Texture Segmentation Research Articles

A review on the wavelet methods for sonar image segmentation

The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results.

In this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals' age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches's t-test and Kolmogorov-Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

ObjectivesTo investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).MethodsThis retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra- and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant.ResultsFour proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326).ConclusionThis study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.Key Points• CT-based texture features of intra- and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC.• CT imaging traits correlate with protein abundance in patients with HGSOC.

Abandoned Farmland Location in Areas Affected by Rapid Urbanization Using Textural Characterization of High Resolution Aerial Imagery

Several studies have demonstrated that farmland abandonment occurs not only in rural areas, but is also closely interlinked with urbanization processes. Therefore, the location of abandoned land and the registration of the spatial information referring to it play important roles in urban land management. However, mapping abandoned land or land in the process of abandonment is not an easy task because the limits between the different land uses are not clear and precise. It is therefore necessary to develop methods that allow estimating and mapping this type of land as accurately as possible. As an alternative to other geomatics methods such as satellite remote sensing, our approach proposes a framework for automatically locating abandoned farmland in urban landscapes using the textural characterization and segmentation of aerial imagery. Using the city of Poznań (Poland) as a case study, results demonstrated the feasibility of applying our approach, reducing processing time and workforce resources. Specifically and by comparing the results obtained with the data provided by CORINE Land Cover, 2275 ha (40.3%) of arable land within the city limits were abandoned, and the area of abandoned arable land was almost 9.2% of the city’s area. Finally, the reliability of the proposed methodology was assessed from two different focuses: (i) the accuracy of the segmentation results (from a positional point of view) and (ii) the efficiency of locating abandoned land (as a specific type of land use) in urban areas particularly affected by rapid urbanization.

A Comprehensive Horizon‐Picking Method on Subbottom Profiles by Combining Envelope, Phase Attributes, and Texture Analysis

AbstractWe propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking. This method overcomes the shortcomings of the traditional methods in envelope and phase automatic horizon picking in terms of accuracy and continuity, and in efficiency when it comes to manual picking. Under the constraint of the envelope threshold from the envelope image and the texture segmentation from the phase image, the accurate envelope horizons are picked from the envelope image. Through mean filtering and template enhancement, the fine phase horizons are picked from the phase image. Finally, by combining the two kinds of picked horizons, more continuous, accurate, and finer horizons are obtained. The proposed method was tested on a real data set.

Plaid Detectors in Macaque V1 Revealed by Two-Photon Calcium Imaging.

Neuronal responses to one-dimensional orientations are combined to represent two-dimensional composite patterns; this plays a key role in intermediate-level vision such as texture segmentation. However, where and how the visual cortex starts to represent composite patterns, such as a plaid consisting of two superimposing gratings of different orientations, remains neurophysiologically elusive. Psychophysical and modeling evidence has suggested the existence of early neural mechanisms specialized in plaid detection [1-6], but the responses of V1 neurons to an optimally orientated grating are actually suppressed by a superimposing grating of different orientation (i.e., cross-orientation inhibition) [7, 8]. Would some other V1 neurons be plaid detectors? Here, we used two-photon calcium imaging [9] to compare the responses of V1 superficial-layer neurons to gratings and plaids in awake macaques. We found that many non-orientation-tuned neurons responded weakly to gratings but strongly to plaids, often with plaid orientation selectivity and cross-angle selectivity. In comparison, most (∼94%) orientation-tuned neurons showed more or less cross-orientation inhibition, regardless of the relative stimulus contrasts. Only a small portion (∼8%) of them showed plaid facilitation at off-peak orientations. These results suggest separate subpopulations of plaid and grating responding neurons. Because most of these plaid neurons (∼95%) were insensitive to motion direction, they were plaid pattern detectors, not plaid motion detectors.

A semi-supervised deep learning approach for circular hole detection on composite parts

This paper introduces the usage of semi-supervised learning to obtain competitive detection accuracy of measuring drilled holes on composite parts with very limited noisy training data. An improved texture segmentation algorithm based on local binary patterns algorithm is proposed, named local exponential patterns. The algorithm divides the image texture into nine levels, of which the highest level of texture is selected for contour extraction. An ellipse fitting method is used to fit the target contours and vote for the candidate ellipses. The regions inside the candidate ellipses are taken as the semi-supervised semantic label for images. A new loss named round loss is proposed, and a superior circle segmentation model was trained by learning from incompletely annotated data. To verify the effectiveness of the method, experiments were conducted with the drilled holes on the composite parts. The results show that the proposed semi-supervised deep learning approach is exceedingly suitable for circle detection of holes with different texture information commonly found in robotic drilling. Massive data labeling can be completely avoided with proposed method. The measurement accuracy can reach 0.03 mm, which can meet the visual measurement requirements of the circular holes on composite parts in the robotic drilling system.

