Abdolaziz Haghnegahdar, Shahriar Kolahi, Leila Khojastepour et al. · SHILAP Revista de lepidopterología · 2018
Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.
Cited by 16Open access
Sorensen, L, Shaker, SB, de Bruijne, Marleen · EUR Research Repository (Erasmus University Rotterdam) · 2010
We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a κ nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to vert r\vert = 0.79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.
Cited by 3
Reza Davarzani, Khashayar Yaghmaie, Saeed Mozaffari et al. · Forensic Science International · 2013
Cited by 117
Yang Zhang, Taolun Chai, Chih‐Cheng Hung · 2010
In this paper, an integrated method of Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is used for face recognition under different variations with illumination, pose and facial expression. LBP is applied as a preprocessing step to generate the LBP image which is then analyzed on a block-by-block basis. Facial features are extracted from each block using the DCT algorithm. The objective is to explore the performance of LBP under different illumination, pose and expression variations. Face images in the Yale database and ORL database are used in our experiments. A comparison on the sensitivities of the LBP operator in illumination, pose and expression variations is provided.
Cited by 4
Hardeep Singh, Gagandeep Gagandeep · Zenodo (CERN European Organization for Nuclear Research) · 2022
<strong>Abstract:</strong> This paper focuses on the Local Binary Patterns and its application to facial analysis. LBP is a non-parametric descriptor and used to extract, analyze, recognize and classify the different modality images. It summarizes the local patterns of image characteristics efficiently. In image processing, we have to extract features from a set of different texture or facial images, the Local Binary Pattern is a descriptor to extract, analyze, recognize and classify that data. There are detection, face representation, face detection and face recognition processes needed to analyze a face, from which the data obtained of different faces are comparatively checked according a specific exact facial image of person. This technique is applied at biometric machine and at other purposes to recognize an authentic face image. This paper facial analysis process and different local binary pattern techniques applied for facial detection and recognition are extensively reviewed. <strong>Keywords:</strong> Local Binary Pattern, LBP, Face Detection, Recognition. <strong>Title:</strong> Local Binary Patterns and Its Application to Facial Analysis <strong>Author:</strong> Hardeep Singh, Gagandeep <strong>International Journal of Engineering Research and Reviews</strong> <strong>ISSN 2348-697X (Online)</strong> <strong>Vol. 10, Issue 3, July 2022 - September 2022</strong> <strong>Page No: 11-20</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 22-August-2022</strong> <strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.7014367</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/local-binary-patterns-and-its-application-to-facial-analysis</strong>
Cited by 1Open access
Markus Turtinen · 2007
Cited by 11
Hatice Koç, Seçil Süer, Sarp Ertrk · 2015
Change detection is studied in hyperspectral images. In this paper, hyperspectral image processing for change detection, which aims to detect the differences occurring between images acquired from the same scene at different times. Local Binary Similarity Pattern (LBSP) is used for this detection.
Cited by 0
Mr.T. Udhayakumar · IOSR Journal of Engineering · 2013
This paper presents a efficient facial image recognition based on multi scale local binary pattern (LBP) texture features .It's a fast and simple for implementation, has shown its superiority in face recognition. To extract representative features, "uniform" LBP was proposed and its effectiveness has been validated. However, all "non-uniform" patterns are clustered into one pattern, so a lot of useful information is lost. In this study, propose to build a hybrid multiscale LBP histogram for an image. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The useful information of "non-uniform" patterns at large scale is dug out from its counterpart of small scale, The performance of the proposed method is that it can fully utilize LBP information while it does not need any training step, which may be sensitive to training samples assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.
Cited by 1Open access
Devrim Akgün · U Porto Journal of Engineering · 2021
Advances in machine learning frameworks like PyTorch provides users with various machine learning algorithms together with general purpose operations. PyTorch framework provides Numpy like functions and makes it practical to use computational resources for accelerating computations. Also users may define their custom layers or operations for feature extraction algorithms based on the tensor operations. In this paper, Local Binary Patterns (LBP) which is one of the important feature extraction approaches in computer vision were realized using tensor operations of PyTorch framework. The algorithm was written both using Python code with standard libraries and tensor operations of PyTorch in Python. According to experimental measurements which were realized for various batches of images, the algorithm based on tensor operations considerably reduced the computation time and provides significant accelerations over Python implementation with standard libraries.
Cited by 0Open access
Rohit Verma, Mohamed-Yahia Dabbagh · 2013
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called Local Binary Patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.
