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About 3,851 papers for “Local binary patterns”

Local Similarities for Boosting the Performance of Local Binary Patterns Technique

Abdelhamid Abdesselam · International Journal of Signal Processing Systems · 2016

Cited by 1

CNN with local binary patterns for hyperspectral images classification

Xiangpo Wei, Xuchu YU, Pengqiang Zhang et al. · National Remote Sensing Bulletin · 2020

Cited by 7Open access

FACE RECOGNITION WITH SALIENT LOCAL GRADIENT ORIENTATION BINARY PATTERNS

Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2009

Cited by 0

Local Binary Patterns applied to Face Detection and Recognition

Sánchez López, Laura · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2010

Nowadays, applications in the field of surveillance, banking and multimedia equipment are\nbecoming more important, but since each application related to face analysis demands\ndifferent requirements on the analysis process, almost all algorithms and approaches for face\nanalysis are application dependent and a standardization or generalization is quite difficult.\nFor that reason and since many key problems are still not completely solved, the face analysis\nresearch community is still trying to cope with face detection and recognition challenges.\nAlthough emulating human vision system would be the ideal solution, it is a heuristic and\ncomplicated approach which takes into account multiple clues such as textures, color, motion\nand even audio information. Therefore, and due to the fast evolution of technology that makes\nit possible, the recent trend is moving towards multimodal analysis combining multiple\napproaches to converge to more accurate and satisfactory results.\nContributions to specific face detection and recognition applications are helpful to enable the\nface analysis research community to continue building more robust systems by concatenating\ndifferent approaches and combining them. Therefore, the aim of this research is to contribute\nby exploring the Local Binary Patterns operator, motivated by the following reasons. On one\nhand, it can be applied to face detection and recognition and on the other hand due to its\nrobustness to pose and illumination changes.\nLocal Binary Patterns were first used in order to describe ordinary textures and, since a face\ncan be seen as a composition of micro textures depending on the local situation, it is also\nuseful for face description. The LBP descriptor consists of a global texture and a local texture\nrepresentation calculated by dividing the image into blocks and computing the texture\nhistogram for each one. The global is used for discriminating the most non-face objects\n(blocks), whereas the second provides specific and detailed face information which can be\nused not only to select faces, but also to provide face information for recognition.\nThe results will be concatenated in a general descriptor vector, that will be later used to feed\nan adequate classifier or discriminative scheme to decide the face likeness of the input image\nor the identity of the input face in case of face recognition. It is in that stage where this\nresearch will focus, first evaluating more simple classification methods and then trying to\nimprove face detection and recognition ratios by trying to eliminate features vector\nredundancy.

Cited by 0Open access

Introduction to local binary patterns : new variants and applications

Brahnam, Sheryl, Jain, Lakhmi, Lumini, Alessandra et al. · University of Canberra Research Portal · 2014

Cited by 0

Dominant Rotated Local Binary Patterns (DRLBP) for texture classification

Rakesh Mehta, Karen Egiazarian · Pattern Recognition Letters · 2015

Cited by 186

Learning and recognizing texture characteristics using local binary patterns

University of Oulu Repository (University of Oulu) · 2007

Cited by 0

Trabecular Bone Anisotropy Characterization Using 1D Local Binary Patterns

Lotfi Houam, Adel Hafiane, Rachid Jennane et al. · Lecture notes in computer science · 2010

Cited by 10

Robust texture classification by subsets of local binary patterns

M. Topi, O. Timo, P. Matti et al. · 2002

Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3/spl times/3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture description which can achieve better classification rates in comparison with the whole LBP histogram. Experiments on classification of textures from the Columbia-Utrecht (CURET) database demonstrate the robustness of the approach.

Cited by 212

Local binary patterns partitioning for rotation invariant texture classification

Navid Shadkam, Mohammad Sadegh Helfroush, Kamran Kazemi · 2012

Local binary pattern (LBP) is a well-defined operator and it has been widely used in texture description. By representing a local region with its center pixel and local difference vector, LBP just encodes the sign component of this difference vector. This paper presents an operator, which efficiently encodes the magnitude part of local difference, as a complementary to LBP. We combine the sign and magnitude component of image local difference vectors, by making the joint distribution of LBP and presented magnitude based features. It has been experimentally demonstrated that, considerable improvement can be made for rotation invariant texture classification, in comparison with recently proposed completed LBP (CLBP) method.

Cited by 2

Local Binary Patterns: Spatial Encoding to Deep Learning Fusion

Vandana Gupta, Ashish Mishra, Nishant Shrivastava · 2025

Local Binary Pattern (LBP) is a popular and important handcrafted method that is used to extract local structural information from images. It is very simple and fast in computation, which makes it suitable for texture classification tasks and for systems that need to work in real time or with limited resources. LBP and its variations are very useful in many computer vision applications such as image retrieval, object detection, medical image analysis, and face recognition. This work reviews the main developments in LBP-based methods with emphasis on the variants developed in the spatial domain. These versions were introduced to handle issues such as noise, changes in lighting, and the lack of rotation invariance. This study also shows that LBP-based methods continue to be effective, discriminative, and practical for real-world applications.

