• The Journal of Korea Robotics Society
• /
• v.7 no.2
• /
• pp.120-128
• /
• 2012

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.19 no.5
• /
• pp.219-228
• /
• 2015

For small-size reinforce-concrete buildings, Midas Gen, OpenSees, and Perform-3D, which are structural analysis programs that are most popularly used at present, were applied for nonlinear static pushover analysis, and then difference between those programs was analyzed. Example buildings were limited to 2-story frames only and frames with one or more rectangular walls. Analysis results showed that there was not much difference for frames only based on capacity curves. There were some differences for frames with rectangular walls, but it was not so significant. The global behaviors represented by the capacity curve were not so different, but the feature of each analysis program appeared when the results were analyzed in more detail. Therefore, the program users should understand the feature of the program well, and then conduct performance assessment. The result of this study is limited to low-story frames only and frames with rectangular walls so that it should be noted that it is possible to get different results for frames with non-rectangular walls or mid- to high-rise buildings.

• Journal of the Speleological Society of Korea
• /
• no.63
• /
• pp.61-79
• /
• 2004

The Franz Josef Glacier is sited 250 m above sea level. The moisture westerly winds from Tasman Sea and the feature of the alp geomorphology have made the Franz Josef Slatier. That is why the Franz Josef Slatier is. The Franz Josef Slatier has receded during the past century in response to global warming since the end of the Little ice Age in the 1890s. Even between 2002 and 2003, the Franz Josef Glacier is confirmed retreat ins. This is confirmed by climate factors. The expressing of the Franz Josef Slatier retreating in 2003 which is advanced air temperature and amount of precipitation in 1998 than compared another years. There are lots of metamorphic rock as known biotite, schist, greywack and the Alpine Fault is passing near the Franz Josef Glacier. The grooved and scratching trend surface the rock are observed as the evidence of retreating glacier left.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.633-636
• /
• 2012

최근 디지털카메라와 스마트 폰 등의 모바일 기기가 급속도로 발전 하면서 언제, 어디서나 손쉽게 사진을 찍을 수 있게 되었다. 이런 환경의 변화는 수없이 많은 사진을 양산하게 되었고, 손쉽게 많이 찍은 사진에 대한 분류에 불필요한 시간을 많이 보내게 되었다. 따라서 보다 편리하게 촬영된 사진들을 분류 관리하기에 적합한 자동화된 프로그램이 필요하게 되었다. 이 논문에서는 GPS나 시간 등의 메타 정보에 의존하지 않고 오직 사진의 주 색상을 이용한 히스토그램 특징과 Mean Shift 분류기를 사용하여 대략적인 분류를 시도하려했다. 실험결과를 토대로 살펴보면, 제안된 방법은 사진의 주 색상이 확실한 경우는 잘 분류할 수 있지만 여러 가지 색상이 복잡하게 혼합된 경우와 주 색상을 찾기 어려운 경우에는 분류에 한계가 있음을 알 수 있었다. 따라서 제안된 알고리즘은 사진과 영상들을 개략적인 분류를 실시할 때 주 색상 히스토그램특징이 의미 있는 전역적 특징(Global Feature)중의 하나로 생각된다.

• Science of Emotion and Sensibility
• /
• v.24 no.2
• /
• pp.57-64
• /
• 2021

It has long been speculated that the visual system should use a coding strategy that takes advantage of statistical redundancies in images. But how such a coding strategy should manifest in neural responses has been less clear. Low-level image structure related to the power spectrum of natural images appears to be captured by a hard-wired efficient code in the retina of the fly and precortical structures like the LGN of cats that maximizes information content through the limited capacity channel of the optic nerve. But visual images are typically filled with higher-order structure beyond that captured by the power spectrum and visual cortex is not constrained by the same capacity limits as the optic nerve. Whether and how visual cortex can flexibly code for higher order redundancies is unknown. Here we show using psychophysical techniques that the neural response in early human visual cortex may be modulated by orientation redundancies in images such that a visual feature that is contained within a predictive pattern results in slower reaction times than a feature that deviates from a pattern, suggesting lower neural responses to predictable stimuli in the visual cortex. Our results point to a neural response in early visual cortex that is sensitive to global patterns and redundancies in visual images and is in marked contrast to standard models of cortical visual processing.

