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 ...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.展开更多
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 experim...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.展开更多
This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><...This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 98.05% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 95% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px respectively. A facial deflection of about 30</span></span></span><span><span><span><span><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">°</span></span><span> 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.</span></span></span></span>展开更多
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 establis...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.展开更多
Local Binary Patterns (LBPs) have been highly used in texture classification <span style="font-family:Verdana;">for their robustness, their ease of implementation an</span><span style="fo...Local Binary Patterns (LBPs) have been highly used in texture classification <span style="font-family:Verdana;">for their robustness, their ease of implementation an</span><span style="font-family:Verdana;">d their low computational</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysi</span><span style="font-family:Verdana;">s withou</span><span style="font-family:Verdana;">t any assumption.</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">from 375 nm to 940 nm. In all achieved experimentations, our propos</span><span style="font-family:Verdana;">al presents the best classification scores compared to common multicomponent LBP methods.</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.81%, 100.00%,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.07% and 97.67% are</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.</span>展开更多
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...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.展开更多
Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applicatio...Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.展开更多
Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve ...Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.展开更多
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...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.展开更多
文摘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.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61573057)the National Science and Technology Supporting Project(Grant No.2015BAF08B01)
文摘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.
文摘This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 98.05% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 95% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px respectively. A facial deflection of about 30</span></span></span><span><span><span><span><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">°</span></span><span> 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.</span></span></span></span>
基金Project(61172047)supported by the National Natural Science Foundation of China
文摘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.
文摘Local Binary Patterns (LBPs) have been highly used in texture classification <span style="font-family:Verdana;">for their robustness, their ease of implementation an</span><span style="font-family:Verdana;">d their low computational</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysi</span><span style="font-family:Verdana;">s withou</span><span style="font-family:Verdana;">t any assumption.</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">from 375 nm to 940 nm. In all achieved experimentations, our propos</span><span style="font-family:Verdana;">al presents the best classification scores compared to common multicomponent LBP methods.</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.81%, 100.00%,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.07% and 97.67% are</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.</span>
文摘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.
文摘Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.
基金This research received funding from Duhok Polytechnic University.
文摘Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.
文摘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.