Region Merging Research Articles

Experimental and numerical investigations into cold flow characteristics of multiple micro-mixing jets for hydrogen-rich gas turbines

To explore the flow field of micro-mixing jets, cold flow characteristics of a model Micromix burner were investigated by particle image velocimetry (PIV) system and Large-eddy simulation (LES) model. Results show that LES results are in good agreement with experimental results. In the flow field of multiple micro-mixing jets, the jet velocities of nozzles farther away from the burner center have a high increase and decay rate. When the outlet Reynolds number increases, the Reynolds stress increases first and then decreases in the merging region indicating that the velocity fluctuation disappears in the second jet half, but it has little effect on the flow field structure. Comparing the flow fields of round multiple micro-mixing jets, the merging point and combined point in the elliptical jets flow field move backward. Moreover, the maximum velocity for elliptical jets is also faster than the round jets, which is caused by the high turbulent kinetic energy in the elliptical jet flow field. When the tube spacing increases from 2 to 3 times the tube diameter, positions of the two feature points change linearly. Further, the surrounding jets can decrease the velocity attenuation of the center nozzle and elongate the axial length of the two feature regions.

A hybrid method for delineating homogeneous forest segments from LiDAR data in Catalonia (Spain)

ABSTRACT Laser scanning data are increasingly used to segment forests into homogeneous units, called segments or stand compartments. Region growing and region merging have been traditionally employed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising more homogeneous stand delineations. However, these newer methods often require fine-tuning due to rugged stand boundaries. To address this, we propose and assess a novel hybrid approach that combines two segmentation algorithms, resulting in smoother boundaries and homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques, particularly using CA or SOM for initial segmentation and then refining it with another cellular automaton (CA’), adjusted to produce smooth boundaries. The new two-step hybrid method resulted in better segmentation results than the one-step algorithm combined with previously suggested fine-tuning methods. SOM-CA’ showed the highest degree of explained variance (R2) of the LiDAR metrics used in the segmentation. CA-CA’ reached almost the same R2 values with a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion, the segments produced by the hybrid method outperformed the stand delineations of the current Spanish forest map with a clear margin.

Methods for constructing and evaluating consensus genomic interval sets.

The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.

Self-sparse fuzzy clustering with automatic region merging approach for image segmentation

Image segmentation based on fuzzy clustering has made enormous progress in recent years. However, the performance of existing methods degrades in noisy conditions and suffers from losses of foregrounds and the emergence of backgrounds. To settle these issues, in this paper, two methods – proposed method 1 and proposed method 2 – based on self-sparse fuzzy clustering algorithm (SSFCA) are proposed. The proposed methods use a normal shrink (NS) denoising algorithm as a pre-processing step for the suppression of noise. The output of NS undergoes the segmentation block SSFCA to get the segmentation output. The segmentation output is then subjected to a post-processing block for the removal of isolated pixels. In proposed method 1, the morphological cleaning operation acts as a post-processing block, whereas it is the morphological cleaning operation along with the automatic region merging approach in proposed method 2. The automatic region merging is achieved using connected component filtering based on the area density balance strategy (CCF-ADB). The proposed methods are tested on different datasets, and experimental results demonstrate their superior performance and effectiveness as compared to the state-of-art algorithms.

Shell mergers in the late stages of massive star evolution: new insight from 3D hydrodynamic simulations

ABSTRACT One-dimensional (1D) stellar evolution models are widely used across various astrophysical fields, however they are still dominated by important uncertainties that deeply affect their predictive power. Among those, the merging of independent convective regions is a poorly understood phenomenon predicted by some 1D models but whose occurrence and impact in real stars remain very uncertain. Being an intrinsically multi-D phenomenon, it is challenging to predict the exact behaviour of shell mergers with 1D models. In this work, we conduct a detailed investigation of a multiple shell merging event in a 20 M$_\odot$ star using 3D hydrodynamic simulations. Making use of the active tracers for composition and the nuclear network included in the 3D model, we study the merging not only from a dynamical standpoint but also considering its nucleosynthesis and energy generation. Our simulations confirm the occurrence of the merging also in 3D, but reveal significant differences from the 1D case. Specifically, we identify entrainment and the erosion of stable regions as the main mechanisms that drive the merging, we predict much faster convective velocities compared to the mixing-length theory velocities, and observe multiple burning phases within the same merged shell, with important effects for the chemical composition of the star, which presents a strongly asymmetric (dipolar) distribution. We expect that these differences will have important effects on the final structure of massive stars and thus their final collapse dynamics and possible supernova explosion, subsequently affecting the resulting nucleosynthesis and remnant.

