Local Statistical Information Research Articles

Improved region-based active contour segmentation through divergence and convolution techniques

<p>In this paper, we present a novel approach to improve the robustness of region-based active contour models for image segmentation, particularly in the presence of noise. Traditional active contour methods often struggle with noise sensitivity and intensity variations within the image. To overcome these limitations, we propose an enhanced segmentation model that integrates the average convolution with entropy-based mean gray level values. Our method leverages the local statistical information by introducing a local similarity factor and local region relative entropy to build a robust energy functional. This energy functional balances the intensity differences between neighboring pixels and regions within the local window, while reducing the impact of noise. By incorporating convolution and entropy into the energy formulation, our model distinguishes between the interior and exterior regions of an image more effectively, thus leading to more accurate segmentation results. We demonstrate the numerical implementation of the proposed model, along with its convexity properties, to ensure stability and reliability. The experimental results show that our method significantly improves the segmentation performance, even in challenging scenarios with varying noise levels. This advancement has the potential to improve image analyses in fields such as medical imaging, object detection, and texture classification.</p>

Detection of Deepfake Videos Using Long-Distance Attention.

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video content and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this article, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and interframe inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces from a global perspective, respectively. The two components are designed using a novel long-distance attention mechanism. One component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves state-of-the-art performance, and the proposed long-distance attention method can effectively capture pivotal parts for face forgery.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

Robust Multimodal Remote Sensing Image Registration Based on Local Statistical Frequency Information

Multimodal remote sensing image registration is a prerequisite for comprehensive application of remote sensing image data. However, inconsistent imaging environment and conditions often lead to obvious geometric deformations and significant contrast differences between multimodal remote sensing images, which makes the common feature extraction extremely difficult, resulting in their registration still being a challenging task. To address this issue, a robust local statistics-based registration framework is proposed, and the constructed descriptors are invariant to contrast changes and geometric transformations induced by imaging conditions. Firstly, maximum phase congruency of local frequency information is performed by optimizing the control parameters. Then, salient feature points are located according to the phase congruency response map. Subsequently, the geometric and contrast invariant descriptors are constructed based on a joint local frequency information map that combines Log-Gabor filter responses over multiple scales and orientations. Finally, image matching is achieved by finding the corresponding descriptors; image registration is further completed by calculating the transformation between the corresponding feature points. The proposed registration framework was evaluated on four different multimodal image datasets with varying degrees of contrast differences and geometric deformations. Experimental results demonstrated that our method outperformed several state-of-the-art methods in terms of robustness and precision, confirming its effectiveness.

Fully Size-Adaptive Sliding Window Method for SAR Image Edge Detection

The local statistics are useful for the synthetic aperture radar (SAR) image processing. However, unreliable statistical results are produced due to insufficient sample size. To overcome the statistical limitation, the appropriate size of sliding window is vital in the image processing. Therefore, a completely size-adaptive sliding window method is proposed in this letter. A method to determine the size boundaries of the sliding window based on the basic size information is firstly proposed. Then, a strategy for changing the sliding window size is used to generate the sliding window size index matrix of the image. Among the many image processing applications that use sliding windows, we choose the SAR image edge detection arbitrarily to verify the effectiveness of the proposed method. And the experimental results indicate that our method makes the original edge detection algorithms improved significantly. Further, the proposed size-adaptive sliding window method has potential for many other image processing tasks that require the use of local statistical information.

Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern

As one of the most critical components in rotating machinery, it is essential to determine the health of rolling bearings on time. The effective feature extraction method is considered significant for fault diagnosis. In order to extract sufficient and effective bearing information, a novel extraction method called multiscale one-dimensional hybrid pattern (1D-HBP) is proposed for fault diagnosis of rolling bearings. The proposed method extracts the local and global texture statistical information of signals to reflect different bearing conditions. Considering the inherent multi-scale characteristics of the vibration signals, multiscale analysis is employed to obtain discriminative features of different scales. Two rolling bearings sets with changeable loads and rotating speeds verified the effectiveness and practicability of the proposed diagnostic model. Compared to other models examined for the same dataset our proposed model achieves a remarkably high classification accuracy.