Image Texture Detection and Extraction based on Entropy and Eigenvectors of growing seeds

Texture determination, segmentation and extraction is always a field of research and development at image processing and computer vision science. Since there are no clear and general definitions of texture, all known is that texture depends on local pixels gray levels with respect to their spatial position. There has been quite some efforts put on this topic but nearly all of the presented techniques are either very complicated and requiring too much computations, not covering all textures presented in scene or need supervision and databases for training. At this paper a new approach is presented which is completely unsupervised, rapid and adoptable to increase efficiency and accuracy. It also needs no training sequences or databases and can be utilized to identify objects as texture components. The presented approach acquires texture identification features with respect to image areas entropies and basic neighborhoods eigenvectors. After re-defining these parameters for all possible pixel divisions at the image segments with optimal size with respect to the image geometries, certain or random points are chosen as texture seeds and grown up until new additions vary the segment’s entropy dramatically. After this the set of largest grown and separately located areas are introduced in sequence as discovered texture segments. Furthermore different variations of this method can be developed for different functionality properties.

Markov Random Field Based Color Texture Segmentation

Texture segmentation is one of the popular research domains and researchers across the globe are working on texture segmentation to enhance segmentation performance to address its requirements in many fields. Color texture segmentation has wide spectrum of applications in diverse fields such as segmentation of natural images, medical image analysis, remote sensing, shape extraction and inspection of products etc. This paper presents color texture segmentation algorithm which can satisfy requirements for such applications. Proposed algorithm is based on Markov Random Field (MRF) model eliminating the need of major contributor viz. Gabor filter used in past four decades for feature extraction and use only color as texture feature. Highly crude segmentation results are produced using only color as texture features. Crude segmentation results are enhanced by using Median filter with enlarged window size quantitatively determined by using parameters viz. structural similarity index (SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR). Feature space dimensions are reduced by factor of 11 in proposed approach and this reduced computations by a factor of 11. The experimentation is carried out on 80 multi-class color texture benchmark images from Prague texture segmentation dataset and 4 benchmark images in Vistex dataset. Mean segmentation accuracy achieved for Prague texture dataset is 87.55% and it is higher by 9.82% over the best performing algorithm among 11 state-of-art algorithms suggested in most recent literature. Accuracy achieved for Vistex dataset is 98.21%. Average SSIM for Prague dataset is 0.91403 and Vistex dataset is 0.9405.

Circular Gabor wavelet algorithm for fingerprint liveness detection

Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.  Â

Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

Detection And Classification of Weft Knitted Fabrics Defects Using Gabor Wavelet

The globalization of competition, the complexity of the economy and the plethora of information available today place companiesin a more than shifting context with which they must cope. Accelerating change becomes a constant feature of business life. Companies need methodological help to process a lot of information, In today's competitive world, customers are demandingbetter quality products with fast and reliable deliveries. To meet this demand, new manufacturing technologies are developingrapidly, resulting in new products and improvements in manufacturing processes.Today’s challenging world demands minimum loss and waste from industries. Moreover, it has to ensure the required quantityand quality with customer delivery lead time. A Circular weft knitting machine contains different parts such as needles, cams,sinkers, Fabric takedown mechanism, creel, a yarn metering and storage device, yarn breakage indicator, feeders and lubricationsystem. All those machine parts are responsible to increase or decrease the productivity of weft knit fabric production as well asthe fabric quality. The Circular weft Knitting Machine has to stop when defects occurred and then faults are corrected, whichresults in a loss in time and efficiency in order to be ready to meet customer requirements; the goal is to quickly provide productsthat combine quality and competitive price. In this sense, effective monitoring is required to avoid defects and maintain highproductivity and customer required quality. The purpose of this study is to identify and analyze weft knitted fabric defects onthe weft circular knitting machine of knitting industries.This paper describes a computer vision-based fabric inspection system implemented on a weft circular knitting machine to detectdefection and classification of the weft-knitted fabric defects under construction. We using Gabor Wavelets that have been successfully applied to various machine vision applications such as Texture segmentation, Edge detection, and Boundary detection, a multi-scale and multi orientation Gabor filter scheme simulates the human eye and that’s applied to the weft-knittedfabric under construction. On-line weft knitted fabric defect detection was tested automatically by analyzing fabric imagescaptured by a digital camera using Gabor wavelets and classification (Identification) these fabric defects to known classes. Wesucceeded to detect weft knitted fabric defects and classify defect’s on the weft circular knitting machine at the same time themachine stops to correct the fabric defect to achieve customer required quantity and quality. As well as we cancel the fabricinspection process that means, saving money, time, manpower which leads to reducing production lead time and cost.