Unsupervised Classification Identifies Warm, Fresh, and Dense Regimes of the Antarctic Margins

Abstract The ocean surrounding Antarctica, also known as the Antarctic margins, is characterized by complex and heterogeneous process interactions, which have major impacts on the global climate. A common way to understand changes in the Antarctic margins is to categorize regions into similar “regimes,” thereby guiding process-based studies and observational analyses. However, this categorization is traditionally largely subjective and based on temperature, density, and bathymetric criteria that are bespoke to the dataset being analyzed. In this work, we introduce a method to classify Antarctic shelf regimes using unsupervised learning. We apply Gaussian mixture modeling to the across-shelf temperature and salinity properties along the Antarctic margins from a high-resolution ocean model, ACCESS-OM2-01. Three clusters are found to be optimum based on the Bayesian information criterion and an assessment of regime properties. The three clusters correspond to the fresh, dense, and warm regimes identified canonically via subjective approaches. Our analysis allows us to track changes to these regimes in a future projection of the ACCESS-OM2-01 model. We identify the future collapse of dense water formation, and the merging of dense and fresh shelf regions into a single fresh regime that covers the entirety of the Antarctic shelf except for the West Antarctic. Our assessment of these clusters indicates that the Antarctic margins may shift into a two-regime system in the future, consisting only of a strengthening warm shelf in the West Antarctic and a fresh shelf regime everywhere else. Significance Statement The Antarctic margins are characterized by complex interactions of surface and ocean processes, producing distinct regions or “regimes.” Understanding where these regimes are and their future state is critical to understanding climate change. Based on a subjective assessment of ocean conditions, past research has identified fresh, dense, and warm regimes in the Antarctic margins. In this work, we use an unsupervised classification tool, Gaussian mixture modeling, to objectively identify the location of regimes around the Antarctic margins. Our method detects three regimes in an ocean model, which match the location of subjectively identified fresh, dense, and warm regimes, and indicates a future shrinking of the dense regime. Our method is adaptable to multiple datasets, enabling us to identify trends and processes in the Antarctic margins.

Analysis of morphological properties of fibrous electrospun polyurethane grafts using image segmentation

The concentration of the polymer in the electrospinning solution greatly influences the mechanical behaviour of electrospun vascular grafts due to the influence on scaffold morphology. The scaffold morphology (fiber diameter, fiber orientation and inter-fiber voids) of the grafts plays an important role in their behaviour during use. Even though manual methods and complex algorithms have been used so far for characterisation of the morphology of electrospun architecture, they still have several drawbacks that limit their reliability. This study therefore uses conventional, statistical region merging and a hybrid image segmentation algorithm, to characterise the morphology of the electrospun vascular grafts. Consequently, vascular grafts were fabricated using an in-house electrospinning equipment using three polymer material concentration levels (14%, 16% and 18%) of medical-grade thermoplastic polyurethane (Pellethane®). The image thresholding and segementation algorithms were then used for segmentation of SEM images extracted from the polymer grafts and then morphological parameters were investigated in terms of fiber diameter, fiber orientation, and interfiber spaces (pore area and porosity). The results indicate that electrospun image segmentation was "best" when the hybrid algorithm and the conventional algorithm was used, which implied that fiber property values computed from the hybrid algorithm were closed to the manually measurements especially for the 14% PU with fiber diameter 2.2%, fiber orientation 7.6% and porosity at 1.9%. However there was higher disperity between the manual and hybrid algorithm. This suggests more fiber uniformity in the 14%PU potentially affected the accuracy of the hybrid algorithm.

