Local Contour Research Articles

Theoretical investigation, synthesis and fabrication of efficient organometallic light emitting diodes based on Schiff base platinum complexes: A QTAIM study

The atomic electronic properties of two Schiff base Pt-salophen structures, with H and Br substitution atoms are studied using DFT/TDDFT computational calculation methods. Also, based on the quantum atoms-in-molecules theory (QTAIM), the atomic basin properties of these molecular systems are investigated. QTAIM analysis results show that these molecules can be grouped into donor-like and acceptor-like sections. Intra-molecular charge and thus energy transfer efficiency for each molecular systems are also studied, using the local contour map of the atomic electronic densities. In addition, these two structures have been synthesized and the absorption property of the molecular structures have been investigated. Finally, using these Schiff base Pt-salophen complexes (as an emitter layer), two different types of organic light emitting devices (OLEDs) have been fabricated. This study's results show that there is a good agreement between the theoretical analysis derived via QTAIM analysis and the experimental results of the fabricated devices. These results provide a new approach of the QTAIM analysis to be applied in investigation of organic materials used in OLED optoelectronic devices .

Classification of 3-D Point Clouds by a New Augmentation Convolutional Neural Network

Nowadays, the classification of point clouds has become a fundamental problem in 3-D information study. Different from the deep learning process of natural images, 3-D point clouds are massive and unorganized, which can be difficultly captured features by the convolution process directly. This letter proposes a new augmentation convolutional neural network (ACNN) to classify point clouds by adding a key augmentation layer before the classical sampling and convolution structure. Input data will be augmented before each sampling layer, which brings abundant learning information to help the network capture more local structures. In order to make the augmentation more effective, we formulate the parameters of augmentation layers learnable in the learning process according to the loss function. The proposed augmentation is based on automatically tuning the magnitude of the smoothness, which plays a significant role in point cloud processing and provides local features, for example, edges, contours, and edges. Results show that we have achieved the overall accuracy of 92.52% and 89.11% in the object classification on ModelNet10 and ModelNet40, respectively, which shows our superiority over other methods. Besides, the ACNN achieves an average miscalculation error of 0.28 and cross-entropy loss of 0.48 in the classification of laser scanning point clouds, which shows high robustness to noise and density in the outdoor scene classification.

ГЕОМЕТРИЯ ЛОКАЛЬНЫХ КОНТУРОВ КАПЕЛЬНОГО УВЛАЖНЕНИЯ ПОЧВЫ, ФОРМИРУЮЩИХСЯ В ЮЖНЫХ ЧЕРНОЗЕМАХ

Purpose: to determine empirical dependencies that allow predicting the geometric parameters of local soil moisture contours formed during drip irrigation of southern chernozems. Materials and methods. The empirical basis of the study was the data of experimental measurements of the moisture contour parameters of southern chernozems, formed during drip irrigation, on 10 experimental sites, characterized by: the content of physical clay from 29.0 to 71.1 % of the dry soil mass (DSM), the field moisture capacity from 20.0 to 30.3 % of DSM, soil density from 1.29 to 1.4 t/cub. m. Soil characteristics were studied using the methods of N. A. Kachinsky, “flooded areas” and “cutting ring”. The moisture content of soil samples was determined according to GOST 28268-89. Results. Analytical processing of 10 experimental moisture contour outlines made it possible to obtain a system of empirical dependencies for determining the outline, diameter, horizontal projection area and volume of the local soil moisture contour. Dependencies take into account soil characteristics – the content of physical clay, the field moisture capacity and bulk density, as well as the technological parameters of drip irrigation – dripper consumption, moisture depth, levels of pre-irrigation and post-irrigation soil moisture and irrigation rate. The obtained dependencies were tested on the experimental data of other researchers. As a result, the deviations of the calculated values of the moisture contour diameter from the experimental ones did not exceed 11.3 % and averaged 4.8 %. Forecasting the area of the horizontal projection showed a deviation of the calculation from the experiment on average 8.9 %, and for the volume of the moisture contour – 8.8 %. Conclusions. Empirical dependences that allow predicting the shape, linear, areal and volumetric dimensions of soil moisture contours formed in southern chernozems during drip irrigation have been established. The results of testing help conclude that the proposed dependencies are acceptable for practical use.

