Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter

Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

Palmprint Recognition Based on Statistical Local Binary Orientation Code

A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation information is attained with a winner-take-all rule. Subsequently, the resulting orientation mapping array is operated by uniform local binary pattern. Accordingly, LBOCode image is achieved which contains palmprint orientation information in pixel level. Further we divide the LBOCode image into several equal-size and nonoverlapping regions, and extract the statistical code histogram from each region independently, which builds a global description of palmprint in regional level. In matching stage, the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms＇ dissimilarity, which brings the benefit of computational simplicity. Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain

Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.

Salient Local Binary Pattern for Ground-Based Cloud Classification

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain

The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.

Exploiting global and local features for image retrieval

Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad features.To address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision.The bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an image.The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method.The performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,respectively.Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

A deformation measurement method based on surface texture information of rocks and its application

Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock surfaces.The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability.Based on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks.The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone.Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks.The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.