• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• 한국지능시스템학회논문지
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• 제26권4호
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• pp.316-320
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• 2016

본 논문에서는 질감영상의 마이크로패턴 간 공간적인 동시발생 빈도를 고려한 패턴인식을 제안한다. 여기서 마이크로패턴은 블록영상의 중간값에 기반한 국소이진패턴(local binary pattern : LBP)으로 추출되고, 추출된 국소이진패턴들 사이의 동시발생빈도를 고려하여 패턴인식을 수행한다. 중간값 이진패턴은 영상의 국소속성을 고려할 뿐만 아니라 잡음에 강건한 패턴분석을 위함이고, 동시발생빈도는 영상의 전역속성을 고려하여 인식성능을 좀 더 향상시키기 위함이다. 제안된 기법을 120*120 픽셀의 17개 RGB 질감 패턴영상을 대상으로 유클리디언(Euclidean) 거리에 기반한 실험결과, 우수한 인식성능이 있음을 확인하였다.

• 반도체디스플레이기술학회지
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• 제18권4호
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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.

• 반도체디스플레이기술학회지
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• 제8권2호
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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 Electrical Engineering and Technology
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• 제12권1호
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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.

• 융합보안논문지
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• 제14권5호
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• pp.17-24
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• 2014

본 논문에서는 얼굴 표정 인식을 위한 지역미세패턴(local micro pattern)의 하나인 LBP(Local Binary Pattern) 코드의 잡음에 대한 단점을 해결하기위하여 새로운 미세패턴 방법인 LDP(Local Directional Pattern)를 제안한다. 제안된 방법은 LBP의 문제점을 해결하기 위해 $m{\times}m$ 마스크를 이용하여 8개의 방향 성분을 구하고, 이를 크기에 따라서 정렬한 후 상위 k개를 선정하여 해당 방향을 나타내는 비트를 1로 설정한다. 그리고 8개의 방향 비트를 순차적으로 연결하여 최종 패턴 코드를 생성한다. 실험결과, 제안된 방법은 기존 방법에 비해 회전에 대한 영향이 적으며, 잡음에 대한 적응력이 현저히 높았다. 또한, 제안된 방법을 기반으로 얼굴의 영구적인 특징과 일시적인 특징을 함께 표현하는 새로운 지역미세패턴의 개발이 가능함을 확인하였다.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.443-451
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• 2015

In this paper we suggest a new two-wheelers detection algorithm using uniform local binary pattern weighting value for projection vectors. The first, we calculate feature vectors using projection method which has robustness for rotation invariant and reducing dimensionality for each cell from origin image. The second, we applied new weighting values which are calculated by the modified local binary pattern showing the fast compute and simple to implement. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of Multimedia Information System
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• 제1권2호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.