Contour Description Research Articles

Segmentation of beach plastic fragments’ contours based on self-organizing map and multi-shape descriptors: A rapid indication of fragmentation and wearing types

Environmental plastic fragments have been verified as byproducts of large plastic and its secondary pollutants including micro and nanoplastics. There are few quantitative studies available, but their contours have values for the weathering mechanisms. We used geometric descriptors, fractal dimensions, and Fourier descriptors to characterize field and artificial polyethylene and polypropylene samples as a means of investigating the contour characteristics. It provides a methodological framework for contour classification. Unsupervised classification was performed using self-organizing neural networks with size-invariance parameters. We revealed the isometric phenomenon of plastic fragments during fragmentation, i.e., that the degree of contour rounding and complexity increase and decrease, respectively, with decreasing fragment size. With an average error rate of 8.9 %, we can distinguish artificial samples from field samples. It was also validated by the difference in Carbonyl Index between groups. We propose a two-stage process for plastic fragmentation and give three types of contour features which were key in the description of fragmented contours, i.e., size, complexity, and rounding. Our work will improve the accuracy of characterizations regarding the weathering and fragmentation processes of certain kinds of plastic fragments. The contour parameters also have the potential to be applied in more realistic scenarios and varied polymers.

A Cognitive View on Prosodic Relations

The paper presents a new F0 contour partitioning approach by using the category of prosodic relations also used by Ladd (2008) for improving the F0 contour descriptions based on phonological categories. The new approach involves a cognitive view on the low-high and high-low ’metrical’ structures of prosodic relations, by relating them to the structures of cognitive relations generated during speech object representations at the cortical level. In section 2, the paper presents the information structure model by defining the cognitive categories that describes prosodic relations of F0 contours involving their two overlapped structures and nuclear positions. CU_predicate-CU_argument and CU_theme-CU_rheme are the two structural levels of prosodic relations. The model proposes a binary-tree hierarchy to describe the articulation of prosodic relations within utterances. Two rules are formulated for the identification of nuclear constituents of prosodic relations. The utterances analysed in section 3 illustrate how prosodic and prominence relations can be identified by analysing acoustic cues of their F0 contours. Utterances correspond to English borad focus and narrow focus statements. Focus positions are deduced by only using the rule of the cognitive model.

Cone beam computed tomography assessment of maxillary anterior teeth cervix dimensions in healthy adults for optimal anatomic healing abutments.

The abutments produced with circular symmetry failed to accurately replicate the natural teeth's cervical shapes. The purpose of this study was to measure cervical cross-sections of maxillary anterior teeth using cone beam computed tomography (CBCT) images to design anatomic healing abutments. CBCT data of 61 patients were analyzed using Ez3D Plus software. Measurements were taken at the cemento-enamel junction (CEJ) and 1 mm coronal to CEJ for maxillary central incisors, lateral incisors, and canines. Various parameters, including area, perimeter, and eight line segments in the distal (a), disto-palatal (b), palatal (c), mesio-palatal (d), mesial (e), mesio-labial (f), labial (g), and disto-labial (h) directions, were used to describe dental neck contours. The ratios (f/b and h/d) were analyzed, and differences based on sex and dental arch morphology were explored. Significant differences were found in area and perimeter between males and females, but not in f/b and h/d ratios. Differences in the f/b ratio were observed among dental arch morphologies for maxillary central incisors, lateral incisors, and canines. CBCT measurements of cervical cross-sections provide more accurate data for designing anatomic healing abutments. The fabrication of anatomical healing abutments needs to consider the influence of gender on cervical size and to explore the potential effect of arch shape on cervical morphology. The novel method provides detailed measurements for the description of dental cervical contours for patients with bilateral homonymous teeth missing. The measurements of this study could be utilized to design more accurate anatomic healing abutments to create desired morphology of peri-implant soft tissue.

Advanced Planar Projection Contour (PPC): A Novel Algorithm for Local Feature Description in Point Clouds.