Low-Contrast Infrared Target Detection Based on Multiscale Dual Morphological Reconstruction

This letter proposes a novel method based on multiscale dual morphological reconstruction (MDMR) to detect low-contrast ship target with unknown size and polarity (bright or dark) in infrared image. First, the multiscale morphological reconstruction perceives the structural information of various scales. The target signal map (TSM) is presented to indicate the target polarity and enhance target by background subtraction and logarithmic histogram transform. Next, the boundary constraint entropy threshold selection (BCETS) is designed to extract candidate targets and avoid the over- or under-segmentation problems. Then, the local contour contrast descriptor (LCCD) is constructed and the discriminant rule of low-contrast target is established to identify true target and eliminate false alarms. Finally, the pixel-OR operation fuses the multiscale detection results. Extensive experiments show that the proposed method has a better effectiveness and robustness against compared methods.

Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data

The activity of the urban night-time economy is one of the most important indicators reflecting the prosperity of an urban economy. The business circle is an important carrier of urban commercial activities and the core area of urban nightlife. This paper takes the main urban area of Yiwu city as the research object. Based on POI data and night-time light remote sensing data, two-factor mapping, kernel density analysis, DBSCAN clustering, and local contour tree methods are adopted to identify the business circle structure of the main urban area of Yiwu city and analyse the relationship between business circle characteristics and the night-time economy. The following conclusions can be drawn. (1) The spatial superimposition relationship between the night-time remote sensing data and points of interest (POI) data in the main urban area of Yiwu city is good, and the overall coupling results show obvious circle structure characteristics. (2) The spatial distribution of different business combinations has obvious regularity: comprehensive shopping business shows a multicentre distribution pattern and has a hierarchical feature. In contrast, professional food and beverage and leisure and entertainment businesses are close to urban residential areas, and different groups of people live in different places with their own characteristics. (3) From 2015 to 2019, the brightness value of each business circle showed a continuously increasing trend. In 2020, due to the impact of COVID-19, most of them declined. (4) Overall, the difference in business circle tiers reflects the difference in the level of night-time economic activities.

Extraction of Text Areas in Scene Images by Focusing on Local Contour Density and Corner Pixel Ratio

Extraction of Text Areas in Scene Images by Focusing on Local Contour Density and Corner Pixel Ratio

Coastline Extraction from GF-3 SAR Images Using LKDACM and GMM Algorithms

Coastline detection using a Gaussian Mixture Model (GMM) applied to synthetic aperture radar (SAR) imagery is usually inaccurate due to the inherent noise of SAR data. In addition, the traditional active counter model is sensitive to the initial position of the contour line and requires a large number of iterations to converge to a solution. In this study, we first used the GMM algorithm to segment the SAR images and obtain a coarse land and sea segmentation map. This map is then used as the initial position for a subsequent active contour model. The K distribution was introduced into the local statistical active contour model to better model the SAR image. The Gaussian distribution-based local active contour model and the algorithm detailed in this paper were used to perform coastline extraction experiments on four SAR images. Four GF-3 SAR images with different modes were collected to validate the efficiency of the proposed method. The experimental results show that the coastline extraction methods from SAR images based on the GMM algorithm and the K distribution-based local statistical active contour model (LKDACM) overcame the shortcomings of the traditional active contour model to accurately and quickly detect coastlines, thus enabling the detection of coastline changes.

Deep Learning Technology in Pathological Image Analysis of Breast Tissue.

To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms (P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant (P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.

Analyzing the Impact of Building Wind in Coastal Areas

Kwon, Y.; Kim, J., and Kwon, S., 2021. Analyzing the impact of building wind in coastal areas. In: Lee, J.L.; Suh, K.S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 266–270. Coconut Creek (Florida), ISSN 0749-0208. Building wind causes a large amount of damage in coastal cities, where high-rise buildings are, typically, densely constructed. This paper describes the effects of building wind on coastal cities with a high concentration of highrise buildings under extreme weather conditions such as during typhoons. We performed simulations involving four wind directions (east, west, south, and north) at two sites, i.e., Marine City and LCT, which are well known as representative coastal urban areas in Busan, South Korea, with concentrated areas of high-rise buildings. We used a computational fluid dynamics program; a user-defined function was employed to simulate the wind environment during a typhoon. A contour analysis of the wind velocity indicated that the overall wind speeds increased by a factor of approximately two or more compared with the inflow data (25 m/s) when the wind passed between high-rise buildings. Generally, among the four wind directions, wind speeds were significantly enhanced when the north wind blew over both Marine City and LCT. The highest wind speed was observed at LCT when the west wind blew; however, the value was considerably local, with a significantly small contour area. Interestingly, the highest wind speed with a considerably local contour was observed near the tallest highrise building, supporting the hypothesis that wind speed increases rapidly downwind from the tallest building. As the height of the building increased, the downwind effect became stronger.