• Journal of Digital Convergence
• /
• v.18 no.2
• /
• pp.249-259
• /
• 2020

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set's dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.4
• /
• pp.51-58
• /
• 2008

In this paper, we propose a place modeling based on the distribution of scale-invariant features, and a place recognition method that recognizes places by comparing the place model in a database with the extracted features from input data. The proposed method is based on the assumption that every place can be represented by unique feature distributions that are distinguishable from others. The proposed method uses global information of each place where one place is represented by one distribution model. Therefore, the main contribution of the proposed method is that the time cost corresponding to the increase of the number of places grows linearly without increasing exponentially. For the performance evaluation of the proposed method, the different number of frames and the different number of features are used, respectively. Empirical results illustrate that our approach achieves better performance in space and time cost comparing to other approaches. We expect that the Proposed method is applicable to many ubiquitous systems such as robot navigation, vision system for blind people, wearable computing, and so on.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.2
• /
• pp.99.2-99.2
• /
• 2012

We updated the far-ultraviolet (FUV) spectral images of the entire Vela supernova remnant (SNR) using newly processed FIMS/SPEAR data. In the present study, we compare the newly produced FUV images with the X-ray and $H{\alpha}$ images, and examine how the Vela SNR evolves and interacts with the ambient medium on a global scale. The comparison with X-ray images has revealed a FUV filamentary feature corresponding with the boundary of the northeast-southwest asymmetry of the X-ray shell. The relatively low O IV] ${\lambda}1404$ to O III] ${\lambda}{\lambda}1661$, 1666 ratio estimated on the FUV filament is compatible with the previous proposal that the observed asymmetry of the Vela SNR could be due to the ${\gamma}2$ Velorum stellar wind bubble (SWB). The southwest FUV features surrounding a faint extended X-ray region are characterized as the region where the Vela SNR is interacting slightly stronger with ambient mediums within the dim X-ray southwest section. From a comparison with the $H{\alpha}$ image, we identify a ring-like $H{\alpha}$ feature overlapped with an extended hot X-ray feature of similar size and two local peaks of C IV ${\lambda}{\lambda}1548$, 1551 emission. Their morphologies are consistent with the expected shape when the $H{\alpha}$ ring is in direct contact with the near or far side of the Vela SNR. We suggest that the B3V-type star HD 76161 found at the center of the $H{\alpha}$ ring would be the exciting source of the H II region.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.5
• /
• pp.1393-1402
• /
• 1999

This paper proposes an efficient principal feature extraction of the image data using neural networks of a new learning algorithm. The proposed learning algorithm is a backpropagation(BP) algorithm based on the steepest descent and dynamic tunneling. The BP algorithm based on the steepest descent is applied for high-speed optimization, and the BP algorithm based on the dynamic tunneling is also applied for global optimization. Converging to the local minimum by the BP algorithm of steepest descent, the new initial weights for escaping the local minimum is estimated by the BP algorithm of dynamic tunneling. The proposed algorithm has been applied to the 3 image data of 12${\times}$12pixels and the Lenna image of 128${\times}$128 pixels respectively. The simulation results shows that the proposed algorithm has better performances of the convergence and the feature extraction, in comparison with those using the Sanger method and the Foldiak method for single-layer neural networks and the BP algorithm for multilayer neural network.

• Journal of Intelligence and Information Systems
• /
• v.13 no.4
• /
• pp.35-44
• /
• 2007

This paper proposes a new method of weighted template matching for machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. Template matching is vulnerable to random noises that generate ragged outlines of a pattern when it is binarized. This paper offers a method of chain code trimming in order to remove ragged outlines. The method corrects specific chain codes within the chain codes of the inner and the outer contour of a pattern. The experiment compares confusion matrices of both the template matching and the proposed weighted template matching with chain code trimming. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.