Incorporating global multiplicative decomposition and local statistical information for brain tissue segmentation and bias field estimation

Medical image is a cornerstone of modern healthcare providing unique diagnostic. However, intensity nonuniformity is quite often found in medical images and unavoidably poses many challenges for precise image segmentation. In this paper, we present an incorporating global multiplicative decomposition and local statistical information level set technique for tissue segmentation and bias estimation that handles both high noise and intensity nonuniformity. An image can be factored into two multiplicative parts (namely, bias-free image and bias field), which are used to capture global intensity statistics. At the same time, our technique considers statistical information with not only the mean but also the variance. Pixel intensity generally fluctuates around its mean in a local region. The variance can adjust the disagreement of each pixel to its mean, and is explored to overcome the effects of intensity nonuniformity. Finally, the level set energy formulation is in the form of the global multiplicative decomposition and local statistical information. To verify the availability of our technique, we have thoroughly experimented on composite and real images. Our techniques are also applied to the lesion regions of brains. Experimental results display that our technique can segment different kinds of images with high noise and intensity nonuniformity more accurately, and outperforms other solutions in comparison.

Multi-layer and multi-order fine-grained feature learning for artwork attribute recognition

With the digital development of artwork, more and more artwork images are made available to the public. In iMet Collection Dataset which is part of the FGVC6 workshop at CVPR 2019, an artwork image is annotated by multiple labels. Because of the local and subtle differences between artwork images with different attribute labels, artwork attribute recognition can be considered as a fine-grained visual categorization (FGVC) task. In this paper, a multi-layer and multi-order network (MLMO-Net) is proposed to capture both first- and second-order information in the artwork images. First-order information can be used to characterize the global spatial information and second-order information can be used to characterize the local statistical information. Both of first- and second-order information from multiple layers are aggregated together in MLMO-Net. In our experiments, several convolutional neural network (CNN) architectures, such as Vgg16 and ResNet50, and recent FGVC methods, such as the bilinear CNN, hierarchical bilinear CNN, and Navigator–teacher–scrutinizer network (NTS-Net), are tested on the iMet Collection 2019 Dataset. Experimental results shown that MLMO-Net achieves improvements over baseline methods. Through the research of this paper, a direction to improve the performance of artwork attribute recognition could be provided.

An Information-Geometric Optimization Method for Ship Detection in SAR Images

This study aims to investigate the information-geometric optimization method for ship detection in synthetic aperture radar (SAR) images. The detection strategy relies on an optimization-based representation of images, which considers the relationship between local and global statistical information and is more robust to sea clutter. The algorithm has three main stages. First, a constant false alarm rate (CFAR) detector is used to accomplish the initial detection. It is designed to acquire the initial distribution parameters of pure sea clutter and ship targets. Second, a Riemannian metric is constructed according to the local statistical information. Its purpose is to design the energy function of the optimization model. Third, the detection problem is formulated as a maximum decision rule. The experimental results show that the model can effectively detect ships in SAR images, is not sensitive to clutter, and can eliminate the effect of inhomogeneous sea state conditions. Compared with similar algorithms, the proposed algorithm has higher detection precision and good stability in ship detection in SAR images.

A coarse-to-fine strategy for the registration of the multi-wavelength high-resolution solar images

The registration of multi-wavelength high-resolution solar images is an important task in the research of solar physics. This paper proposed a coarse-to-fine strategy to realize the accurate registration of high-resolution photospheric images and chromospheric images observed by the New Vacuum Solar Telescope (NVST) whose field-of-view is about 2′ ∼ 3′, and the spatial resolution can reach 0.1″ after image reconstruction. In this strategy, the full-disk solar images with relatively lower resolution taken by other space- or ground-based telescopes are taken as transition images, and the Fourier-Merlin transform, Template matching and a local statistical information based algorithm are used in combination. After registration, the geometric transformation between multi-wavelength images of NVST are corrected at the level of sub-arcseconds, including the rotation, scaling and translation relations. Two sets of data observed in active regions (i.e., the NOAA 11982 and the NOAA 12673) are used to illustrate our method step by step. The result shows that the registration accuracy can reach less than 1″. Moreover, this work also has facilitated the combination of high-resolution observations of NVST with the continuum, ultraviolet passbands and magnetic field observations of the Solar Dynamic Observation (SDO), which is highly beneficial to the multi-instrument joint measurement of solar activities.

Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition

When adding new tasks/classes in an incremental learning scenario, the previous recognition capabilities trained on the previous training data can be lost. In the real-life application of automatic target recognition (ATR), part of the previous samples may be able to be used. Most incremental learning methods have not considered how to save the previous key samples. In this article, the class boundary exemplar selection-based incremental learning (CBesIL) is proposed to save the previous recognition capabilities in the form of the class boundary exemplars. For exemplar selection, the class boundary selection method based on local geometrical and statistical information is proposed. And when adding new classes continually, a class-boundary-based data reconstruction method is introduced to update the exemplar set. Thus, when adding new classes, the previous class boundaries could be kept complete. Experimental results demonstrate that the proposed CBesIL outperforms the other state of the art on the accuracy of multiclass recognition and class-incremental recognition.

Segmentation of Pulmonary Nodules Using a GMM Fuzzy C-Means Algorithm

Segmentation of pulmonary nodule in thoracic computed tomography (CT) plays an important role in the computer-aided diagnosis (CAD) and clinical practices. However, segmentation of pulmonary nodules still remains a challenging task due to the presence of intrinsic noise, low contrast, intensity-profile inhomogeneity, variable sizes and shapes. Many variants and extensions of fuzzy C-mean (FCM) clustering algorithm have been developed to preserve image details as well as suppress image noises. However, these variants overemphasize the importance of the spatial information and neglect the role of the prior knowledge. To address this problem, a GMM fuzzy C-means (GMMFCM) algorithm is proposed for the segmentation of pulmonary nodules in this paper. A novel local similarity measure is defined by using local spatial information and GMM statistical information. A neighboring term is added to the energy function of traditional fuzzy C-mean algorithm. A superpixel-based random walker is proposed to segment pulmonary parenchyma, which reduces the computational complexity and improves the segmentation performance. Experiments performed on the LIDC dataset and the GHGZMCPLA dataset demonstrate that the segmentation performance of proposed GMMFCM algorithm is superior to the state-of-the-art algorithms.

Active Contours Driven by Local and Global Region-Based Information for Image Segmentation

Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises.

Enhanced Fuzzy-based Local Information Algorithm for Sonar Image Segmentation.

The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of sonar images, where the object's highlight and shadow are distinguished from the seabed background. In this work, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzybased with Kernel metric (EnFK) algorithm for the segmentation of sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of sonar images. We tested our approach using simulated images, real sonar images, and sonar images that we created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the stateof-the-art results.

Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes.

Fuzzy logic based computational model for speckle noise removal in ultrasound images

High level of uncertainty is always present due to impulsive noise in ultrasonic images, which may put negative effect on image interpretation, quantitative measurement and diagnostic purposes. In order to deal with this uncertainty, fuzzy modelling is being used which is very helpful in distinguishing noise from edges and other critical details present in the image. In this study, a novel fuzzy logic based non-local mean filter is proposed to model the speckle noise and to restore the degraded image using Fuzzy Uncertainty Modelling (FUM), smoothed by local statistic based information while preserving the image details for low and highly speckled ultrasound images. Proposed denoising technique acquires the local parameters to find distinct “similar and non-similar” non-local regions using FUM. These homogenous regions are first smoothed through local statistical information and then used to restore the selected noisy pixels using fuzzy logic based noise removal process. The study evaluates the performance of the proposed technique on different real and simulated data sets, and compares the numerical values with existing state of art filters using standard well known global quantitative measure like signal to noise ratio (SNR) and a local error measure – structural similarity index measure (SSIM). Visual and quantitative results demonstrate that the proposed technique outperforms the existing state of the art filters in removing speckle noise while preserving the edges and other important details present in the image.

Locally Statistical Dual-Mode Background Subtraction Approach

Due to the variety of background model in the real world, detecting changes in a video cannot be addressed exhaustively by a simple background subtraction method, especially with several motion detection challenges, such as dynamic background, camera jitter, intermittent object motion, and so on. In this paper, we propose an efficient background subtraction method, namely locally statistical dual-mode (LSD), for detecting moving objects in video-based surveillance systems. The method includes a local intensity pattern comparison algorithm for foreground segmentation by analyzing the homogeneity of intensity patterns of the input frame and the background model, in which the homogeneity is calculated by the mean and standard deviation of pixel intensity. Besides that, a dual-mode scheme is developed to temporally update the background model for the short- and long-term scenarios corresponding to sudden and gradual changes in the background. The advantage of this scheme is the allowance of updating the model in both pixel- and frame-wise manners simultaneously. The parameters used in both the local intensity pattern comparison algorithm and the dual-mode background model updating scheme are estimated for every input frame consecutively based on local and global statistical information of segmentation result. In experiments, the proposed LSD method is extensively evaluated on the Wallflower and CDnet2014 datasets; and remarkable performance demonstrates its preeminence to the many state-of-the-art background subtraction approaches in terms of segmentation accuracy and computational complexity.