Local feature description of point clouds is essential in 3D computer vision. However, many local feature descriptors for point clouds struggle with inadequate robustness, excessive dimensionality, and poor computational efficiency. To address these issues, we propose a novel descriptor based on Planar Projection Contours, characterized by convex packet contour information. We construct the Local Reference Frame (LRF) through covariance analysis of the query point and its neighboring points. Neighboring points are projected onto three orthogonal planes defined by the LRF. These projection points on the planes are fitted into convex hull contours and encoded as local features. These planar features are then concatenated to create the Planar Projection Contour (PPC) descriptor. We evaluated the performance of the PPC descriptor against classical descriptors using the B3R, UWAOR, and Kinect datasets. Experimental results demonstrate that the PPC descriptor achieves an accuracy exceeding 80% across all recall levels, even under high-noise and point density variation conditions, underscoring its effectiveness and robustness.

Forming characteristics analysis of variable-process alternated overlapping strategy based on contour description for laser melting deposition

To predict the geometric characteristics and improve the forming quality and macro-micro defects of multi-track overlapping cladding layer effectively, the experimental research of laser melting deposition single-track Co-based alloy on 42CrMo substrate was carried out. The influence trends of laser process parameters on the single-track cladding height and cladding width were analyzed, and the regression prediction models were established. A variable-process alternated overlapping strategy was proposed using the geometric functions to fit the single-track profile. The surface forming quality and macro-micro defect of different overlapping cladding layers were compared and evaluated. The results showed that the cladding height and cladding width regression models established by an improved optimization algorithm had an excellent prediction effect. The variable-process alternated overlapping cladding layer prepared by the parabolic function had a flat surface, and the fluctuation distance was 81.56 μm, which was significantly improved compared with other overlapping strategies. The surface flatness of the continuous overlapping cladding layer was the worst and the spheroidization defect was the most unsatisfied. The internal defects of the constant-process alternated overlapping cladding layer were the most terrible, with more non-fusion and cracks caused by oxides. The variable-process alternated overlapping strategy proposed in this study can effectively prepare a multi-track cladding layer with a smooth surface and dense interior, which provides essential theoretical guidance for the near-net forming of component surface deposition and repair.

A Case-Based Reasoning Method for Generalizing Depth Contours considering Both Navigational Safety Assurance and Line Shape Preservation

To address the problem that the parameters are relatively fixed in the existing automatic methods of depth contour generalization, a case-based reasoning method for generalizing depth contours is proposed considering navigational safety and line shape. First, the structured description of depth contours before and after cartography generalization is made to form case samples. Then, driven by the training samples, the machine learning of BP neural network model is constructed, to obtain the simplification degree taking into consideration the preservation of contour shapes. Finally, the generalization parameters are flexibly adjusted based on the simplification degree obtained through the case-based reasoning, so that depth contours can be adaptively generalized for various complex situations. The experimental results demonstrate that: (1) The case-based reasoning method can make the generalization of depth contours comply with the principle of navigational safety; (2) The case-based reasoning method has a stronger applicability maintaining the shape of depth contour, and is more suitable for the automatic generalization of depth contours, compared with the rolling circle method and the triangulation method. Generally, the case-based reasoning method has the potential to improve cartographic quality meeting the requirements of IHO specification, supporting the automatic production of ENC and nautical chart product.

Спецификация автозаправочной станции как объекта кадастрового учета

The authors discuss the features of the gas stations cadastral state registration as structures. The inaccuracy of information on the description of contours and main characteristics of real estate objects in the data of the Unified State Register of Real Estate, caused by the lack of a unified methodology, was revealed. In order to reduce the influence of the subjective component and avoid inconsistencies, it is necessary to adopt a single model standard for cadastral works on gas stations. We propose their specification as cadastral registration units, which means uniform immovable complexes, whose practical application will enable preserving information on the characteristics of real estate objects as part of the whole construction, as well as taking most correctly into account their design features by determining the coordinates of all characteristic points of contours of different types. This will contribute to improving the safety level of the gas station complex operating and implementing technology unity principle for maintaining a Unified State Register of Real Estate throughout the territory of the Russian Federation