Prediction of metastasis in oral squamous cell carcinoma through phenotypic evaluation and gene expression of E-cadherin, β-catenin, matrix metalloproteinase-2, and matrix metalloproteinase-9 biomarkers with clinical correlation.

CONTEXT:Controversies prevail regarding the true predictive role of epithelial–mesenchymal transition (EMT) biomarkers in metastasis of oral squamous cell carcinoma (OSCC). There is also limited research carried on till date wherein the protein and gene expression of EMT biomarkers have been investigated simultaneously in the Indian population.AIM:The aim of this study was to assess the gene expression and quantitative protein expression of EMT biomarkers using conventional method and MATLAB software and to determine if there is any difference in the expression between metastatic and nonmetastatic OSCCs with clinicopathologic correlation.SETTINGS AND DESIGN:Twenty metastatic and nonmetastatic OSCC tissue sections each were obtained from department archives. Gene expression and quantified protein expression of EMT markers were done and correlated with clinical parameters.SUBJECTS AND METHODS:Sections immunostained for EMT biomarkers were evaluated using semi-quantitative and quantitative (MATLAB) methods. Gene expression using semi-quantitative reverse transcriptase–polymerase chain reaction was done. These findings were correlated with clinical parameters.STATISTICAL ANALYSIS USED:Pearson's Chi-square test, Student's t-test, and univariate logistic regression analysis were performed using SPSS software.RESULTS:The low immunoexpression of E-cadherin and β-catenin and the high expression of matrix metalloproteinase (MMP)-2 and MMP-9 correlate with Stages III and IV showing high metastatic risk. Furthermore, the upregulated MMP-2 and MMP-9 gene expressions in advanced clinical stages of OSCC have high metastatic potential.CONCLUSIONS:This study is the first of its kind to employ texture and color segmentation in MATLAB to objectively assess the protein expression of EMT biomarkers. This research is instrumental in studying the protein and gene expressions of EMT markers with clinical correlation.

Novel morphological multi-scale evaluation system for quality assessment of decellularized liver scaffolds

Decellularized scaffolds can serve as an excellent three-dimensional environment for cell repopulation. They maintain tissue-specific microarchitecture of extracellular matrix proteins with important spatial cues for cell adhesion, migration, growth, and differentiation. However, criteria for quality assessment of the three-dimensional structure of decellularized scaffolds are rather fragmented, usually study-specific, and mostly semi-quantitative. Thus, we aimed to develop a robust structural assessment system for decellularized porcine liver scaffolds. Five scaffolds of different quality were used to establish the new evaluation system. We combined conventional semi-quantitative scoring criteria with a quantitative scaffold evaluation based on automated image analysis. For the quantitation, we developed a specific open source software tool (ScaffAn) applying algorithms designed for texture analysis, segmentation, and skeletonization. ScaffAn calculates selected parameters characterizing structural features of porcine liver scaffolds such as the sinusoidal network. After evaluating individual scaffolds, the total scores predicted scaffold interaction with cells in terms of cell adhesion. Higher scores corresponded to higher numbers of cells attached to the scaffolds. Moreover, our analysis revealed that the conventional system could not identify fine differences between good quality scaffolds while the additional use of ScaffAn allowed discrimination. This led us to the conclusion that only using the combined score resulted in the best discrimination between different quality scaffolds. Overall, our newly defined evaluation system has the potential to select the liver scaffolds most suitable for recellularization, and can represent a step toward better success in liver tissue engineering.

Weakly-Supervised Sparse Coding With Geometric Prior for Interactive Texture Segmentation

Texture segmentation is about dividing a texture-dominant image into multiple homogeneous texture regions. The existing unsupervised approaches for texture segmentation are annotation-free but often yield unsatisfactory results. In contrast, supervised approaches such as deep learning may have better performance but require a large amount of annotated data. In this letter, we propose a user-interactive approach to win the trade-off between unsupervised approaches and supervised deep approaches. Our approach requires the user to mark one pixel in each texture region, whose label is directly propagated to its neighbor region. Such labeled data are of very small amount and even partially erroneous. To effectively exploit such weakly-labeled data, we construct a weakly-supervised sparse coding model that jointly conducts feature learning and segmentation. In addition, the geometric constraints are developed for the model to exploit the geometric prior on the local connectivity of region boundaries. The experiments on two benchmark datasets have validated the effectiveness of the proposed approach.

