Robust Local Binary Pattern Research Articles

Video Enhancement to Reduce Reflection and Darkness Caused by the Sunglasses using Fusion Algorithm

The enhancement of video of a person wearing sunglasses to reduce the reflection and darkness is a very challenging task in computer vision applications such as video surveillance. The existence of reflections and darkness caused by sunglasses results in intrusive images. The absence of a clear eye diminishes the visible quality of the complete face image. The eyes under sunglasses are not identified clearly if the reflection and darkness exist in the sunglasses. This paper demonstrates the reduction of adverse artifacts such as reflection and darkness in the eye region caused by sunglasses. The system is carried out with the fusion algorithm which consists of three modules: eyeglass tracking on face images, reduction of reflection, and reduction of darkness through the image enhancement method. The image enhancement method includes a color balance algorithm and a histogram stretching algorithm. Firstly, an automatic glasses presence detection model, based on a Robust Local Binary Pattern identifies the imaging process of the ocular region covered by the sunglasses. Secondly, a non-convex optimization scheme, guided by landmarks on the glasses, effectively reduces reflections through several iterations. The image enhancement method incorporating Color Balance, and Histogram Stretching is used to identify eye regions within sunglasses. The resulting regenerated eye regions within sunglasses exhibit increased brightness, subtle darkness, and minimized reflection. The objective evaluation metrics such as peak signal-to-noise ratio, structural similarity index measure, and logarithmic mean square error are used to measure the strength of the proposed system. Qualitative evaluations are conducted to demonstrate the good quality of eyeglass face images with reduced reflection and darkness.

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Line X Tester for Enhanced Vitamin A and E in Sorghum Sorghum Bicolor L. Genotypes.

In this paper, we presented a method for representing facial expressions by extracting texture features from Completed Robust Local Binary Pattern (CRLBP). Completed Robust Local Binary Pattern, which is resistant to noise and changes in lighting conditions, is used to generate consistent texture features. These features are then combined to create a feature vector that represents facial expressions. The suggested approach is assessed by conducting facial expression recognition using a standardized database like JAFFE. The process of identifying facial expressions is carried out by using a chi-square distance measure along with a nearest-neighbor classifier. The findings from our experiment demonstrate that our method is more effective than other widely used LBP approaches.

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most preferred screening technique, generally uses low-contrast images where identification of some of the mass lesions from surrounding normal tissues is a difficult task even for skilled radiologists. Therefore, to alleviate the issue, this paper introduces an effective computer-aided detection scheme based on a novel contrast enhancement scheme for mammograms to automate the localization procedure of mass cases. Hyperbolic tangent function, a simple yet effective method, is introduced as an enhancement transfer function whose adjustable parameter is optimized by the Tunicate swarm algorithm, a nature-inspired optimization algorithm, via fitness function. To minimize the false positives generated after the detection, the median robust extended local binary pattern, a texture-based descriptor, is introduced, which possesses the ability to capture both micro- and macro-structure information from an image patch. The analysis includes four classifiers: Fisher linear discriminant analysis, K-nearest neighbor, support vector machine, and an ensemble classifier in the feature-based classification for false positive reduction (FPR). The proposed approach of automatic detection of masses when verified using two standard databases, mini-MIAS and Digital Database for Screening Mammography, comprised 68 and 550 mammograms, respectively, achieved sensitivities of 95.3% and 94.1% with 0.36 and 0.40 false positives per image after false positive reduction.

Robust local binary pattern for face recognition in different challenges

Robust local binary pattern for face recognition in different challenges

Image splicing detection using discriminative robust local binary pattern and support vector machine

PurposeThis paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image.Design/methodology/approachThe structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged.FindingsThe performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique.Originality/valueThe proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern (LTP) Features and Non-subsampled Shearlet Transform (NSST) Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and concatenate all of them with the 2-state LM parameters to describe the global features of the image. The various properties of NSST such as multiscale, localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image. In order to extract the dense local features, a new 3D-LTP is proposed where dimension reduction is performed via selection of ‘uniform’ patterns. The 3D-LTP is calculated from spatial RGB planes of the input image. The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too. Finally, a fused feature representation (NSSTds-3DLTP) is proposed using new global (NSSTds) and local (3D-LTP) features to enhance the discriminativeness of features. The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19, Aerial Image Dataset (AID) and PatternNet in terms of mean average precision (MAP), average normalized modified retrieval rank (ANMRR) and precision-recall (P-R) graph. The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB, Granulometry, local binary pattern (LBP), Fisher vector (FV), vector of locally aggregated descriptors (VLAD) and median robust extended local binary pattern (MRELBP). For WHU-RS19 dataset, in terms of {MAP,ANMRR}, the NSSTds-3DLTP improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {41.93%,20.87%}, {92.30%,32.68%}, {86.14%,31.97%}, {18.18%,15.22%}, {8.96%,19.60%} and {15.60%,13.26%}, respectively. For AID, in terms of {MAP,ANMRR}, the NSSTds-3DLTP improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {152.60%,22.06%}, {226.65%,25.08%}, {185.03%,23.33%}, {80.06%,12.16%}, {50.58%,10.49%} and {62.34%,3.24%}, respectively. For PatternNet, the NSSTds-3DLTP respectively improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {32.79%, 10.34%}, {141.30%, 24.72%}, {17.47%,10.34%}, {83.20%,19.07%}, {21.56%,3.60%}, and {19.30%,0.48%} in terms of {MAP,ANMRR}. The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k-nearest neighbor classifier.