Evaluation of effects of ultrasound-assisted curing on the flavor of Chinese bacon

The curing stage is of great importance in flavor formation during Chinese bacon processing. Ultrasound-assisted curing plays an essential role in the Lipid oxidation of meat products. In this study, GC–MS and electronic nose were used to analyze the influence of different power ultrasonic-assisted curing on the flavor formation of Chinese bacon. Through the analysis of phospholipid and lipase, the fundamental precursors of ultrasonic on the flavor of Chinese bacon were determined. It was found that there were differences in the flavor contour description of Chinese bacon between the ultrasonic treatment group, mainly due to the change in the W1W sensor. A total of 28 volatile compounds were detected by GC–MS, and the aldehyde content increased with ultrasonic power. PC and PE are the main flavor precursors in the curing process. This study provides a theoretical basis for improving the curing technology of Chinese bacon.

Peanut kernel integrity detection based on deep learning convolution neural network

The edge features of peanut grain image are not considered in peanut grain integrity detection, and there are many noises, resulting in low accuracy of peanut grain integrity detection and poor effect of image edge noise removal. Therefore, this paper designs a peanut seed integrity detection method based on deep learning convolution neural network. The edge contour of peanut grain image is closed, the edge contour curve characteristics of peanut grain image are extracted, the noise area of peanut grain edge image is determined according to the filter window of Gaussian kernel function, and the wavelet descriptor in contour description operator is used to reduce the edge noise of peanut grain image. Input the image into the convolution neural network, update the weight by gradient descent method, construct the peanut seed integrity detection model, output the peanut seed integrity detection results, and realize the peanut seed integrity detection. The experimental results show that the proposed detection method can improve the accuracy of peanut grain detection and has certain feasibility.

Soliton Solutions of Klein–Fock–Gordon Equation Using Sardar Subequation Method

The Klein–Fock–Gordon equation (KFGE), defined as the equation of relativistic wave related to NLEEs, has numerous implications for energy particle physics and is useful as a model for several types of matter, with deviation in the basic stuffs of particles and in crystals. In this work, the Sardar subequation method (SSM) is used for finding the solution of this KFGE. The advantage of SSM is that it provides many different kinds of solitons, such as dark, bright, singular, periodic singular, combined dark–singular and combined dark–bright solitons. The results show that the SSM is very reliable, simple and can be functionalized to other nonlinear equations. It is verified that all the attained solutions are stable by modulation instability process. To enhance the physical description of solutions, some 3D, contour and 2D graphs are plotted by taking precise values of parameters using Maple 18.

Effects of Familiarity and Dialect Experience on the Description of Tonal Variant.

This study investigates the tonal variant description of the official dialect in China (Putonghua) as a factor in the coevolution of dialects. Three sociophonetic factors, target tone familiarity, tonal variant familiarity, and tonal inventory size, are included in order to raise interesting theoretical questions concerning the role of familiarity and dialect experience in sound change. Standard Putonghua tones are manipulated in height and shape in order to create systematically varying stimuli. Speakers from three Chinese dialect groups, Beijing Mandarin, Shanghai Wu, and Guangzhou Cantonese, are invited to rate the applicability of a description of pitch contour and height to the stimuli. The three dialects have different tonal inventory size, and their native speakers have different levels of familiarity with Putonghua tone or Putonghua tonal variants. The above three sociophonetic factors make different predictions about listeners' performances. The findings of the experimental analysis of data confirm the role of tonal variant familiarity in predicting participants' descriptive decisions on tone height variants. Tonal variant familiarity is also combined with tone inventory size to explain the assignment of descriptions of tone shape variations. This suggests that when variations still follow the phonetic pattern of the tone distribution of the Putonghua tonal system, listeners give phonetic patterns the primary role in acoustic decisions but still benefit from their dialect experiences in making more precise acoustic decisions. It also suggests that when variations violate the phonetic features of the target tonal system, they may depend on familiarity with the individual variant. This study applies an innovative sociophonetic method by conducting a perception experiment online with a self-paced procedure. The findings here are crucial for examining the relationship between sociophonetic factors and listeners' acoustic decisions and the cultural coevolution of cross-dialect tonal variation. The findings here also give support to the validity of the current web-based crowd perception experiment design and are also needed to facilitate research under restricted conditions, such as a pandemic situation.