DETECTOR QUASI-PERIODIC TEXTURE SEGMENTATION METHOD FOR DERMATOLOGICAL IMAGES PROCESSING

Currently,digital diagnosis systems that process medical images are widely used in the diagnosis processin the field of healthcare. The purpose of such systems is to assist the doctorin establishing the diagnosis, or in monitoring changes in the patient's condition during treatment. Dermatology is one of the areas of medicine where the number of visits to a doctoris high. At the same time, the tasks of establishing a diagnosis and monitoring changes in the patient's condition during treatment are time-consuming and subjective and they depend on knowledge and experience of a dermatologist. However, today, digital systems for the diagnosis of dermatological diseases are not in every locality, expensive and are stationary systems. With the development ofmobile information technologies, it became possible to develop mobile image processing systems for the analysis of dermatological diseases, which allow you to: receiving, analyze, and compareimages before and after treatment at anytime, anywhere.One of the basic procedures in image processing systems is segmentation, the purpose of which is to reduce the amount of processed data. Segmentation methods can be classified asboundary-based methods and region-based methods. Dermatological disease images consistof regions which have difference by texture, that is, the segmentation problem is considered as the task of selection homogeneous regions by texture. The result of image processing depends on the quality of segmentation. To improve the qualityof segmentation, in this work, we developed a detector quasi-periodic texture segmentation method for dermatological images processing,which contain quasi-periodic textures on a complex background in noisy conditions. This method is developed on the basis of the methodology of texture segmentation using detector, the stages of which are localization of spatial frequencies, detection, and contoursegmentation. To localize of spatial frequencies, a wavelet-function improved by transform of graph of power function was used, which increases the accuracy of determining the boundaries of quasi-periodic textures contained in dermatological disease images.On the detection step,the comb filters, which are wavelets with a periodic or quasi-periodic transfer function that are applied to each image line, wereused. The Canny method was use, as a contour preparation. Detectorsegmentation methods are focused on the image model. Therefore, a mathematical model of medical dermatological disease images was proposed, which contain quasi-periodic textures on a complex background in noisy conditions, as a model of a texture image with amplitude-modulated fluctuations in the intensity values by a random change in the amplitude and frequency of the oscillation. The developed method was applied to test medical images of psoriasis disease, which are available on the Internet. The accuracy of the segmentation of medical images of psoriasis disease containing a quasi-periodic texture was evaluated using the proposed method and the method using Gabor filters.It is shown that the proposed method is characterized by high speed and high segmentation quality, that is, it can be used in the development of express-diagnostic systems for monitoring changes in the patient's condition during treatment andto determine a parameter such as lesionsarea.

Imaging sedimentary basins from high-resolution aeromagnetics and texture analysis

Image texture is an important property of gridded aeromagnetic data that together with the availability of closer survey line-spacings and more sensitive instrumentation, has become more relevant for qualitative interpretations. Problems arise though when it is used as a basis for geological interpretation, since visual textures are difficult to define. This can introduce bias when interpretations are not validated against repeatable and testable quantitative measures. We propose the use of well-established unsupervised texture segmentation techniques that use multi-scale, orientation-selective Gabor filters to automatically domain contrasting textures in aeromagnetic maps. These methods are applied to tilt-derivative reduced-to-pole magnetics over the Palaeoproterozoic Earaheedy Basin, that was deposited over the northeastern Yilgarn Craton in a marginal setting prior to the ca. 1800 Ma Capricorn Orogeny Western Australia. These results are then validated against a geophysically constrained interpretation of the area and a review of the magnetic characteristics of the region. Our results demonstrate how automatic texture domaining from gridded magnetic-field data can produce geologically feasible results that can be used to constrain the architecture of basins that contain magnetic sedimentary rocks.

A Novel Motion Detection Method Using 3D Discrete Wavelet Transform

Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, an accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, burst physical motion, dynamic texture, and environmental changes such as those on climate changes, sunlight changes during a day, etc. In this paper, we propose a novel per-pixel motion descriptor for both motion detection and dynamic texture segmentation which outperforms the current methods in the literature particularly in severe scenarios. The proposed descriptor is based on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and three-dimensional wavelet leader. In this approach, a feature vector is extracted for each pixel by applying a novel three dimensional wavelet-based motion descriptor. Then, the extracted features are clustered by a clustering method such as well-known k-means algorithm or Gaussian Mixture Model (GMM). The experimental results demonstrate the effectiveness of our proposed method compared to the other motion detection approaches from the literature. The application of the proposed method and additional experimental results for the different datasets are available at (http://dspl.ce.sharif.edu/motiondetector.html).

Hierarchical coastline detection in SAR images based on spectral‐textural features and global–local information

This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline detection, and refinement. A novel spectral–textural segmentation framework (STSF) is proposed by using the spectral–textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.