Independent Mechanisms for Processing Local Contour Features and Global Shape

Independent Mechanisms for Processing Local Contour Features and Global Shape

Automatic Reconstruction of Building Façade Model from Photogrammetric Mesh Model

Three-dimensional (3D) building façade model reconstruction is of great significance in urban applications and real-world visualization. This paper presents a newly developed method for automatically generating a 3D regular building façade model from the photogrammetric mesh model. To this end, the contour is tracked on irregular triangulation, and then the local contour tree method based on the topological relationship is employed to represent the topological structure of the photogrammetric mesh model. Subsequently, the segmented contour groups are found by analyzing the topological relationship of the contours, and the original mesh model is divided into various components from bottom to top through the iteration process. After that, each component is iteratively and robustly abstracted into cuboids. Finally, the parameters of each cuboid are adjusted to be close to the original mesh model, and a lightweight polygonal mesh model is taken from the adjusted cuboid. Typical buildings and a whole scene of photogrammetric mesh models are exploited to assess the proposed method quantitatively and qualitatively. The obtained results reveal that the proposed method can derive a regular façade model from a photogrammetric mesh model with a certain accuracy.

Symmetric mean and directional contour pattern for texture classification

In this letter, we propose a simple yet effective texture descriptor, symmetric mean and directional contour pattern (SMDCP), for texture classification. In particular, first the robust symmetric mean pattern (RSMP) that extracts the sign and amplitude information of the local difference through the neighbourhood average in a new scheme of encoding to further enhance the robustness to noise is constructed. Then a local directional and contour pattern (LDCP) to represent the contour information and direction information of adjacent sampling points is extracted. By concatenating the RSMP and LDCP, a robust and effective texture descriptor (SMDCP) for classification is built. Experimental results reveal that the proposed method obtains significant performance and high discrimination in comparison with 10 representative approaches.

Polyetheretherketone patient-specific implants (PPSI) for the reconstruction of two different mandibular contour deformities.

This study aims to prospect the soft and hard tissue changes after augmentation of two different local mandibular contour defects using polyetheretherketone (PEEK) patient-specific onlay implants. Six patients with disfiguring local mandibular deformities were included in this study (five males and one female) and received seven PEEK patient-specific implants (PSI), virtually designed and surgically settled to augment seven mandibular defects, three deficient chins, and four mandibular angels. The analysis of the soft and hard tissue changes utilized the superimposition of the preoperative and the 6-month postoperative sagittal and coronal CT views, after standardizing the radiographic interpretation. The soft tissue gain for the chin was 6.8mm ± 0.98 with a 45.8% increase versus 4.42mm ± 0.41 with a 22.9% increase for the angle. The difference in the soft tissue gain between the two groups was statistically significant (P = 0.0001). Comparing the soft tissue gain to the planned implant thickness, the percentage of the soft tissue gain for the chin recorded 109.2% versus 65.57% for the angle. The difference between the two groups was also statistically significant (P < 0.0001). PEEK patient-specific onlay implants represented an efficient and straightforward modality to augment local mandibular contour deformities with favorable esthetic outcomes; the total soft tissue profile gain of the chin region markedly exceeded that of the mandibular angle.

Numerical assessments of flow pattern and heat transfer profile for the round tube equipped with different configurations of the dual-inclined baffle

Numerical studies of the heat exchanger tube (HXT) equipped with the dual-inclined baffle (DIB) on fluid streams and heat transfer profiles are reported. The DIB configurations can be separated into three types: 1. The DIB is inserted in the middle of the HXT called “type I″, 2. The DIB is placed on the HXT wall called “type II” and 3. The combination of the type I and II DIB called “type III”. The three DIB types are designed with the main purpose to create three different flow profiles. The influences of DIB height with a single pitch distance (P/D = 1) and DIB attack angle of 30° for heat transfer characteristics and fluid streams are discussed in a laminar flow region at Re = 100–2000 (considered at the entry condition). The numerical problem of the HXT inserted with the DIB is solved with a commercial code (the finite volume method). The computation domain is validated to confirm the reliability and accuracy of the simulated results. The flow configurations: streamlines in transverse planes (y-z planes), and heat transfer behaviors: temperature contours in transverse planes (y-z planes) and local Nusselt number contours (Nux), in the HXT equipped with the DIB are proposed in the simulated-result section. The relations of the average Nusselt number ratio (Nu/Nu0), friction factor ratio (f/f0) and thermal enhancement factor (TEF) with the Reynolds numbers in the HXT inserted with the DIB are presented. As the simulated results, the equipment of the DIB in the HXT brings upper heat transfer rate and thermal performance than those of the smooth tube because of the creations of the vortex streams and impinging streams. The different flow profiles are detected when varying the DIB types that impact for the variations of the heat transfer profile. For the studied range, the increased heat transfer rate in the HXT installed with the DIB is observed to be around 1.03–17.46 times above the plain tube depended on the DIB type, DIB blockage and Reynolds number. Additionally, the maximal TEF of 3.70 is found for the type II DIB at b/D = 0.25 and Re = 2000.