H-ProSeg: Hybrid ultrasound prostate segmentation based on explainability-guided mathematical model

Background and ObjectiveAccurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for image-guided prostate interventions and prostate cancer diagnosis. However, it remains a challenging task for various reasons, including a missing or ambiguous boundary between the prostate and surrounding tissues, the presence of shadow artifacts, intra-prostate intensity heterogeneity, and anatomical variations. MethodsHere, we present a hybrid method for prostate segmentation (H-ProSeg) in TRUS images, using a small number of radiologist-defined seed points as the prior points. This method consists of three subnetworks. The first subnetwork uses an improved principal curve-based model to obtain data sequences consisting of seed points and their corresponding projection index. The second subnetwork uses an improved differential evolution-based artificial neural network for training to decrease the model error. The third subnetwork uses the parameters of the artificial neural network to explain the smooth mathematical description of the prostate contour. The performance of the H-ProSeg method was assessed in 55 brachytherapy patients using Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) values. ResultsThe H-ProSeg method achieved excellent segmentation accuracy, with DSC, Ω, and ACC values of 95.8%, 94.3%, and 95.4%, respectively. Meanwhile, the DSC, Ω, and ACC values of the proposed method were as high as 93.3%, 91.9%, and 93%, respectively, due to the influence of Gaussian noise (standard deviation of Gaussian function, σ = 50). Although the σ increased from 10 to 50, the DSC, Ω, and ACC values fluctuated by a maximum of approximately 2.5%, demonstrating the excellent robustness of our method. ConclusionsHere, we present a hybrid method for accurate and robust prostate ultrasound image segmentation. The H-ProSeg method achieved superior performance compared with current state-of-the-art techniques. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. The proposed models have the potential to improve prostate cancer diagnosis and therapeutic outcomes.

The American English spontaneous speech minicorpus. Architecture and comparability

This paper presents the American English (AE) minicorpus, a spontaneous speech resource created within the auspices of the C-ORAL-BRASIL project consisting of texts selected from the Santa Barbara Corpus of Spoken American English. We focus on the sampling strategy that guided the selection of texts, the transcription criteria that were implemented and the prosodic and informational annotation carried on the AE minicorpus. The minicorpus was designed to be comparable to the minicorpora of the C-ORAL projects for Italian and Brazilian Portuguese, which were conceived to allow the study of information structure in spontaneous speech in accordance with the principles of the Language into Act Theory. This theory comprises a pragmatic framework for the study of spontaneous speech and it integrates the IPO approach into its prosodic model. The IPO approach consists of a perception-based model for the study of intonation, providing an apparatus for the description and classification of melodic contours observed in spontaneous speech.

Intracardiac Mass Detection and Classification Using Double Convolutional Neural Network Classifier

Intracardiac masses identification in the images of echocardiogram images in one of the most essential tasks in making the diagnosis of cardiac disease. For making the improvement in accuracy over the diagnosis as a new complete method of classifying the echocardiogram images automatically which is based on robust back propagation neural network algorithm in being proposed for distinguishing intracardiac thrombi and tumor. Initially, the cropping over the specific region is done in order to make the definition of the mass area. Later on, as the second step the processing of globally unique denoising technique is being implied for the removal of speckle and in order to make the preservation of anatomical structured component in the image. This is defined in terms of preprocessing and it is carried out by Patch-based sparse representation. Subsequently the description of the mass contour and its interconnected wall of the artery are being done by the segmentation mechanism denoted as Linear Iterative Vessel Segmentation model. As the prefinal stage, the processing of boundary, texture and the motion features are being carried out through the processing by double convolutional neural network (DCNN) classifier in order to determine the classification of two different masses. Totally 108 cardiac masses images are being collected for accessing the effectiveness of the classifier. It is also realized with the various state of the art classifiers as projected the demonstration of the greatest performance that has been disclosed with an achievement of 98.98% of accuracy, 98.89% of sensitivity and 99.16% of specificity that has been resulted for DCNN classifier. It determines the explication that the proposed method is capable of performing the classification of intracardiac thrombi and tumors in the echocardiography and ensures for potentially assisting the medical doctors who are in the clinical practice.

