Neighborhood Correlation Research Articles

Extended correlation functions for spatial analysis of multiplex imaging data.

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

An Exploration of Face Recognition Methods Based on LBP Algorithm and PCA Analysis

Local Binary Pattern is an operator that can be effectively used for facial description, and its application field is relatively wide, which has made great contribution to the research and technical support related to face recognition. The traditional local texture feature extraction method of LBP operator is relatively single and cannot fully synthesize the problem of neighborhood correlation. Therefore, this paper proposes a face recognition method based on the combination of LBP algorithm and PCA principal component analysis method for the above problems. Through the application of this method, it can effectively weaken the influence of noise on the results and show the basic characteristic situation of the sub-block region, and the experiments of this method have confirmed that, compared with the use of PCA, LBP and other methods alone, the method in this study has a significant improvement in the recognition rate.

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Accurately determining the focus plane of the reconstructed image is crucial for obtaining high-quality reconstructed images in the process of digital hologram reconstruction. In this paper, a focusing evaluation function based on S-th power weighted neighborhood correlation coefficient (SPWNCC) is proposed to realize automatic focusing of the reconstructed image in off-axis digital Fresnel holography. The Fresnel transform method is utilized as the off-axis digital holographic reconstruction algorithm. Both the numerical simulation and optical experiment results are given to verify the validity of the proposed autofocusing method. The obtained focusing curve can maintain good unimodality and noise immunity performance over a large search range (approximately 800 mm). The proposed SPWNCC based focusing evaluation function has a certain guiding significance on the automatic focusing of off axis digital holographic reconstructed images of long distance recorded objects.

Gene expression extraction in cervical cancer by segmentation of microarray images using a novel fuzzy method

ABSTRACT It is necessary to obtain gene expression values to identify gene biomarkers involved in all types of cancers, and microarray data is one of the best data for this purpose. In order to extract gene expression values from microarray images that have different challenges. This article presents a completely automatic and comprehensive method that can deal with the various challenges in these images and obtain gene expression values with high accuracy. A pre-processing approach is proposed for contrast enhancement using a genetic algorithm and for removing noise and artefacts in microarray cells using wavelet transform based on a complex Gaussian scaling model. For each point, the coordinate centre is determined using Self Organising Maps. Then, using a new hybrid model based on the Fuzzy Local Information Gaussian Mixture Model (FLIGMM), the position of each spot is accurately determined. In this model, various features are obtained using local information about pixels, considering the pixel neighbourhood correlation coefficient. Finally, the gene expression values are obtained. The performance of the proposed algorithm was evaluated using real microarray images of cervical cancer from the GMRCL microarray dataset as well as simulated images. The results show that the proposed algorithm achieves 90.91% and 98% accuracy in segmenting microarray spots for noiseless and noisy spots, respectively.

Multi-Focus Image Fusion for Full-Field Optical Angiography.

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations.

Neighborhood Correlation Enhancement Network for PCB Defect Classification

In recent years, deep-learning has been gradually applied to the detection and classification of true and pseudo defects in printed circuit boards (PCB). To judge the authenticity of PCB defects, it is necessary not only to combine the shape characteristics of the defects themselves, but also to add the relationship information between the surrounding environment and defects. However, it is difficult for general classification methods to extract such features. In this paper, we propose the neighborhood correlation enhancement network (NCE-Net), which effectively uses defect and surrounding relationship information to accurately distinguish defect authenticity. This network has a relevance residual block (RRB), which is used to establish correlation between defects and their surroundings, including the location enhancement and locate module (LEL) and relevance convolution (RC), which are respectively used to enhance the effective geographical information and extract the relationship between features of different positions. It also utilizes a small squeeze residual block (SRB) to classify pseudo defects more quickly and efficiently in industrial applications. In addition, to achieve the lowest pseudo defect detection error rate, we created a unique multi-network specific integration (SI) method for use with NCE-Net. The experimental results show that our proposed network can be trained on a PCB defect classification dataset (PCB-2-DET) for higher efficiency and more significant PCB defect detection. Additionally, the model’s identification accuracy can be further improved through our unique SI method.

SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms

The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods.

Grid-Based Essential Urban Land Use Classification: A Data and Model Driven Mapping Framework in Xiamen City

Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities.