Characteristics of land surface temperature clusters: Case study of the central urban area of Guangzhou

Land surface temperature (LST) clusters within cities are typical regions reflecting the spatial heterogeneity of the urban thermal environment. To explore the spatial heterogeneity of the urban thermal environment in the central urban area of Guangzhou, we used the localized contour tree method to identify high- and low-LST clusters. In addition, to understand the characteristics of LST clusters (characteristics of the thermal environment and landscape distribution within the LST cluster), we obtained thermal field value (TFV) data based on the rules of equal-area division and determined the urban functional grid (UFG) based on multi-source data. We found that the characteristics of the high- and low-LST clusters significantly differed. Compared with that of the low-LST clusters, the spatial distribution of the high-LST clusters is denser. The number and total area of low-LST clusters are smaller than that of high-LST clusters, although the average area of low-LST clusters is larger. The spatial heterogeneity of the 10 types of UFGs within the high- and low-LST clusters is significant. The developed and ecological UFGs classified from 10 types of UFGs affect the formation of LST clusters. In addition, there are main UFGs that affect the mean TFV of the LST clusters. This study provides a new approach for the identification of LST clusters and a new perspective for exploring the spatial heterogeneity of urban thermal environments.

An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines

Microorganisms attached to marine current turbine may induce imbalance faults that badly affect the power generation efficiency. Therefore, it is necessary to conduct attachment recognition for prompt device maintenance. This paper proposes a coarse-fine semantic segmentation network (CSSN) to adaptively recognize the attachment location and size from blurry underwater images. The CSSN contains a deep coarse branch to perform global segmentation and a shallow fine branch to obtain local contours. The two branches are adaptively fused with dynamic weights in the training process. The final segmentation maps are produced by a softmax layer, after which the precise attachment area percentage can be computed. Besides, dropout is applied to estimate the recognition uncertainty that provides intuitive guidance for the maintenance decision. Experimental results show that the proposed method is efficient to recognize the attachment under turbid submerged conditions.

Novel methodology to assess the effect of contouring variation on treatment outcome.

Contouring variation is one of the largest systematic uncertainties in radiotherapy, yet its effect on clinical outcome has never been analyzed quantitatively. We propose a novel, robust methodology to locally quantify target contour variation in a large patient cohort and find where this variation correlates with treatment outcome. We demonstrate its use on biochemical recurrence for prostate cancer patients. We propose to compare each patient's target contours to a consistent and unbiased reference. This reference was created by auto-contouring each patient's target using an externally trained deep learning algorithm. Local contour deviation measured from the reference to the manual contour was projected to a common frame of reference, creating contour deviation maps for each patient. By stacking the contour deviation maps, time to event was modeled pixel-wise using a multivariate Cox proportional hazards model (CPHM). Hazard ratio (HR) maps for each covariate were created, and regions of significance found using cluster-based permutation testing on the z-statistics. This methodology was applied to clinical target volume (CTV) contours, containing only the prostate gland, from 232 intermediate- and high-risk prostate cancer patients. The reference contours were created using ADMIRE® v3.4 (Elekta AB, Sweden). Local contour deviations were computed in a spherical coordinate frame, where differences between reference and clinical contours were projected in a 2D map corresponding to sampling across the coronal and transverse angles every 3°. Time to biochemical recurrence was modeled using the pixel-wise CPHM analysis accounting for contour deviation, patient age, Gleason score, and treated CTV volume. We successfully applied the proposed methodology to a large patient cohort containing data from 232 patients. In this patient cohort, our analysis highlighted regions where the contour variation was related to biochemical recurrence, producing expected and unexpected results: (a) the interface between prostate-bladder and prostate-seminal vesicle interfaces where increase in the manual contour relative to the reference was related to a reduction of risk of biochemical recurrence by 4-8% per mm and (b) the prostate's right, anterior and posterior regions where an increase in the manual contour relative to the reference contours was related to an increase in risk of biochemical recurrence by 8-24% per mm. We proposed and successfully applied a novel methodology to explore the correlation between contour variation and treatment outcome. We analyzed the effect of contour deviation of the prostate CTV on biochemical recurrence for a cohort of more than 200 prostate cancer patients while taking basic clinical variables into account. Applying this methodology to a larger dataset including additional clinically important covariates and externally validating it can more robustly identify regions where contour variation directly relates to treatment outcome. For example, in the prostate case we use to demonstrate our novel methodology, external validation will help confirm or reject the counter-intuitive results (larger contours resulting in higher risk). Ultimately, the results of this methodology could inform contouring protocols based on actual patient outcomes.