Modelling of microscopic crack initiation behaviour of fretting fatigue based on X-ray micro-computed tomography scan

Fretting fatigue is a particularly destructive form of fatigue failure that has drawn significant attention from researchers. This paper proposes a numerical modelling method based on tomography scan to investigate the impact of inclusions on fretting fatigue crack initiation behaviour. The results reveal that inclusions can affect the crack initiation direction and reduce the initiation lifetime. The difference between the properties of inclusions and matrix leads to local mechanical response mismatch, resulting in local stress, strain, and damage concentration. The mismatch in strain and damage follows a gradient distribution along the thickness as the distribution in these mechanical responses. These could explain why particles closer to the surface are more likely to cause damage. Additionally, the aggregation distribution of inclusions results in merging and connecting local regions with high stress, strain, and damage concentration, ultimately promoting fracture behaviour. Finally, the numerical models are validated with experimental data from fretting fatigue tests. Compared to the homogeneous model, the numerical model that considers inclusions from topography data can more accurately analyse the material damage and provide a more comprehensive understanding of fretting fatigue behaviour.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM).

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.

The local variation of the Gaussian modulus enables different pathways for fluid lipid vesicle fusion

Viral infections, fertilization, neurotransmission, and many other fundamental biological processes rely on membrane fusion. Straightforward calculations based on the celebrated Canham–Helfrich elastic model predict a large topological energy barrier that prevents the fusion process from being thermally activated. While such high energy is in accordance with the physical barrier function of lipid membranes, it is difficult to reconcile with the biological mechanisms involved in fusion processes. In this work, we use a Ginzburg–Landau type of free energy that recovers the Canham–Helfrich model in the limit of small width-to-vesicle-extension ratio, with the additional ability to handle topological transitions. We show that a local modification of the Gaussian modulus in the merging region both dramatically lowers the elastic energy barrier and substantially changes the minimal energy pathway for fusion, in accordance with experimental evidence. Therefore, we discuss biological examples in which such a modification might play a crucial role.

A Novel Image Segmentation Technique with N-L Means Filter Using Region Merging Strategy

Image segmentation is a branch of digital image processing which emphases on splitting an image into different fragments according to their features. This work proposes a novel image segmentation technique using fast non local (N-L) means filter with region merging approach. Here, a random noise is added to the original image and discrete wavelet transform (DWT) is applied to separate the low and high frequency bands of the digital image. The low frequency component is subjected to a fast N-L means filter and the filtered image is segmented by graph-based segmentation technique. Here, these segmented image regions are merged to reduce the over segmentation patterns. The detailed coefficients of the image component are treated with soft threshold filter to reduce the residual noise. Finally merged image is combined with preserved edge features at wavelet projection resulting in better segmented image. The results are compared with some of the best methods available in literature and are found to be significantly better. Keywords: Discrete wavelet transform (DWT), fast NL-means filter, graph-based segmentation, boundary based hierarchical region merging, soft-thresholding, peak signal to noise ratio (PSNR).

LES Investigation of a Piston-driven Synthetic Jet Actuator with Multiple Orifices

A piston-driven synthetic jet actuator has the potential for application in flow control and fundamental studies of turbulence, although the high-speed flow generated by this actuator is less investigated than a low-speed synthetic jet. The interaction of high-speed jets issued from a piston-driven synthetic jet actuator with multiple orifices is investigated with large eddy simulation (LES). The maximum jet Mach number is related to the maximum pressure inside the actuator regardless of the number of orifices. Temporal variations of the jet Mach number are almost identical for different cycles, and the jet formation in each cycle occurs under the same conditions despite the unsteady nature of the jet interaction. The phase-averaged statistics are used to examine the interaction of the synthetic jets. The converging, merging, and combined regions known for the interaction of continuous jets appear for the interaction of the high-speed synthetic jets slightly before the end of the blowing phase. However, the converging region is not clearly observed at the beginning of the blowing phase because the jets tend to be parallel to each other. Therefore, the combined region forms at a late stage of the blowing phase. Before the jets are combined, velocity fluctuations in the blowing phase become large near the furthest locations where the jets reach. Once the jets merge by their interaction, large velocity fluctuations are observed at the downstream end of the merging region. The probability density functions of velocity fluctuations in the blowing phase tend to deviate from a Gaussian distribution along the centerline of the jets. This deviation is more significant for the two-orifice model than for the four-orifice model under the same actuation frequency.

Retracted: Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.

[This retracts the article DOI: 10.1155/2021/1141619.].

Improving SLIC superpixel by color difference-based region merging

Improving SLIC superpixel by color difference-based region merging

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color. With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques.

