• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.110-115
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• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• Convergence Security Journal
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• v.17 no.3
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• pp.21-29
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• 2017

Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• Convergence Security Journal
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• v.14 no.7
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• pp.23-28
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• 2014

This study proposes the design and implementation of the system for a facial expression recognition system. LDP(Local Directional Pattern) feature computes the edge response in a different direction from a pixel with the relationship of neighbor pixels. It is necessary to be estimated that LDP code can represent facial features correctly under various conditions. In this respect, we build the system of facial expression recognition to test LDP performance quickly and the proposed evaluation system consists of six components. we experiment the recognition rate with local micro patterns (LDP, Gabor, LBP) in the proposed evaluation system.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of architectural history
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• v.18 no.5
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• pp.41-58
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• 2009

The purpose of this study is to investigate the symbolic meaning of pattern designs attached on the main building of Chungcheongnam-do Provincial Government (CPG). While most of researches mainly focused on the value and evaulation of the CPG building in terms of architectural history, relatively little insight has been gained on the symbolic meaning and mature of the pattern designs. What king of connections are related between the emblem of Governor-General of Chosun(Korea) and those of CPG? What symbolic meanings are engraved on the pattern designs? The researcher then took up the task of elucidating symbolic meanings of patterns and their relation to the building. The existing pattern of the outer wall of the CPG building consists of the symbol of sun(太陽輪), mums(菊花輪), and angle of stairs(雁大角). According to the Japanese Studies of emblems, these visual elements symbolize 'the sun of Japan'((日の丸) or 'the emperor', 'the royal family', and 'rays of the sun'. Based on these preliminary findings, the researcher considered the following in-depth connections: (1) relations with the emblem of Governor-General of Chosun, (2) relations with the emblems of local autonomous entity, Kyoungsung in Chosun. (3) relations with the emblem designs of the local provinces in Japan etc. In conclusion, patterns of the main building were not just an architectural decoration, but they had significant meanings utilizing design elements and methods adopted by local autonomous entities in Japan, The patterns found at the ceilling and floor of hall in the CPG building were associated with the emblem of Governor-General of Chosun. Therefore, all the patterns of the CPG building are powerful symbols that have meanings for the colonial rule by using the shape and method of city identities that Japanese local provinces had enacted. That is, it came out into the open that they were designed for special meanings that Korean and Japanese are united as a single body(內鮮一體) for a subject of the Emperor of Japan(皇國臣民).

• Journal of the Semiconductor & Display Technology
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• v.8 no.2
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• pp.23-29
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• 2009

To increase the data capacity of one-dimensional symbology, 2D barcodes have been proposed a decade ago. In this paper, a new 2D barcode detection algorithm based on Local Binary Pattern is presented. To locate 2D barcode symbols, a texture analysis scheme based on the Local Binary Pattern is adopted, and a gray-scale projection with sub-pixel operation is utilized to separate the symbol precisely from the input image. Finally, the segmented symbol is normalized using the inverse perspective transformation for the decoding process. The proposed method ensures high performances under various lighting/printing conditions and strong perspective deformations. Experiments show that our method is very robust and efficient in detecting the symbol area for the various types of 2D barcodes.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.4
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• pp.505-514
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• 1998

Flow pattern of air-water two phase flow depends on the conditions of pressure void fraction and channel geometry. We classify the flow pattern by measuring the output signal of the conductivity probe. under the classified flow pattern we mount a visualization equipment on the test section and take pictures. We vary the concentration of pure solvent and polymer to measure local void fraction. We know that the maximum point position of local void fraction distribution move from the center of the pipe to the wall of the pipe as JSL increase when JSA is constant in two phase flow. But we find that the maximum point position of local void friction move from the wal of the pipe to the center of the pipe when polymer concentration increase.