• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.530-536
• /
• 2021

Since the LBP algorithm has the characteristic of local texture expression, it is possible to obtain completely different results depending on the extraction location and the size of the reference image and the sample image. In order to solve these shortcomings, in this paper, we first investigate the basic characteristics of LBP, make the size of the reference image (100×100) in order to include most of the characteristics in the image, and select a sample image (40×40) extracted from an arbitrary point. After finding the matching position in the LBP of the reference image by using the correlation test between the LBP of the reference image and the LBP of the sample image, a chi analysis method is used to find the reference image that most closely matches the sample image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.197-200
• /
• 2014

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image. The proposed method produces good results of facial expression and discriminates risk situation well.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.06a
• /
• pp.253-255
• /
• 2016

본 논문에서는 다중 지역 이진 패턴(Multi-scale Bock LBP, MB-LBP) 특징과 랜덤 포레스트에 기반한 새로운 기법의 머리 방향 분류 기법을 제안한다. 제안 기법에서는 occlusion 과 조명의 변화에 강인한 분류 정확도를 얻기 위해서 랜덤화된 트리를 학습하는 것을 목표로 한다. 우선, 얼굴 이미지로부터 많은 MB-LBP 특징을 추출하고, 얼굴 영상들을 랜덤하게 입력하고 MB-LBP 크기 파라미터와 같은 랜덤 특징과 블록 좌표들을 사용하여 트리를 생성한다. 게다가 각 노드에서 정보 이득을 최대화 하는 트리의 내부 노드를 생성하기 위해서 uniform LBP 의 특성을 고려한 분할 함수를 개발한다. 랜덤화된 트리는 랜덤 포레스트에 포함되어 있으며 마지막 결정단계에서 Maximum-A-Posteriori criterion 으로 최종 결정을 한다. 실험 결과는 제안 기법이 다양한 조명, 자세, 표현, occlusion 상황에서 기존의 방법보다 개선된 성능으로 머리 방향을 분류 할 수 있음을 보여준다.

• Physical Therapy Korea
• /
• v.21 no.4
• /
• pp.40-48
• /
• 2014

Differential item functioning (DIF) based on Rasch model can be used to examine whether the items function similarly across different groups and identify items that appear to be too easy or difficult after controlling for the ability levels of the compared groups. The Oswestry low back pain disability (Oswestry) has traditionally been proved as an effective instrument measuring disability resulting from low back pain (LBP). In this study, DIF method was used to explore whether items on the Oswestry perform similarly across two different groups (participants with LBP and no LBP). A series of Rasch analyses on the 10 items of the Oswestry were performed using Winsteps$^{(R)}$ software. Forty-two participants with back pain were recruited from 3 rehabilitation hospitals in Gainesville, Florida. Another 42 participants with no LBP were recruited from several public places in the rehabilitation hospitals. Based on the DIF analysis across the two groups, several items were found to have an uniform DIF. Participants with no LBP had more difficulty on lifting and personal care items and participants with LBP had more difficulty on sleeping and social life items. For non-LBP group, a high ceiling effects (83% of participants with non-LBP) was detected, which was not be able to be effectively measured with the Oswestry items. Although 4 items of the Oswestry function differently across the two groups, all items of the Oswestry were well targeted the LBP group.

• International Journal of Internet, Broadcasting and Communication
• /
• v.7 no.2
• /
• pp.51-55
• /
• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing dangerous situation. The proposed method firstly extracts the facial region using Harr-like technique from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, detects facial expression, and recognizes dangerous situation. The proposed method is evaluated for MUCT database image and web cam input. The proposed method produces good results of facial expression and discriminates dangerous situation well and the average recognition rate is 91.05%.

• Journal of the Korean Society of Food Culture
• /
• v.34 no.6
• /
• pp.793-800
• /
• 2019

This study was performed to examine the physicochemical and antioxidative properties of the sponge cakes with different contents (0, 2, 4, 6, 8%, w/w) of lemon balm (Melissa officinalis) powder (LBP). An increase in LBP content in the cake led to a significant increase in the baking loss rate, specific volume, DPPH radical scavenging activity, ABTS radical scavenging activity, total phenol contents and total flavonoid contents of the cakes (p<0.05). As the LBP content increased, significant decreases were shown in the specific gravity of batter, sugar contents, pH, lightness, redness and yellowness of the cakes (p<0.05). Ash contents, uniformity index and other textural properties of hardness, springness, cohesiveness, and brittleness did not show any significant differences between the sample groups (p>0.05). These results suggest that LBP can be applied to sponge cakes to achieve positive textural properties such as uniform pore formation and increased volume with increased antioxidant properties.

• Journal of the Korea Convergence Society
• /
• v.10 no.5
• /
• pp.27-34
• /
• 2019

In this paper, we propose a method to remove the background area by analyzing the pattern of the character area. In the character detection result of the MSER(Maximally Stable External Regions) method which distinguishes a region having a constant contrast background regions were detected. To solve this problem, we use the MSER method in natural images, the background is removed by calculating the change rate by searching the character area and the background area which are not different from the areas where the contrast values are different from each other. However, in the background removed image, using the LBP(Local Binary Patterns) method, the area with uniform values in the image was determined to be a character area and character detection was performed. Experiments were carried out with simple images with backgrounds, images with frontal characters, and images with slanted images. The proposed method has a high detection rate of 1.73% compared with the conventional MSER and MSER + LBP method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.3
• /
• pp.523-528
• /
• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.

• Journal of Korea Multimedia Society
• /
• v.18 no.10
• /
• pp.1197-1204
• /
• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

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