The alternative irregularity reduction algorithm built on 2-stage identification with AMF on FMIO

Built on neighborhood correlation, a 2-stage identification technique was offered to incorporate on the irregularity reduction algorithm for random magnitude impulse outlier (RMIO). As a result, this algorithm has an ultimate efficacy thereby a 2-stage identification technique becomes to be one of the high efficacy identification technique. Accordingly, this paper attempts to propose an alternative irregularity reduction algorithm built on a 2-stage identification and adaptive median filter (AMF) with under fix magnitude impulsive outlier (FMIO) at little, mild and immense massiveness. First, by examining great number of depictions, the optimized window dimension for 2-stage scheme from computation and performance is disposed. Second, comprehensive examinations represent that the 2-stage identification technique is disposed to identify between regular and irregular pixels at all massiveness, especially little and mild massiveness. Third, the identification efficacy on great depictions at all massiveness is examined on regular, irregular, regular-irregular efficacy perspective to estimate the optimal window size and optimal 2-stage constant value. Finally, the overall outlier reduction efficacy of an outlier reduction built on 2-stage technique and AMF is examined on great depictions at all massiveness related with other up-to-the-minute outlier reductions. From these results, the outlier reduction has remarkable efficacy than other up-to-the-minute outlier reductions.

Intelligent data deduplication with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System

The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classification in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction; bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.

Geometric effects of volume-to-surface mapping of fMRI data

In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data that emerges in local connectivity analysis. We show that neighborhood correlations on the surface of the brain vary spatially with the gyral structure, even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose several measures to mitigate these unwanted effects.

Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images

An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images.

Non-Markovian temporal networks with auto- and cross-correlated link dynamics.

Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often observed to be non-Markovian, and the dynamics of their links are often correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other relevant quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key. In this article we introduce a general class of models of temporal networks based on discrete autoregressive processes for link dynamics. As a concrete and useful case study, we then concentrate on a specific model within this class, which allows to generate temporal networks with a specified underlying structural backbone, and with precise control over the dynamical dependencies between links and the strength and length of their memories. In this network model the presence of each link is influenced not only by its past activity, but also by the past activities of other links, as specified by a coupling matrix, which directly controls the causal relations, and hence the correlations, among links. We propose a maximum likelihood method for estimating the model's parameters from data, showing how the model allows a more realistic description of real-world temporal networks and also to predict their evolution. Due to the flexibility of maximum likelihood inference, we illustrate how to deal with heterogeneity and time-varying patterns, possibly including also nonstationary network dynamics. We then use our network model to investigate the role that, both the features of memory and the type of correlations in the dynamics of links have on the properties of processes occurring over a temporal network. Namely, we study the speed of a spreading process, as measured by the time it takes for diffusion to reach equilibrium. Through both numerical simulations and analytical results, we are able to separate the roles of autocorrelations and neighborhood correlations in link dynamics, showing that not only is the speed of diffusion nonmonotonically dependent on the memory length, but also that correlations among neighboring links help to speed up the spreading process, while autocorrelations slow it back down. Our results have implications in the study of opinion formation, the modeling of social networks, and the spreading of epidemics through mobile populations.

Low Complexity Encoder with Multilabel Classification and Image Captioning燤odel

Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images. At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image captioning techniques. To achieve this, this paper presents an efficient low complexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves the low complexity encoder using the Neighborhood Correlation Sequence (NCS) with a burrows wheeler transform (BWT) technique called LCE-BWT for encoding the RS images captured by the UAV. The application of NCS greatly reduces the computation complexity and requires fewer resources for image transmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presented to determine the multiple class labels of the RS image, shows the novelty of the work. Finally, the Computational Linguistics based Bidirectional Encoder Representations from Transformers (BERT) technique is applied for image captioning, to provide a proficient textual description of the RS image. The performance of the presented technique is tested using the UCM dataset. The simulation outcome implied that the presented model has obtained effective compression performance, reconstructed image quality, classification results, and image captioning outcome.

An effective risk identification method for power fence operation based on neighborhood correlation network and vector calculation

The power operation site typically faces high voltages, strong currents, and other environmental hazards. Safety fence as a boundary for power operation is of great importance to ensure the safety of operators. The preparation of operation fence is temporary and random, and the risk identification is a complex problem that involves a dynamic interaction between individuals and fence. The existing methods of determining the safety area based on prior knowledge are insufficient. Therefore, this paper proposes a risk identification method based on neighborhood correlation networks and vector calculation. In this method, firstly, the key targets of fence operation are detected by the target detection method. Then, considering that the fence operation target has the characteristics of flat shape and small size; the region proposal network by guided anchoring (GA-RPN) and neighborhood-related network module is introduced to improve the detection accuracy. Finally, the vector calculation criterion is used to judge whether the operator is in the safety fence area, to realize the risk identification of the operation fence overrun. Experiments are conducted on available power fence operation datasets, and the obtained results showed that the proposed method presents better performance than the classical target detection methods.

Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation.

Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor () algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix () between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.

