LOCALIZED PATTERNS OF THE CUBIC-QUINTIC SWIFT-HOHENBERG EQUATIONS WITH TWO SYMMETRY-BREAKING TERMS

Homoclinic snake always refers to the branches of homoclinic orbits near a heteroclinic cycle connecting a hyperbolic or non-hyperbolic equilibrium and a periodic orbit in a reversible variational system. In this paper, the normal form of a Swift-Hohenberg equation with two different symmetry-breaking terms （non-reversible term and non-k-symmetry term） are investigated by using multiple scale method, and their bifurcation diagrams are initially studied by numerical simulations. Typically, we predict numerically the existence of so- called round-snakes and round-isolas upon particular two symmetric-breaking perturbations.

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.

Discrete Plane Segmentation and Estimation from a Point Cloud Using Local Geometric Patterns

This paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discretegeometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. In contrast to the continuous case, there exist a finite number of local geometric patterns （LGPs） appearing on discrete planes. Moreover, such an LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate the values of parameters of a discrete plane by minimizing its thickness.

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.

Changes of local agricultural landscape patterns induced by land consolidation in hilly and mountainous Southwestern China

Land consolidation (LC), as a type of human disturbance, improves land production efficiency and changes landscape distribution through land parcel reallocation. The objective of this study was to comparatively analyze the changes of landscape patches before and after a land consolidation project (LCP) and the effects of land levelling, irrigation and drainage work and road engineering on the landscape structure. FRAGSTAT3.3 and buffer zone analysis were used to investigate those changes. The results suggest that the heterogeneity of landscape depressed, and tended to simplification after LC. Dry land was the most highly variable land use pattern, and the change of forestland was least due to its locations at a gradient larger than 25°. LC resulted in a more rational use of land, and could be an important step in promoting rural development in depressed and fragmented agricultural areas through unused land exploitation, small-patch combination, irrigation and water conservancy, and road construction. Land levelling leveled off the gradient field surface and decreased the slope. The fragmentized patches were much more incorporated with increasing slope. On the other hand, the ridge of a field became longer so that the length of field surface and area of patch were increased. Land levelling regulated, simplified and combined patches, so that the complexity degree was reduced. It is found that the buffer distance of 35 m was a turning point of human disturbance by irrigation and drainage systems, and patches presented flaky distribution when the buffer distance was smaller than 35 m. Meanwhile, the distance range between 25 m to 50 m was an impressible area for road engineering, which was sensitive to human actions, and the changes of all landscape metrics were larger than those in other buffer zones. In general, LC not only reallocated fragmented parcels, but also improved agricultural conditions.

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

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 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.

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

Local Flow Regime Transition Criteria of Gas-Liquid Two-phase Flow in Vertical Upward Tube with a Horizontal Rod

The upward multiphase cross flow and heat transfer in the vertical tube may occur in oil production and chemical facilities. In this study, the local flow patterns of an upward gas-water two phase cross flow in a vertical tube with a horizontal rod have been investigated with an optical probe and the digital high speed video system. The local flow patterns are defined as the bubble, slug, churn and annular flow patterns. Optical probe signals are ana- lyzed in terms of probability density function, and it is proved that the local flow patterns can be recognized by this method. The transition mechanisms between the different flow patterns have been analyzed and the corresponding transitional models are proposed. Finally, local flow pattern maps of the upward gas-water two-phase flow in the vertical tube with a horizontal rod are constructed.

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.

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.

Classification of traditional Chinese medicine constitution based on facial features in color images

Objective:To explore the possible correlation between traditional Chinese medicine(TCM)constitution and facial features in color images and to improve the accuracy of automated constitution classification.Methods:Color images were taken of 5150 individuals of different professions.Automated face detection and key point positioning were performed on the collected images,which were then transformed into a standard size.The relationship between facial features and TCM constitution based on the red,green,blue(RGB)pixel and the local binary pattern(LBP)texture features was explored.Results:The overall accuracy rate and robustness of TCM constitution classification based on RGB features were low.Classification results of the phlegm-dampness,damp-heat,blood stasis,and balance constitutions achieved high accuracy rates.Classification accuracy rate using the LBP texture feature was higher than that of the RGB feature,with the best accuracy observed for the balance constitution.Conclusion:Application of computer image acquisition and processing of facial features may serve as an adjunct to the TCM diagnostic method of inspection.

Facial Expression Recognition Based on the Q-shift DT-CWT and Rotation Invariant LBP

In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-CWT can provide a group delay of 1/4 of a sample period, and satisfy the usual 2-band filter bank constraints of no aliasing and perfect reconstruction. To resolve illumination variation in expression verification, low-frequency coefficients produced by DT-CWT are set zeroes, high-frequency coefficients are used for reconstructing the image, and basic LBP histogram is mapped on the reconstructed image by means of histogram specification. LBP is capable of encoding texture and shape information of the preprocessed images. The histogram graphs built from multi-scale rotation invariant LBPs are combined to serve as feature for further recognition. Template matching is adopted to classify facial expressions for its simplicity. The experimental results show that the proposed approach has good performance in efficiency and accuracy.

Face Recognition on Partial and Holistic LBP Features

An algorithm for face description and recognition based on multi-resolution with multi-scale local binary pattern （multi-LBP） features is proposed. The facial image pyramid is constructed and each facial image is divided into various regions from which partial and holistic local binary patter （LBP） histograms are extracted. All LBP features of each image are concatenated to a single LBP eigenvector with different resolutions. The dimensionaUty of LBP features is then reduced by a local margin alignment （LMA） algorithm based on manifold, which can preserve the between-class variance. Support vector machine （SVM） is applied to classify facial images. Extensive experiments on ORL and CMU face databases clearly show the superiority of the proposed scheme over some existed algorithms, especially on the robustness of the method against different facial expressions and postures of the subjects.

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

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.

An Efficient Acoustic Scattering Model Based on Target Surface Statistical Descriptors for Synthetic Aperture Sonar Systems

Acoustic scattering as the perturbation of an incident acoustic field from an arbitrary object is a critical part of the targetrecognition process in synthetic aperture sonar(SAS)systems.The complexity of scattering models strongly depends on the size and structure of the scattered surface.In accurate scattering models including numerical models,the computational cost significantly increases with the object complexity.In this paper,an efficient model is proposed to calculate the acoustic scattering from underwater objects with less computational cost and time compared with numerical models,especially in 3D space.The proposed model,called texture element method(TEM),uses statistical and structural information of the target surface texture by employing non-uniform elements described with local binary pattern(LBP)descriptors by solving the Helmholtz integral equation.The proposed model is compared with two other well-known models,one numerical and other analytical,and the results show excellent agreement between them while the proposed model requires fewer elements.This demonstrates the ability of the proposed model to work with arbitrary targets in different SAS systems with better computational time and cost,enabling the proposed model to be applied in real environment.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.