Detecting and Analyzing Urban Centers Based on the Localized Contour Tree Method Using Taxi Trajectory Data: A Case Study of Shanghai

Urban structure is of vital importance to urban planning, transportation, economics and other applications. Since detecting and analyzing urban centers is crucial for understanding urban structure, a large number of studies on urban center extraction have been performed. In this paper, we propose an analysis framework to identify urban centers by using taxi trajectory data. The proposed approach differs from previous methods by employing a novel way to simulate taxi trajectory data with the topographic surface. We extracted pick-up and drop-off spots from taxi trajectory data and employed the localized contour tree method to delineate the boundaries and hierarchies of urban centers. The experiments show that the proposed method can successfully detect urban centers and analyze their temporal patterns in different periods in Shanghai, China.

The Interaction Between Timescale and Pitch Contour at Pre-attentive Processing of Frequency-Modulated Sweeps

Speech comprehension across languages depends on encoding the pitch variations in frequency-modulated (FM) sweeps at different timescales and frequency ranges. While timescale and spectral contour of FM sweeps play important roles in differentiating acoustic speech units, relatively little work has been done to understand the interaction between the two acoustic dimensions at early cortical processing. An auditory oddball paradigm was employed to examine the interaction of timescale and pitch contour at pre-attentive processing of FM sweeps. Event-related potentials to frequency sweeps that vary in linguistically relevant pitch contour (fundamental frequency F0 vs. first formant frequency F1) and timescale (local vs. global) in Mandarin Chinese were recorded. Mismatch negativities (MMNs) were elicited by all types of sweep deviants. For local timescale, FM sweeps with F0 contours yielded larger MMN amplitudes than F1 contours. A reversed MMN amplitude pattern was obtained with respect to F0/F1 contours for global timescale stimuli. An interhemispheric asymmetry of MMN topography was observed corresponding to local and global-timescale contours. Falling but not rising frequency difference waveforms sweep contours elicited right hemispheric dominance. Results showed that timescale and pitch contour interacts with each other in pre-attentive auditory processing of FM sweeps. Findings suggest that FM sweeps, a type of non-speech signal, is processed at an early stage with reference to its linguistic function. That the dynamic interaction between timescale and spectral pattern is processed during early cortical processing of non-speech frequency sweep signal may be critical to facilitate speech encoding at a later stage.

Efficient convolutional neural network with multi-kernel enhancement features for real-time facial expression recognition

Facial expressions are the most direct external manifestation of personal emotions. Different from other pattern recognition problems, the feature difference between facial expressions is smaller. The general methods are difficult to effectively characterize the feature difference, or their parameters are too large to realize real-time processing. This paper proposes a lightweight mobile architecture and a multi-kernel feature facial expression recognition network, which can take into account the speed and accuracy of real-time facial expression recognition. First, a multi-kernel convolution block is designed by using three depthwise separable convolution kernels of different sizes in parallel. The small and the large kernels can extract local details and edge contour information of facial expressions, respectively. Then, the multi-channel information is fused to obtain multi-kernel enhancement features to better describe the differences between facial expressions. Second, a "Channel Split" operation is performed on the input of the multi-kernel convolution block, which can avoid repeated extraction of invalid information and reduce the amount of parameters to one-third of the original. Finally, a lightweight multi-kernel feature expression recognition network is designed by alternately using multi-kernel convolution blocks and depthwise separable convolutions to further improve the feature representation ability. Experimental results show that the proposed network achieves high accuracy of 73.3 and 99.5% on FER-2013 and CK + datasets, respectively. Furthermore, it achieves a speed of 78 frames per second on 640 × 480 video. It is superior to other state-of-the-art methods in terms of speed and accuracy.