A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data

Due to its high ranging accuracy, spaceborne laser altimetry technology can improve the accuracy of satellite stereo mapping without ground control points. In the past, full-waveform ICE, CLOUD, and Land Elevation Satellite (ICESat) laser altimeter data have been used as one of the main data sources for global elevation control. As a second-generation satellite, ICESat-2 is equipped with an altimeter using photon counting mode. This can further improve the application capability for stereo mapping because of the six laser beams with high along-track repetition frequency, which can provide more detailed ground contour descriptions. Previous studies have addressed how to extract high-accuracy elevation control points from ICESat data. However, these methods cannot be directly applied to ICESat-2 data because of the different modes of the laser altimeters. Therefore, in this paper, we propose a method using comprehensive evaluation labels that can extract high-accuracy elevation control points that meet the different level elevation accuracy requirements for large scale mapping from the ICESat-2 land-vegetation along-track product. The method was verified using two airborne lidar data sets. In flat, hilly, and mountainous areas, by using our method to extract the terrain elevation, the root-mean-square error of elevation control points decrease from 1.249–2.094 m, 2.237–3.225 m, and 2.791–4.822 m to 0.262–0.429 m, 0.484–0.596 m, and 0.611–1.003 m, respectively. The results show that the extraction elevations meet the required accuracy for large scale mapping.

Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation. Enlightened by the multiapplication and favorable performance of fuzzy clustering methods, intuitionistic fuzzy c-means (IFCM) is applied as the backbone of our segmentation method. The contributions of the proposed method mainly include two parts. First, motivated by potential target characteristics and tendency for clearer contour description, structural symmetry information is utilized, which is an intrinsic shape feature of objects and would be significant especially when the texture and details of the target are lost in infrared images. Further, a map that represents the probability of pixels belonging to the target is constructed in the form of ellipse symmetry region. Combined with the probability map, symmetry information is utilized to establish a novel dissimilarity function in fuzzy clustering. Second, local homoplasy information which is designed based on region similarity is introduced to suppress the intensity inhomogeneity and noises to further improve the performance of the proposed method. Finally, a dataset containing 500 infrared pedestrian images paired with corresponding pixel-wise annotation is constructed to verify segmentation effectiveness. The proposed SR-IFCM is compared with 12 state-of-the-art segmentation methods. The experimental results indicate that the proposed method outperforms the comparison methods and works better for infrared pedestrian segmentation.

Phonetics of White Hmong vowel and tonal contrasts

Hmong languages, particularly White Hmong, are well studied for their complex tone systems that incorporate pitch, phonation, and duration differences. Still, prior work has made use mostly of tones elicited in their citation forms in carrier phrases. In this paper, we provide a detailed description of both the vowel and tone systems of White Hmong from recordings of read speech. We confirm several features of the language, including the presence of nasal vowels (rather than derived nasalized vowels through coarticulation with a coda [ŋ]), the description of certain tone contours, and the systematic presence of breathy and creaky voice on two of the tones. We also find little evidence of additional intonational f0 targets. However, we show that some tones vary greatly by their position in utterance, and propose novel descriptions for several of them. Finally, we show that $\textrm{H}1^{\!*}$ –H2*, a widely used measure of voice quality and phonation in Hmong and across languages, does not adequately distinguish modal from non-modal phonation in this data set, and argue that noise measures like Cepstral Peak Prominence (CPP) are more robust to phonation differences in corpora with more variability.