Cited by 0

Towards Understanding the Formation of Uniform Local Binary Patterns

Olli Lahdenoja, Jonne Poikonen, Mika Laiho · ISRN Machine Vision · 2013

The research reported in this paper focuses on the modeling of Local Binary Patterns (LBPs) and presents an a priori model where LBPs are considered as combinations of permutations. The aim is to increase the understanding of the mechanisms related to the formation of uniform LBPs. Uniform patterns are known to exhibit high discriminative capability; however, so far the reasons for this have not been fully explored. We report an observation that although the overall a priori probability of uniform LBPs is high, it is mostly due to the high probability of only certain classes of patterns, while the a priori probability of other patterns is very low. In order to examine this behavior, the relationship between the runs up and down test for randomness of permutations and the uniform LBPs was studied. Quantitative experiments were then carried out to show that the relative effect of uniform patterns to the LBP histogram is strengthened with deterministic data, in comparison with the i.i.d. model. This was verified by using an a priori model as well as through experiments with natural image data. It was further illustrated that specific uniform LBP codes can also provide responses to salient shapes, that is, to monotonically changing intensity functions and edges within the image microstructure.

Cited by 40Open access

FACIAL EXPRESSION EMOTION RECOGNITION USING LOCAL BINARY PATTERNS HISTOGRAMS

Mrs.M.K Nivodhini, Mr.B Rajesh · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024

Local Binary Patterns (LBP) histograms are used as texture descriptors by the proposed facial expression emotion detection system to identify various face expressions in photos. Localized facial patterns are represented by histograms formed by the extraction of LBP features based on pixel intensity comparisons after pre-processing operations such as picture normalization and scaling. Using a labeled emotion dataset, these histograms are used as feature vectors to train a machine learning model. The model's performance is evaluated using evaluation measures, such as accuracy, precision, recall, and F1 score. The system, which was developed with OpenCV and scikit-learn, shows a reliable and useful method for recognizing facial expressions that can be used in practical settings. Key Words: Facial Recognition, Deep Learning, Image Recognition

Cited by 0Open access

Face Identification Based On Local Binary Patterns: A Survey

Saini, Sumit, Abhishek Goel, Kumar, Manish et al. · Zenodo (CERN European Organization for Nuclear Research) · 2018

The goal of this research project was to come up with a combined face recognition algorithm which out-performs existing algorithms on accuracy. The identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, illuminations, makeup, rotations, and gestures. Facial images also contain a great deal of the redundant information, which negatively affects the performance of the recognition system. This paper proposes a novel approach for recognizing facial images from facial features using feature descriptors, namely local binary patterns (LBP) and local directional patterns (LDP). This research work consists of three parts, namely face representation, feature extraction and classification. The useful and unique features of the facial images have been extracted in the feature extraction phase. In the classification, the face image has been compared with the images from the database. The face area has been divided into small regions from which local binary and directional patterns (LBP/LDP) histograms have been extracted and concatenated into a single feature vector (histogram). Experiments performed on the Cohn-Kanade facial expression database have shown a good recognition rate 99% indicating superiority of the proposed method compared to other methods. The proposed methods includes a combination of local binary pattern(LBP) and local directional patterns(LDP+LBP+Voting Classifier) as the feature extractor and voting classifier classification algorithm which is an aggregate classifier composed of k-Nearest Neighbor, decision trees and support vector machines. The results reveal improved accuracy results as compared to other local binary pattern variants in both scenarios where small datasets or huge datasets are used.

Cited by 0Open access

Enhanced face recognition using Cross Local Radon Binary Patterns

Srinivasa Rao Chalamala, Santosh Kumar Jami, B. Yegnanarayana · 2015

In this paper we introduce a novel face representation method called Cross Local Binary Patterns(XLBP) to improve the robustness of face recognition for partially occluded and non-uniformly illuminated face images. In our method we use Radon transform to capture the coarse level shape information and XLBP to capture the texture information. Individual histograms computed on each sub-block of the face image and are concatenated in spatial pyramid fashion to get the complete face descriptor. Distance measures based on Pyramid Matching Kernel(PMK) is used to match these face descriptors. Experiments on FERET and YaleB databases show the significance of this method.

Cited by 6

3D Facial Expression Recognition on Curvature Local Binary Patterns

Yiding Wang, Meng Meng · 2013

This paper proposed a new method Based on curvature Based LBP feature to recognize 3D facial expression automatically. 3D facial expression images are described by means of four images which gray level are the value of curvature-Based descriptors (principal curvatures k1, k2, mean curvature, shape index) and then encoded by LBP. To efficiently optimize the performance, Chi-square distance is employed for classification. Finally, experimental result achieved on the Bosphorus database illustrates that the curvature-Based LBP (CLBP) has performs better than other features and also shows these features are significant for 3D facial expression recognition.

Cited by 6

Local Binary Patterns for Human Detection on Hexagonal Structure

Xiangjian He, Jianmin Li, Yan Chen et al. · 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 6

No reference image blurriness assessment with local binary patterns

Guanghui Yue, Chunping Hou, Ke Gu et al. · Journal of Visual Communication and Image Representation · 2017

Cited by 24

Using local binary patterns for object detection in images

Karel Petránek, Pavel Janečka, Jan Vaněk · Global Journal of Computer Sciences Theory and Research · 2015

The article discusses a texture operator called Local Binary Patterns (LBP) and its applications in image processing and object detection. We provide a description of the algorithm for computing LBP together with a rationale for using LBP as a feature for object detection and image recognition. Based on the algorithm we show that LBP features have a low computational overhead compared to more complicated image features such as the commonly used SIFT or SURF features or neural network based approaches because they exploit the use of extremely fast bitwise and integer operations of the CPU. We demonstrate that LBP is robust to changes in brightness, contrast, image rotation, image scale. We develop two enhancements for LBP that improve its resistance to camera noise and enhance the discriminative power of LBP when it is used as a feature for machine learning algorithms. We present the results on a challenging real-world object detection task.Â Keywords: computer vision, object detection, local binary patterns.

Cited by 0Open access

Advanced Local Binary Patterns for Remote Sensing Image Retrieval

Tekeste, Issayas, Demir, Begüm · DepositOnce · 2018

The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.

Cited by 0Open access