SE 2 Net: semantic segmentation of remote sensing images based on self-attention and edge enhancement modules

The semantic segmentation of optical satellite remote sensing images is more challenging than that of natural images, owing to the considerable differences in the texture, shape, topology, and scale of ground features in different areas and the coexistence of dense and sparse arrangements. To alleviate these difficulties and ensure a high accuracy, a semantic segmentation framework named the self-attention and edge enhancement network (SE2Net) is constructed considering two aspects. First, because the self-attention mechanism can capture more useful semantic information by modeling large neighborhood correlations, we embed a self-attention module known as spatial expectation maximization attention (SEMA) in the considered network. Second, the Laplace operator is adopted to explore the significant edge information to design an edge enhancement module (EEM). Finally, both SEMA and EEM are embedded in the proposed SE2Net, thereby forming an end-to-end network. To validate the performance of the proposed approach, we construct a semantic segmentation dataset (SSD) using Tian-Hui 1 satellite images and conduct extensive experiments on both the SSD and the gaofen image dataset (GID). The results demonstrate the superiority of the proposed method over other state-of-the-art approaches and the effectiveness of the constructed SSD.

Virtual Restoration of Mural Color Based on Artificial Intelligence

In recent years, the development of artificial intelligence can be described as rapid change, which provides a safer method for the virtual restoration of mural colors. This article mainly studies the virtual restoration of mural color based on artificial intelligence. This paper constructs the minimum spanning tree of the segmentation line and transforms it into a graph, finds out the closed sub-ring as the segmentation line from the graph, and uses the depth-first search algorithm to extract the surface in the segmentation line area. This paper performs threshold segmentation on the mural image to obtain the drop-off area. The threshold T is selected based on experience; the pixel points of the same attribute are merged by 8 adjacent methods, that is, the pixel value is 1, and then the closed operation is used to achieve the purpose of continuous drop-off information. In order to control the tendency of global optimization and neighborhood correlation, a proportional parameter needs to be added, and the parameters are adjusted to obtain a balance between neighborhood correlation and global optimization, and the best matching point can be obtained. The data shows that the average objective evaluation score of Criminisi algorithm is 0.794603, and the average objective evaluation score of the improved algorithm in this paper is 0.848665. The results show that artificial intelligence technology plays an extremely important role in the virtual restoration of mural colors.

A Fuzzy C-Means Clustering Algorithm Based on Spatial Context Model for Image Segmentation

An improved Fuzzy C-Means (FCM) algorithm, which is called Reliability-based Spatial context Fuzzy C-Means (RSFCM), is proposed for image segmentation in this paper. Aiming to improve the robustness and accuracy of the clustering algorithm, RSFCM integrates neighborhood correlation model with the reliability measurement to describe the spatial relationship of the target. It can make up for the shortcomings of the known FCM algorithm which is sensitive to noise. Furthermore, RSFCM algorithm preserves details of the image by balancing the insensitivity of noise and the reduction of edge blur using a new fuzzy measure indicator. Experimental data consisting of a synthetic image, a brain Magnetic Resonance (MR) image, a remote sensing image, and a traffic sign image are used to test the algorithm’s performance. Compared with the traditional fuzzy C-means algorithm, RSFCM algorithm can effectively reduce noise interference, and has better robustness. In comparison with state-of-the-art fuzzy C-means algorithm, RSFCM algorithm could improve pixel separability, suppress heterogeneity of intra-class objects effectively, and it is more suitable for image segmentation.

The impact of the spatio-temporal neighborhood effect on urban eco-efficiency in China

Since urban eco-efficiency is known to be influenced by the contemporaneous efficiency level in the vicinity, one must allow for possible spatio-temporal correlations between such efficiencies. This article assessed the urban eco-efficiency of 282 prefecture-level cities in China from 2004 to 2016 using a super-efficiency model based on the non-radial directional distance function (Super-NDDF) and then conducted neighborhood correlation tests using the spatio-temporal Moran’s I index (STMI). After a variety of parameter identification and econometric tests, the spatio-temporal Durbin model (STDM) and partial differential method were used to investigate the impact of the spatio-temporal neighborhood effect on urban eco-efficiency. The results showed that compared with the traditional spatial econometric model, the spatial coefficient ρ of the STDM increased by 75%, indicating that the time lag and accumulation of neighborhood effects could not be ignored. After the setting of the spatio-temporal weight matrix in the time dimension was changed, the diminishing marginal neighborhood effect on urban eco-efficiency was found as the temporal window increases and the attenuation boundary appeared within approximately five years. Additionally, in contrast to the statistical insignificance of the neighborhood effect on the strict spatial dimension, environmental regulation had a significant positive impact on the eco-efficiency of neighboring cities when the neighborhood effect was considered in a spatio-temporal context, suggesting a time lag in the neighboring demonstration effect of environmental regulation. The obvious increase in the ratio of indirect effects to direct effects indicated that the spatio-temporal neighborhood effect exacerbated the inertia of neighborhood dependence evolution and strengthened the positive spatial externalities of industrial structure upgrading and technological progress.