Describing, listening and teaching questions and orders´ intonation in mexican, caribbean and castilian spanish to brazilian learners

The study intends to present results obtained from a methodology of intonation teaching, based on metacognition assumptions (Flavell, 1976; Gombert, 1992; Ribeiro, 2003). The methodology has five stages labelled as: 1 - learner's awareness of the transfering their mother tongue (LM) into the target foreign language (LEA); 2 - description of the intonational contour of total and partial interrogative utterances; 3-perception; 4-mimetic repetition; 5 - oral production based on careful listening. According to Cortés (2002), intonation is essential to convey messages in an appropriate way, showing an expressive meaning - besides syntactic-semantic aspects- and the meaning of the utterances. According to PINTO (2009), the E / LE learner transfers the intonational pattern of the LM when he or she in he/her LEA. How to teach intonation? This author proposed and applied a methodology of describing and teaching Spanish intonation to undergraduate students of Letras - Portuguese / Spanish, from a public university in Rio de Janeiro. For this study, 18 utterances were written in Spanish as a foreign language, based on the Interactive Atlas of the Spanish intonation. The corpus is composed of 9 utterances in Brazilian Portuguese as L1, both compared to 9 utterances in Spanish as L1, totaling 36 statements. For such data collection, the informants received CD's composed of yes/no questions, confirmatory questions and declarative imperatives of request in the dialect chosen by each student- the Castilian, the Caribbean and the Mexican. Recordings were made at two specific moments: before and after exposure to our methodology of description and intonation teaching. After teaching, we described the utterances phonetically and analyzed them with the aid of PRAAT software and following the Autosegmental Metric (AM) model. After this analysis, we compared the implementation of F0 and the tonal accent of these utterances, in order to check if the learner's accomplishment of the prosodic system undergoes alteration after the work of awareness, description, perception, mimetic repetition and production stuck in attention. We observed in our results that learners, before being submitted to our methodological proposal of intonation description and teaching, transfer characteristic of Brazilian Portuguese contours into the production of Spanish as a foreign language for all listed varieties. This fact confirmed the transfer process. However, when learners are submitted to our methodological proposal, they mostly implement the expected outlines according to the chosen varieties.

Recognition of micro-objects with adaptive models of image processing in a parallel computing environment

Scientific and methodological foundations for optimizing the processing of micro-objects, in particular, pollen grains, have been developed on the basis of models and methods of preliminary information processing with mechanisms for filtering, identifying, and using textural, specific characteristics, and geometric features of images. The efficiency of image filtering mechanisms is investigated based on the use of statistical control rules, adaptive two-threshold control, trend functions, and control of the contour description error, traditional detectors of random noise, and interference. The mechanisms of image identification using biquadratic, orthogonal algebraic polynomials, 4th order splines are proposed. A technique for optimizing image identification by finding a function by its integrals in a family of straight lines based on the Mellin and Fourier transforms has been developed. Combined models with mechanisms for smoothing reference points, segmentation of contours, filtering with detection of changes in dynamics, optimization by the conjugate gradient method, time relaxation have been built. A software complex for the recognition and classification of images of pollen grains in C ++ has been developed, which includes testing identification algorithms based on the Daubechies spline function, orthogonal algebraic polynomials of 4, 8 orders in the parallel computing environment “CUDA”.

Edge denoising of art illustration image based on contour feature recognition

In order to solve the problems of low precision and long time-consuming in traditional edge denoising methods of art illustration image, an edge denoising method of art illustration image based on contour feature recognition is proposed. The edge of art illustration image is segmented, and the feature target value and background value are extracted. By calculating the same degree of edge data in each kind of features, the edge feature classification of art illustration image is realised with the help of naive Bayes classification matrix. The edge noise region of the image is determined, and the wavelet descriptor in the contour descriptor is used to smooth the edge noise region of the art illustration image to complete the edge denoising of the art illustration image. Experimental results show that the edge denoising accuracy of the proposed method is about 95%, and the denoising time is only 2.1 s.