On the Three-Stage Explosion Characteristics of Diethyl Ether-Oxygen Mixtures

ABSTRACT In this article, the three-stage explosion limits of diethyl ether (a typical alternative fuel with a high cetane number and high energy density) are taken as the research object. A comprehensive and in-depth analysis of the double negative temperature coefficient (NTC) behavior of the cool flame region and hot ignition region, through the detailed kinetic analysis, have been carried out. The results show that with the increase of the equivalence ratio, the double NTC phenomenon becomes more remarkable. In very lean conditions, such as the equivalence ratio of less than 0.5, the boundary between the first NTC region and the second NTC region becomes obscure, and a deeper single NTC phenomenon is formed as these two NTC regions merge. The whole explosion limit curve shifts to higher pressure conditions with the increase of the addition of inert gas. The NTC phenomenon in the moderate temperature region becomes more significant and the boundary between these two NTC regions is more obvious. In addition, the hot flame explosion limit also shows such two NTC response. The heat release rate (HRR) profile exhibits three-stage, two-stage, single-stage ignition, or non-ignition depending on the initial conditions. To elucidate the differences in the ignition process between the different explosion limit regimes, the sensitivity and reaction pathway analyses are performed. This study not only provides insight into the oxidation characteristics of ether fuels, but also provides theoretical support for high-performance engines.

Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian

To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%.

New Aspects of Energy Conversion in Magnetic Island Dynamics: Particle-in-cell Simulation of Multiple Island Coalescence and MMS Observations

Coalescence of multiple magnetic islands is recognized as an effective process to energize particles during magnetic reconnection, while its energy conversion process still remains unclear. Here, a two-dimensional fully kinetic simulation of multiple island coalescence with a small reconnection guide field is studied. In the analysis of energy conversion within a magnetic island, the dot product of is a useful quantity to compare with j · E = w 2, since the average work done by the Lorentz force on the circulating particles is negligible in the island and . A bipolar pattern of w 1 is found at a secondary island when the electrons are in circular motion inside the island. Significant energy dynamo (w 3 < 0) resulting from j ∥ E ∥ is found at the secondary island, which has not been reported before, where the parallel electric field E ∥ is highly correlated with w 3. Moreover, significant energy dissipation (w 3 > 0) due to is seen in the merging region between two coalescing islands. Both types of energy conversions are accompanied by enhancements in j ∥ and the parallel electron temperature T e∥. Three ion-scale magnetic islands (FR1, FR2, and FR3) observed by the Magnetospheric Multiscale spacecraft are compared favorably with the simulated signatures of energy dynamo and dissipation of an evolving secondary island. In particular, FR1 displayed a similar energy dynamo signature as that simulated in an early stage of the secondary island. FR2 and FR3 showed a dominant energy conversion similar to that obtained in a later stage of the secondary island.

Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images.

Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.

Object-based continuous monitoring of land disturbances from dense Landsat time series

An accurate mapping of land disturbances with regard to their timing and locations is the prerequisite for the success of downstream disturbance characterization. This study introduces and tests a novel approach that integrates a spatial perspective into the dense Landsat time series analysis, called “Object-Based COntinuous monitoring of Land Disturbance” (OB-COLD). The new algorithm is based on the recognition that the pixels under effects of the same disturbance event often present similar spectral change concurrently in a short time; such pixels experiencing concurrent change could be grouped as an analytic spatial unit, namely “change object”. OB-COLD first generates a change-magnitude snapshot every 60 days by applying per-pixel time series analysis to measure spectral change history. Then, an object-based change analysis is applied for each time-stamped snapshot: two levels of change objects are generated through over-segmentation and region merging; the changing area is determined by examining three object-level properties derived at different scales: the average change magnitude, the pre-change cover type, and the object size. Lastly, OB-COLD reconstructs model coefficients for each temporal segment based on the temporal breaks indicated by new snapshot-based change maps. We tested the new algorithm using 3000 randomly selected reference sample plots and eight Landsat ARD tiles across the continental United States. The accuracy assessment suggests that OB-COLD achieved 76.9% producer's accuracy, which is significantly higher than the per-pixel time series algorithm (i.e., COLD) (16.3 percentage increase) while keeping a comparable user's accuracies (58.7% vs. 57.8%). The quantitative and qualitative evaluations both suggest that OB-COLD could significantly reduce omission errors, particularly for stress disturbances. Most commission errors (73%) are attributed to agricultural practices and climate variability. The proposed algorithm is computationally scalable to large-scale spatiotemporal datasets with advanced cyberinfrastructure resources, holding great potential as the base detection algorithm for the next generation of land disturbance products.