Local Thresholding Approach Research Articles

Integrating Sentinel 2 Imagery with High-Resolution Elevation Data for Automated Inundation Monitoring in Vegetated Floodplain Wetlands

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.

Image segmentation and analysis for densification mapping of nanoporous gold after nanoindentation

Segmentation of scanning electron microscopy (SEM) images of focused ion beam (FIB) cross-sections through indented regions in nanoporous gold (np-Au) is carried out. A key challenge for image analysis of open porous materials is the appropriate binarization of the pore and gold ligament regions while excluding material lying below the cross-sectional plane. Here, a manual approach to thresholding is compared to global and local approaches. The global thresholding resulted in excessive deviations from the nominal solid fraction, due to a strong gray-scale gradient caused by the tilt angle during imaging and material shadowing. In contrast, the local thresholding approach delivered local solid fractions that were free of global gradients, and delivered a quality comparable to the manual segmentation. The extracted densification profiles vertically below the indenter as well as parallel to the surface showed an exponential-type decay from the indenter tip towards the nominal value of 1 far from the indenter.Graphic abstract

A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery

Emergency response to floods requires timely information on water extents, which can be produced by satellite-based remote sensing. As the synthetic aperture radar (SAR) can emit and receive signal in nighttime or cloudy conditions, it is particularly suitable to delineate water extent during flood events. Thresholding SAR imagery is one of the most widely used approaches to delineate water extent for its effectiveness and efficiency. However, most thresholding methods rely on a single threshold to separate water and land without considering the complexity and variability of different land surface types in an image. To account for the heterogeneous surface characteristics, this paper proposes a new local thresholding method to water delineation with SAR images. Specifically, our method follows four major steps. First, a global threshold is applied to the SAR imagery to delineate initial water pixels, from which non-water pixels are further clustered into several land surface types. This divides the SAR imagery into one water cluster and several land clusters. Second, local thresholds are estimated at each subset of land cluster paired with water cluster by fitting Gamma distributions to the backscatter intensities of the combined water/land pixels in each subset. Third, local water extents are delineated from each subset and then merged as the union of all subsets. The results are combined across multiple polarizations by taking an intersection operation to generate the global inundation extent. Finally, the flood water extent is further improved by imposing basic hydrologic constraints. This approach is fast and fully automated for flood detection. Our experiments using Sentinel-1 SAR imagery show that the proposed local thresholding approach could distinguish water from non-water with significantly higher accuracy (4–13% improvement in the harmonic mean of user’s and producer’s accuracy of water) than conventional global-thresholding methods.

Butt welding joints recognition and location identification by using local thresholding

Abstract Manual detection and identification of butt weld joints through welding image in real-time by human observation is subjective in nature, requires experience and can be biased at times. Furthermore, since that most of the welding robots application are programmed by teach and play means that they need to be reprogrammed each time they deal with new task. This is time consuming, plus welding parameters also need to be refined for every new program. Hence, this research aims to tackle the aforementioned issues by suggesting an alternative method that can automatically recognize and locate the butt welding position at starting, middle, auxiliary and end point under three scenarios which are; (1) straight, (2) saw tooth and (3) curve joint. This is done without any prior knowledge of shapes involved. A new approach known as local thresholding is proposed in the segmentation process which consists of image pre-processing, noise reduction and edge region points generation for butt welding joint identification. Region points for butt welding seam path are selected by shape selection generated from contour points according to the three scenarios. Each point is located by 2D coordinates that is usually used by robot controller for path planning. Experimental results showed that the local thresholding approach has managed to fulfill the research objective in detecting, identifying and locating the butt welding joint position in the three different scenarios. When compared with other methods such as background subtraction, local thresholding narrowly loses out in terms of less mismatch error produced. However, it gives the best results in the detection of the butt welding joint edges. Besides having the advantage of the ability to perform identification without prior knowledge from an image, local thresholding also showed that it can work just fine even in the presence of imperfections such as scratches on the surface of mild steel.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier.

