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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권11호
• /
• pp.5474-5490
• /
• 2017

Although two-dimensional principal component analysis (2DPCA) has been shown to be successful in face recognition system, it is still very sensitive to illumination variations. To reduce the effect of these variations, texture-based techniques are used due to their robustness to these variations. In this paper, we explore several texture-based techniques and determine the most appropriate one to be used with 2DPCA-based techniques for face recognition. We also propose a new distance metric computation in 2DPCA called Row Assembled Matrix Distance (RowAMD). Experiments on Yale Face Database, Extended Yale Face Database B, AR Database and LFW Database reveal that the proposed RowAMD distance computation method outperforms other conventional distance metrics when Local Line Binary Pattern (LLBP) and Multi-scale Block Local Binary Pattern (MB-LBP) are used for face authentication and face identification, respectively. In addition to this, the results also demonstrate the robustness of the proposed RowAMD with several texture-based techniques.

• 반도체디스플레이기술학회지
• /
• 제11권2호
• /
• pp.45-51
• /
• 2012

Multilayer co-extrusion blown film construction is a popular technique for producing plastic films for various packaging industries. Automated detection of defective films can improve the quality of film production process. In this paper, we propose a film inspection system that can detect and classify film defects robustly. In our system, first, film images are acquired through a high speed line-scan camera under an appropriate lighting system. In order to detect and classify film defects, an inspection algorithm is developed. The algorithm divides the typical film defects into two groups: intensity-based and texture-based. Intensity-based defects are classified based on geometric features. Whereas, to classify texture-based defects, a texture analysis technique based on local binary pattern (LBP) is adopted. Experimental results revealed that our film inspection system is effective in detecting and classifying defects for the multilayer co-extrusion blown film construction line.

• International Journal of Contents
• /
• 제9권2호
• /
• pp.8-13
• /
• 2013

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

• 한국컴퓨터정보학회논문지
• /
• 제19권10호
• /
• pp.63-70
• /
• 2014

주어진 이진영상 안에 존재하는 객체를 인식하기 위해서는 영상분할과 패턴정합 과정을 거친다. 영상 내의 이진 객체들이 서로 분리되었다는 조건 하에서는 면적, 경계선의 길이, 또는 그들 사이의 비례 등과 같은 대상 전체의 특징을 기술하는 전역적 특징을 이용해서 객체를 인식할 수 있지만 객체들이 서로에 의해 부분적으로 가리어져 있으면 전역적 특징은 사용될 수 없고 점, 선분 등 객체의 부분을 기술하는 국지적 특징들을 이용해서 인식해야 한다. 본 논문에서는 모델의 경계선상의 곡률이 큰 점들을 추출하여 특징점으로 삼고, 그 가운데 두 점을 택하여 하나의 국지적 특징으로 사용한다. 또한 모델과 입력영상에서 각기 추출된 국지적 특징들을 비교하여 정합함으로써 부분적으로 가려진 객체를 인식하는 방법을 제안하고 있다. 특징점의 쌍으로 표현되는 국지적 특징을 서로 비교함에 있어서 두 점간의 거리와 양 특징점에서의 그래디언트 벡터의 사이 각을 일치시키는데 필요한 탄성변형 에너지를 이용하여 국지적 특징 사이의 유사도를 정의한다. 인식대상 객체 상의 한 특징점의 레이블을 다른 특징점의 레이블들이 얼마나 지지하는 지를 계산함으로써 부분적으로 가려진 객체를 안정적으로 인식하는 방법을 제안한다. Kimia-25 데이터에 대한 실험 결과 최대 클리크 알고리즘의 4.5배의 속도로 동일한 인식률을 얻음을 보였다.