Cited by 17
Xiaolei Liu, Runge Lu, Wei Liu · 2017
For face recognition systems, impostors can obtain legal identity authentication by presenting the printed images, the downloaded images or candid videos to the sensor. In this paper, an enhanced face local binary feature (ELBP) of a face map is extracted as a classification feature to identify whether the face map is a real face or a fake face. Compared with the dynamic or static methods proposed in the previous literature, the proposed method is convenient and effective. We achieved over 95% correct recognition rate on NUAA datasets. Through comprehensive analysis and comparison with other static-based algorithms such as M-LBP or LTP, the proposed algorithm can achieve better performances.
Cited by 14
Zhang, JW, Zhou, MG, Zhang, RP · Lanzhou University Institutional Repository · 2015
In this paper, a novel local descriptor, named bilateral local binary patterns (BLBP) is proposed for rotation invariant texture retrieval. The proposed BLBP is based on the Local Binary Pattern operator (LBP) of combining phase and module information of images. For phase information, we use a projecting filter firstly to obtain an analytic signal of the input image and so the obtained image has explicit phase information, then the LBP operator is applied on it in order to achieve a histogram of phase (P-b) to describe the local pattern of phase. For module information, we use LBP operator directly, then we can achieve a histogram of module (M-b) to describe the local pattern of pixel variation. We introduce a linear relation between the two histograms to achieve a new histogram as the feature vector of texture image. Experimental results obtained from two databases demonstrate that the proposed BLBP can achieve higher retrieval rate than only using LBP for original images while the computational complexity is lower than using the phase information with LBP in the complex wavelet domain for rotation invariant image retrieval.
Cited by 0
Sorensen, L, Shaker, SB, de Bruijne, Marleen · Data Archiving and Networked Services (DANS) · 2008
In this paper we propose to rise local binary patterns (LBP) as features in a classification framework for classifying different texture patterns in lung computed tomography. linage intensity is included by means of the joint LBP and intensity histogram, and classification is performed using the k nearest neighbor classifier with histogram similarity as distance measure. The proposed method is evaluated on a set of 168 regions of interest comprising normal tissue and different emphysema patterns, and compared to a, filter bank based on Gaussian derivatives. The joint LBP and intensity histogram, achieving a classification accuracy of 95.2%, shows superior performance to using the common approach of taking moments of the filter response histograms as features, and slightly better performance than using the full filter response histograms instead. Classification results are better than some of those previously reported in the literature.
Cited by 0
K. Meena, A. Suruliandi · 2011
Face recognition is one of the most important tasks in computer vision and Biometrics. Texture is an important spatial feature useful for identifying objects or regions of interest in an image. Texture based face recognition is widely used in many applications. LBP method is most successful for face recognition. It is based on characterizing the local image texture by local texture patterns. In this paper performance evaluation of Local Binary Pattern (LBP) and its modified models Multivariate Local Binary Pattern (MLBP), Center Symmetric Local Binary Pattern (CS-LBP) and Local Binary Pattern Variance (LBPV) are investigated. Facial features are extracted and compared using K nearest neighbour classification algorithm. G-statistics distance measure is used for classification. Experiments were conducted on JAFFE female, CMU-PIE and FRGC version 2 databases. The results shows that CS-LBP consistently performs much better than the remaining other models.
Cited by 61
Shrinivasa Naika C. L. · 2014
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Pedro Morgado, Margarida Silveira, Jorge S. Marques · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2013
In the last decade, the computerised diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) using the information provided by different neuroimaging techniques has been extensively studied. However, the texture of such neuroimages has been little explored. In this work, both diagnoses were conducted based solely on the texture of fluorodeoxyglucose-positron emission tomography (FDG-PET) images, which was extracted using a novel 3D extension of the well-known 2D texture descriptor local binary patterns (LBPs). In LBPs, the concepts of uniformity and rotation invariance are of fundamental importance. We show that the proposed approach, unlike other 3D extensions found in the literature, closely replicates these concepts, as originally proposed in the 2D setting. Experimental results showed that the new 3D LBP version is able to enhance the generalisation ability of the diagnostic system and also that the texture of FDG-PET scans contains distinctive information about the presence of both AD and MCI.