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

Motion Objects Segmentation and Shadow Suppressing without Background Learning

An approach to segmenting motion objects and suppressing shadows without background learning has been developed. Since wavelet transformation indicates the position of sharper variation, it is adopted to extract the information contents with the most meaningful features based on two successive video frames only. According to the fact that the saturation component is lower in the region of shadow and is independent of the brightness, HSV color space is selected to extract foreground motion region and suppress shadow instead of other color models. A local adaptive thresholding approach is proposed to extract initial binary motion masks based on the results of the wavelet transformation. A foreground reclassification is developed to get an optimal segmentation by fusion of mode filtering, connectivity analysis, and spatial-temporal correlation. Comparative studies with some investigated methods have indicated the superior performance of the proposal in extracting motion objects and suppressing shadows from cluttered contents with dynamic scene variation and crowded environments.

Segmentation on the Dental Periapical X-Ray Images for Osteoporosis Screening

Segmentation on the trabecular of dental periapical X-Ray images is very important for osteoporosis screening. Existing methods do not perform well in segmenting the trabecular of dental periapical in X-Ray images due to the presence of large amount of spurious edges. This paper presents a combination of tophat-bothat filtering, histogram equalization contrasting and local adaptive thresholding approach for automatic segmentation of dental periapical in X-Ray images. The qualitative evaluation is done by a dentist and shows that the proposed segmentation algorithm performed well the porous of trabecular features of dental periapical. The quantitative evaluation used fuzzy classification based on neural network to classify these features. It were found accuracy rate to be 99,96% for training set and around 65% for testing set for a dataset of 60 subjects.

Optimal Parameter Selection Technique for a Neural Network Based Local Thresholding Method

Abstract Thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this paper, selection of optimal parameters for Neural Network (NN) based local thresholding approach for grey scale composite document image with non-uniform background is proposed. NN-based local image thresholding technique uses 8 statistical and textural image features to obtain a feature vector for each pixel from a window of size (2n + 1)x(2n + 1), where n ≥ 1. An exhaustive search was conducted on these features and found pixel value, mean and entropy are the optimal features at window size 3x3. To validate these 3 features some non-uniform watermarked document images with known binary document images called base documents are used. Characters were extracted from these watermarked documents using the proposed 3 features. The difference between the thresholded document and base document is the noise. A quantitative measure Peak-Signal-to-Noise ratio (PSNR) is used to measure the noise. In case of unknown base document characters were extracted through the proposed 3 features and used in a commercial OCR to obtain the character recognition rate. The average recognition rate 99.25% and PSNR shows that the proposed 3 features are the optimal compare to the NN-based thresholding technique with different parameters presented in the literature.

Adaptive thresholding of tomograms by projection distance minimization

Segmentation is an important step to obtain quantitative information from tomographic data sets. However, it is usually not possible to obtain an accurate segmentation based on a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold. Selecting the best local thresholds is not a straightforward task, as local image features often do not provide sufficient information for choosing a proper threshold. Recently, the concept of projection distance was proposed by the authors as a new criterion for evaluating the quality of a tomogram segmentation [K.J. Batenburg, J. Sijbers, Automatic threshold selection for tomogram segmentation by reprojection of the reconstructed image, in: Computer Analysis of Images and Patterns, in: Lecture Notes in Computer Science, vol. 4673, Springer, Berlin/Heidelberg, 2007, pp. 563–570.]. In this paper, we describe how projection distance minimization (PDM) can be used to select local thresholds, based on the available projection data from which the tomogram was initially computed. The results of several experiments are presented in which our local thresholding approach is compared with alternative thresholding methods. These results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate compared to previously published methods, in particular when the initial reconstruction contains artifacts.

An objective method for determining principal time scales of coherent eddy structures using orthonormal wavelets

A new, parameter-free method, based on orthonormal wavelet expansions is proposed for calculating the principal time scale of coherent structures in atmospheric surface layer measurements. These organized events play an important role in the exchange of heat, mass, and momentum between the land and the atmosphere. This global technique decomposes the energy contribution at each scale into organized and random eddy motion. The method is demonstrated on vertical wind velocity measurements above bare and vegetated surfaces. It is found to give nearly identical results to a local thresholding approach developed for signal de-noising that assigns the wavelet coefficients to organized and random motion. The effect of applying anti- and/or near-symmetrical wavelet basis functions is also investigated.