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%.

Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to Newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of Newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: For face recognition of newborns in a semi controlled environment. Overcoming the pose and illumination challenge. Uses single gallery image. Uses a hybrid approach to improve the results. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1.

Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor

• Usage of Shearlet coefficients is proposed to cancel illumination and noise variations. • Novel Gammadion Binary Pattern is proposed to extract out local micro level features. • GBPSC descriptor is devised to capture modality-invariant patterns of facial features. • GBPSC along with CNN outperforms other competing methods even with a small dataset. This paper presents a novel face image descriptor called Gammadion Binary Pattern of Shearlet Coefficients (GBPSC) for illumination and noise invariant, homogeneous and heterogeneous face recognition. Exploiting the energy concentration property of the Digital Shearlet Transform, an efficient illumination and noise invariant feature extractor has been devised. Finally, inspired by the Gammadion structure, a robust multi-directional local binary pattern named Gammadion Binary Pattern (GBP) has been proposed. GBP is applied on the previously extracted illumination and noise invariant feature map to generate the GBPSC images. Recognition results on Extended Yale B and TUFTS dataset indicate the primacy of the proposed scheme in terms of common feature representation under varying illumination, and modality. Furthermore, the merger of the proposed GBPSC and Convolutional Neural Network (CNN) consistently outperforms other state-of-the art methods.

Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier

The most common retinal diseases that are to be diagnosed are Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD) and Choroidal Neovascularization (CNV). For the people above 60 years of age, detection of these retinal diseases is an important task for treatment that reduces the risk of vision loss. Retinal fundus images play a significant role in the detection of DR, AMD and CNV disease diagnosis and treatment. The existing techniques for the detection of DR, AMD and CNV have not fulfilled with the classification accuracy of the retinal diseases effectively. This research work proposes an efficient classification framework for retinal fundus image recognition to overcome these drawbacks. Initially, the input image from the publicly available STARE database is preprocessed with the following three steps (a) Specular reflection removal and smoothing, (b) contrast enhancement and (c) retinal region expansion. With the preprocessed image, the features are extracted using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP), based on RGB component selection, Gradient operation, and LBP descriptor. Finally, classification was done using hybrid Convolution Neural Network (CNN) and Radial Basis Function (RBF) model (CNN-RBF) which classifies the retinal fundus images into four classes such as DR, AMD, CNV and Normal (NR). Experimental results of the proposed method gives an accuracy of 97.22% compared with the existing other methodologies.

A three-stage framework for smoky vehicle detection in traffic surveillance videos

The smoky vehicle exhaust pollutes the air and endangers human health. Existing methods detecting smoky vehicles in traffic surveillance videos are with high false alarm rates due to the diversity of smoke characteristics and continuous interferences of common vehicles. To solve this issue, this paper presents a three-stage framework for smoky vehicle detection. In the first stage, a Robust Pixel Based Adaptive Segmenter (R-PBAS) algorithm, which adapts to cameras shaking, is proposed to extract moving objects. The Cumulative Color Histogram (CCH) is adopted to extract smoke-colored blocks from moving objects. In the second stage, three groups of features, including Non-Redundant Robust Local Binary Pattern (NR-RLBP), Weighted Co-occurrence Histograms of Oriented Gradients (W-CoHOG), and Motion Boundary Histograms (MBH) are proposed to extract texture, gradient, and motion information from smoke-colored blocks, respectively. In the third stage, we fuse smoke blocks to obtain Region of Interest (ROI) and extract frequency domain features based on Discrete Wavelet Transform (DWT). To further improve robustness, the Auto-Regressive and Moving Average (ARMA) Model and Hidden Markov Model (HMM) are adopted to model ROIs in consecutive frames. Extensive experiments show that our method performs better than existing methods.

Local Binary Pattern Based Iris Recognition Model

Iris is a unique biometric tool, secure and reliable in recognizing an individual based on the texture information of human physiology. The Local Binary Pattern method uses descriptors based on histograms of Local Binary Pattern. In developed algorithm, Local Binary Pattern (LBP) histograms of iris images are extracted and concatenated into single enhanced histogram. It can be computed by nearest neighbor classifier and iris recognition is performed using Hamming distance as dissimilarity measure. We have conducted experimentation on CASIA dataset. From the experimental results, it is proved that the Robust LBP technique for iris recognition is more accurate than the conventional LBP.

Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering

The comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy, which produces unreliable results. To best address these disadvantages, an automatic comparison method for fractal anti-counterfeit labels is proposed. The method can effectively extract the color features, texture features, and shape features of anti-counterfeit labels, and perform cluster analysis and comparison. First, a color volume histogram is used to extract the color and pixel space information features from the fractal anti-counterfeit labels. To compensate for the deficiency of using a single feature, texture and shape features were also extracted based on the median robust extended local binary patterns (MRELBP) and Hu moments. Next, based on feature extraction, k-means clustering is performed to ensure that as many of the same types of labels as possible can be divided into the same clusters and that the comparison can only be performed on one or a few clusters. The experimental results show that the speed and quality of fractal anti-counterfeit label comparison effectively improved after the clustering analysis. Furthermore, based on visual identification and unreliable comparison results, the proposed method is expected to help consumers quickly identify low-quality, counterfeit products.

Separating real-world photos from computer graphics: comparative study of classification algorithms

Abstract The growing quality of computer graphics makes it difficult to classify real photos from rendered images. At the same time, a high-quality classifier that separates real-world photos from the images created by computer graphics allow solving many applied problems: from automatic evaluation of the quality of the image to detecting manually modified photos. The main directions of research in this area are associated with the identification of distinctive features in the images for use in classification models. In this paper, we provide experimental studies of various classification models and groups of features on the image dataset from Vkontakte social network. We show that the best choice from a variety of features for solving the classification problem is the combination of Haralick features, Bag of Visual Words (BoVW) with Speeded-Up Robust Features (SURF) descriptors and Local Binary Pattern (LBP) histograms. In addition, we show that Gradient Boosting model being trained on the specified combination of feature groups outperform other machine learning models with 90% accuracy.

Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification

In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with wavelet transform will capture more discriminating patterns and thus can help in characterizing masses. In order to evaluate the efficiency of the proposed approach, several experiments are carried out using the mini-MIAS database where a 5-fold cross validation technique is incorporated with Support Vector Machine (SVM) on the optimal set of features obtained via stepwise logistic regression method. An area under the receiver operating characteristic (ROC) curve ([Formula: see text] value) of 0.96 is achieved with DRlbp attributes as the best performance. The superiority of the proposed scheme is established by comparing the obtained results with recently developed other competing schemes.

Edge–texture feature-based image forgery detection with cross-dataset evaluation

A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs Discriminative robust local binary patterns (DRLBP) for encoding tampering traces and a support vector machine (SVM) for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.

Smoke Vehicle Detection Based on Spatiotemporal Bag-Of-Features and Professional Convolutional Neural Network

Existing smoke vehicle detection methods are vulnerable to false alarms. To solve this issue, this paper presents two automatic smoke vehicle detection methods based on spatiotemporal bag-of-features (S-BoF) and professional convolutional neural network (P-CNN). In the first method, we propose the S-BoF model to characterize the key regions detected by the visual background extractor (ViBe) algorithm. The S-BoF model contains three groups of features, including color moments on three orthogonal planes (CM-TOP), completed robust local binary pattern on three orthogonal planes (CRLBP-TOP), and histogram of oriented gradient on three orthogonal planes (HOG-TOP). The extracted features are fed to the support vector machine (SVM) and classify the key regions to smoke regions or non-smoke regions to further detect smoke vehicles. In the second method, we propose the P-CNN model to extract more robust and complementary spatiotemporal features by designing three professional models to analyze different kinds of features in the key region sequence on three orthogonal planes. The three professional models, including color CNN (CCNN), texture CNN (TCNN), and gradient CNN (GCNN), are based on three independent CNN128 models with different inputs. The experimental results show that the proposed methods achieve higher detection rates and lower false alarm rates than existing smoke detection methods.

Automatic Depression Analysis Using Dynamic Facial Appearance Descriptor and Dirichlet Process Fisher Encoding

Depression causes mood disorders with noticeable problems in day-to-day activities. Current methods of assessing depression depend almost entirely on clinical interviews or questionnaires. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of a psychological disorder. To help clinicians effectively and efficiently diagnose depression severity, automated systems, using objective and quantifiable data for depression assessment, are being developed. This paper presents a framework toward estimating a clinical depression-specific score, namely the Beck Depression Inventory–II (BDI-II) score, based on the analysis of facial expressions features. To extract facial dynamic features, we propose a novel dynamic feature descriptor denoted as median robust local binary patterns from three orthogonal planes (MRLBP-TOP), which can capture both the microstructure and macrostructure of facial appearance and dynamics. To aggregate the MRLBP-TOP over an image sequence, we propose a variant to the Fisher vector (FV) encoding scheme, denoted as the Dirichlet process FV (DPFV). DPFV adopts Dirichlet process Gaussian mixture models (DPGMM) to automatically learn the number of GMM mixtures and model parameters. Experimental results on the AVEC2013 and AVEC2014 depression databases have demonstrated the effectiveness of the proposed method.