Cited by 16
Ashirbani Saha, Q. M. Jonathan Wu · 2010
This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at a specific scale and orientation. Local binary patterns are extracted from the selected curvelet sub-bands to form the descriptive feature set of the expressions. The average of the features of a particular class of expression is considered as the representative feature set of that class. The expression recognition is performed using a nearest neighbor classifier with Chi-square as the dissimilarity metric. Experiments show that our method yields recognition rates of 93% and 90% in JAFFE and Cohn-Kanade databases respectively.
Cited by 27
Xiaohua Huang, Guoying Zhao, Wenming Zheng et al. · IEEE Signal Processing Letters · 2012
Feature representation is an important research topic in facial expression recognition from video sequences. In this letter, we propose to use spatiotemporal monogenic binary patterns to describe both appearance and motion information of the dynamic sequences. Firstly, we use monogenic signals analysis to extract the magnitude, the real picture and the imaginary picture of the orientation of each frame, since the magnitude can provide much appearance information and the orientation can provide complementary information. Secondly, the phase-quadrant encoding method and the local bit exclusive operator are utilized to encode the real and imaginary pictures from orientation in three orthogonal planes, and the local binary pattern operator is used to capture the texture and motion information from the magnitude through three orthogonal planes. Finally, both concatenation method and multiple kernel learning method are respectively exploited to handle the feature fusion. The experimental results on the Extended Cohn-Kanade and Oulu-CASIA facial expression databases demonstrate that the proposed methods perform better than the state-of-the-art methods, and are robust to illumination variations.
Cited by 64
He, S, Li, J, Chen, Y et al. · UTS ePRESS (University of Technology Sydney) · 2007
Local Binary Pattern (LBP) was designed and has been widely used for efficient texture classification. LBP provides a simple and effective way to represent texture patterns. Uniform LBPs play an important role for LBP-based pattern/object recognition as they include majority of LBPs. On the other hand, Human detection based on Mahalanobis Distance Map (MDM) recognizes appearance of human based on geometrical structure. Each MDM shows a clear texture pattern that can be classified using LBPs. In this paper, we compute LBPs of MDMs on a hexagonal structure. The circular pixel arrangement in hexagonal structure results in higher accuracy for LBP representation than on square structure. Chi-square as a measure is used for human detection based on uniform LBPs obtained. We show that our method using LBPs built on MDMs has a higher human detection rate and a lower false positive rate compared to the method merely based on MDMs. We will also show using experimental results that LBPs on hexagonal structure lead to more robust human classification.
Cited by 0
Min‐Ki Kim · Journal of the Korea Society of Computer and Information · 2010
본 논문은 물리적 제약이 없는 자연스러운 인터페이스에서 획득한 장문영상을 효과적으로 인식하는 방법을 제안한다. 손의 위치 이동이나 회전으로 인하여 손바닥 영상에서 관심영역의 위치나 방향이 다양하게 나타나므로, 장문인식을 위해서는 안정적인 관심영역 추출이 필요하다. 본 논문은 검지와 중지, 소지와 약지 사이의 손 가랑이 구간의 중심점을 기준으로 관심영역을 추출하는 방법을 제시하고, 국부 이진패턴 히스토그램을 이용한 장문인식 방법을 제안한다. 제안된 방법의 성능을 측정하기 위하여 100인으로부터 획득한 총 1,597개의 장문영상을 대상으로 실험을 수행하였다. 실험 결과 ROI 추출 성공률이 99.5%였고, 장문인식 성능을 보여주는 동일오류율과 결정계수 d'를 측정한 결과 각각 0.136, 3.539를 보였다. 이러한 결과는 제안된 방법이 손의 위치나 회전 변형에 강인함을 나타낸다. This paper proposes an efficient method for verifying palmprint which is captured at the natural interface without any physical restriction. The location and orientation of the region of interest (ROI) in palm images are variously appeared due to the translation and rotation of hand. Therefore, it is necessary to extract the ROI stably for palmprint recognition. This paper presents a method that can extract the ROI, which is based on the reference points that are located at the center of the crotch segments between index finger and middle finger and between ring finger and little finger. It also proposes a palmprint recognition method using the histogram of local binary patterns (LBP). Experiments for evaluating the performance of the proposed method were performed on 1,597 palmprint images acquired from 100 different persons. The experimental results showed that ROI was correctly extracted at the rate of 99.5% and the equal error rate (EER) and the decidability index d' indicating the performance of palmprint verification were 0.136 and 3.539, respectively. These results demonstrate that the proposed method is robust to the variations of the translation and rotation of hand.
Cited by 0Open access