similar areas. Experimental results show that maps created by different users are combined into a single map, and the accuracy of the fused map is similar with the one generated by a single user. We expect that the proposed method can be utilized for fast imagery map generation.

• International Journal of Contents
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• v.9 no.3
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• pp.15-18
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• 2013

In the preceding studies, an analysis of Saga Prefectural sightseeing information by a Self-Organizing Map (SOM) has been tried. And recent development on Information and Communication Technology (ICT) will help us to access any results via the mobile devices easily. This is why the mobile devices, e.g., smartphones and tablet computers, have an operating system installed, and we can improve their functions by downloading any applications on the Web. Then, in order to realize this basic idea, development of an application for the mobile devices is investigated through some computer simulations on the standard desktop PC in this paper. As a result, it is found that i) a developed feature map is useful to identify some candidate topics, ii) a touchscreen is suitable to show the feature map, and iii) arrangement of the feature map can be modified based on our interests. Then, it is concluded that the proposed idea seems to be applicable, even though further consideration is required to brush it up.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.3-4
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• 2015

A self-organizing map (SOM) is a good tool to visualize applied data in the form of a feature map. With the help of such functions, a disaster risk diagnosis based on the residential house structure is tried in this study. According to some computer simulations with actual residential data, it is found that overall tendencies in the developed feature map are acceptable. Then, it is concluded that the proposed method is an effective means to estimate disaster risk appropriately.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.203-210
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• 2009

The problem of outdoor localization can be practically solved by GPS. However, GPS is not perfect and some areas of outdoor navigation should consider other solutions. This research deals with outdoor localization using an elevation map without GPS. This paper proposes a novel feature, elevation moment of inertia (EMOI), which represents the distribution of elevation as a function of distance from a robot in the elevation map. Each cell of an elevation map has its own EMOI, and outdoor localization can be performed by matching EMOIs obtained from the robot and the pre-given elevation map. The experiments and simulations show that the proposed EMOI can be usefully exploited for outdoor localization with an elevation map and this feature can be easily applied to other probabilistic approaches such as Markov localization method.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.83-90
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• 1994

In this paper, we propose a new learning algorithm, ASOFM(Adaptive Self Organizing Feature Map), to solve the defects of Kohonen's Self Organiaing Feature Map. Kohonen's algorithm is sometimes stranded on local minima for the initial weights. The proposed algorithm uses an object function which can evaluate the state of network in learning and adjusts the learning rate adaptively according to the evaluation of the object function. As a result, it is always guaranteed that the state of network is converged to the global minimum value and it has a capacity of generalized learning by adaptively. It is reduce that the learning time of our algorithm is about $30\%$ of Kohonen's.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.38-43
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• 1991

In order to cluster the Input pattern neatly, some neural network modified from Kohonen's self organizing feature map is introduced and Korean phoneme recognition experiments are performed using the modified self organizing feature map(MSOFM) and the auditory model.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1436-1438
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• 2003

A prototype of OGC Web Map Service and Web Feature Service is developed by NEC Aerospace Systems, Ltd. under the contract from NASDA and RESTEC. The server provides satellite imagery data taken by JERS-1 and ADEOS satellite, via OGC interfaces to the client. The related features are also available to the client through the Internet with OGC interfaces. The imagery, map and feature data is handled layer by layer at the client. OGC Web Map Service and Web Feature Service interoperability capability and applicability to Digital Asia Network are demonstrated.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.817-822
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• 2004

This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.402-405
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• 2000

In this paper we propose a method ef pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through leaning and therefore it can classify input patterns. The raw EMC signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.