An effective local regional model based on salient fitting for image segmentation

Abstract Intensity inhomogeneity often occurs in real-world images, and inevitably leads to many difficulties for accurate image segmentation. Although a lot of level set methods have been proposed to solve the problem of intensity inhomogeneity, they are often unavailable for some images with severe intensity inhomogeneity. In this paper, we propose a novel level set based segmentation model to effectively segment those images with severe intensity inhomogeneity, which is named as Local Salient Fitting (LSF) model. In LSF, we firstly transform original image into the new modality in which the object and background regions can be discriminated easily. Specially, we propose a weight factor based on local intensity variation to highlight the local region contrast of image. Meanwhile, the variation degree of local region is also computed to extract the distribution information of intensity variation. Then, since the new modality embodies the distribution information of intensity variation, we utilize the idea of fitting region distribution information to construct the salient fitting term. Finally, the salient fitting term is incorporated into the level set method to segment intensity inhomogeneous images. Furthermore, the combined effect of highlighted local region contrast and statistical distribution information of intensity variation will enhance the robustness of our method. Experiments conducted on synthetic and real images clearly demonstrate the efficiency and robustness of the proposed LSF model.

Interactive segmentation of texture image based on active contour model with local inverse difference moment feature

Texture image segmentation is a challenging problem in image processing field due to wide variability of characterizing textures and a lack of proper contour information. In this paper, an effective presentation is found with which different textures can be represented with some corresponding distributions generated by feature values of local inverse difference moment (LIDM). By the analysis of local statistical information in gray level co-occurrence matrix (GLCM), we found that similar textures can be characterized with similar distributions. In this way, an interactive segmentation method is presented to achieve the segmentation of texture image based on GLCM with an optimizing model. Our scheme can be narrated separately as follows, firstly, a proper Gaussian kernel is selected to discriminate two classes of textures by analyzing LIDM feature distributions, which are obtained from two different local regions marked manually in the initial texture image. Secondly, the LIDM feature map can be constructed by computing LIDM feature values of image patches with the proper Gaussian kernel, and the center of these image patches may traverse the whole image domain. Finally, the texture image segmentation is implemented based on an improved optimizing model with local binary fitting and local extremum regularizing. In order to validate the performance of our proposed method, two kinds of experiments about discriminative feature map construction and texture image segmentation are carried out to demonstrate its well performance, and more experiments on real texture images are also conducted.

Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy.

Background and purposeIn radiation therapy, defining the precise borders of cancerous tissues and adjacent normal organs has a significant effect on the therapy outcome. Deformable models offer a unique and robust approach to medical image segmentation. The objective of this study was to investigate the reliability of segmenting organs-at-risk (OARs) using three well-known local region-based level-set techniques.Methods and materialsA total of 1340 non-enhanced and enhanced planning computed tomography (CT) slices of eight OARs (the bladder, rectum, kidney, clavicle, humeral head, femoral head, spinal cord, and lung) were segmented by using local region-based active contour, local Chan-Vese, and local Gaussian distribution models. Quantitative metrics, namely Hausdorff Distance (HD), Mean Absolute Distance (MAD), Dice coefficient (DC), Percentage Volume Difference (PVD) and Absolute Volumetric Difference (AVD), were adopted to measure the correspondence between detected contours and the manual references drawn by experts.ResultsThe results showed the feasibility of using local region-based active contour methods for defining six of the OARs (the bladder, kidney, clavicle, humeral head, spinal cord, and lung) when adequate intensity information is available. While the most accurate results were achieved for lung (DC = 0.94) and humeral head (DC = 0.92), a poor level of agreement (DC < 0.7) was obtained for both rectum and femur.ConclusionIncorporating local statistical information in level set methods yields to satisfactory results of OARs delineation when adequate intensity information exists between the organs. However, the complexity of adjacent organs and the lack of distinct boundaries would result in a considerable segmentation error.