Shape Classification Research Articles

Variation on a Zernike wavefront sensor theme: Optimal use of photons

Aims. The Zernike wavefront sensor (ZWFS) is a concept belonging to the wide class of Fourier-filtering wavefront sensors (FFWFSs). The ZWFS is known for its extremely high sensitivity and low dynamic range, which makes it a unique sensor for second stage adaptive optics systems or quasi-static aberration calibration sensors. This sensor is composed of a focal plane mask made of a phase shifting dot that is fully described by two parameters: its diameter and depth. We aim to improve the performance of this sensor by changing the diameter of its phase shifting dot. Methods. We begin with a general theoretical framework, providing an analytical description of the FFWFS properties. We then predict the expected ZWFS sensitivity for different configurations of dot diameters and depths. The analytical predictions are then validated with end-to-end simulations. From this, we propose a variation of the classical ZWFS shape that exhibits extremely appealing properties. Results. We show that the ZWFS sensitivity can be optimized by modifying the dot diameter and it can even reach the optimal theoretical limit, though with the trade-off of low spatial frequency sensitivity. As an example, we show that a ZWFS with a 2 λ/D dot diameter (where λ is the sensing wavelength and D the telescope diameter), hereafter called a Z2WFS, exhibits a sensitivity twice higher than the classical 1.06 λ/D ZWFS for all the phase spatial components except for tip-tilt modes. Furthermore, this gain in sensitivity does not impact the dynamic range of the sensor, and the Z2WFS exhibits a similar dynamical range as the classical 1.06 λ/D ZWFS. This study opens the path to the conception of a diameter-optimized ZWFS.

Evolutionary design of composite structures for thermal conductance and strength

This is a study of the evolving design of a composite material with inserts that are free to be configured and distributed through the base material. Several classes of shapes of inserts are considered competitively: parallel plates, staggered plates, forks, chains and diamonds. The architecture evolves in two directions (with two objectives), high thermal conductance and mechanical strength (stiffness). Relative to the base material, the inserts have higher thermal conductivity and lower modulus of elasticity. As a consequence, better architectures emerge from the tradeoff between thermal conductance and strength, for example, chain and diamond shaped inserts. The two-objective performance further improves when the inserts are distributed nonuniformly through the base material, such that the high conductivity inserts are not placed in the regions with the highest stresses.

Adaptive level set method applied to drag minimization problems constrained by Stokes equations

Abstract We present a level set based adaptive mesh method for solving the drag minimization problem of incompressible flow governed by Stokes equations. Shape sensitivity analysis of the cost functional is derived. During the optimization process, two levels of meshes are adopted. A uniform coarse mesh for evolving the level set function is defined over the whole computational domain. The level set function also serves as the refinement indicator with an easy strategy. The coarse mesh that contains the interfaces then further divided into a uniform fine mesh and the computation is mainly near the interfaces. Therefore, the computational cost is significantly reduced compared with the uniform mesh over the whole domain that achieves the same resolution. Above all, the value of the shape derivative on the boundary can be obtained implicitly, which is a very difficult task in classical optimal shape design problems. The overall optimization procedure is presented and verified with benchmark examples.

Analysis of segmented elderly women’s lower bodies using 3D-LOOK scan data and virtual representation

To address the clothing needs of an aging society, this study developed a scale using three-dimensional (3D) scan data that determine elderly women’s lower body shapes to improve the way garments fit the elderly. Body type elements that play an important role in garment fit were identified and five body type elements were selected for use in this study. A stepwise discriminant analysis using 176 dimensions was performed to extract parameters reflecting body shape features, resulting in 37 parameters. A scale for determining body shapes was developed using the discriminant function equation. This study differs from existing studies on body shape classification in that we determined the diverse body shape features of individuals by extracting the lower body type elements related to garment fit. This study demonstrated an organic relationship among lower body types, where a greater posterior pelvic tilt was associated with a protruding lower abdomen, flat buttocks, and an o-type frontal leg shape. The significance of this study lies in the extraction of 3D parameters that reflect the body shape features of elderly women. Such 3D parameter data can be used to create personal virtual bodies in online shopping malls in the future.

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

We propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. Our key idea is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HRNet to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, Our network is concatenated with simple task-specific back-ends and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods. For fine-grained shape segmentation on the PartNet dataset, our method even surpasses existing supervised methods by a large margin.

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types. Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.

Red blood cell phenotyping from 3D confocal images using artificial neural networks.

The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.

The Erythrocyte Sedimentation Rate and Its Relation to Cell Shape and Rigidity of Red Blood Cells from Chorea-Acanthocytosis Patients in an Off-Label Treatment with Dasatinib.

Background: Chorea-acanthocytosis (ChAc) is a rare hereditary neurodegenerative disease with deformed red blood cells (RBCs), so-called acanthocytes, as a typical marker of the disease. Erythrocyte sedimentation rate (ESR) was recently proposed as a diagnostic biomarker. To date, there is no treatment option for affected patients, but promising therapy candidates, such as dasatinib, a Lyn-kinase inhibitor, have been identified. Methods: RBCs of two ChAc patients during and after dasatinib treatment were characterized by the ESR, clinical hematology parameters and the 3D shape classification in stasis based on an artificial neural network. Furthermore, mathematical modeling was performed to understand the contribution of cell morphology and cell rigidity to the ESR. Microfluidic measurements were used to compare the RBC rigidity between ChAc patients and healthy controls. Results: The mechano-morphological characterization of RBCs from two ChAc patients in an off-label treatment with dasatinib revealed differences in the ESR and the acanthocyte count during and after the treatment period, which could not directly be related to each other. Clinical hematology parameters were in the normal range. Mathematical modeling indicated that RBC rigidity is more important for delayed ESR than cell shape. Microfluidic experiments confirmed a higher rigidity in the normocytes of ChAc patients compared to healthy controls. Conclusions: The results increase our understanding of the role of acanthocytes and their associated properties in the ESR, but the data are too sparse to answer the question of whether the ESR is a suitable biomarker for treatment success, whereas a correlation between hematological and neuronal phenotype is still subject to verification.

InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification

We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from microscopic images arising in histology and neuroscience. The framework is based on a novel shape descriptor of closed contours in 2D and 3D. In 2D, it relies on a geodesically uniform resampling of discrete curves to compute unsigned curvatures at vertices and edges based on discrete differential geometry. Our descriptor is, by design, invariant under translation, rotation, and parameterization. We achieve the latter invariance under parameterization shifts by using elliptic Fourier analysis on the resulting curvature vectors. Uniform scale-invariance is optional and is a result of scaling curvature features to z-scores. We further augment the proposed descriptor with feature coefficients obtained through sparse coding of the extracted cellular structures using K-sparse autoencoders. For the analysis of 3D shapes, we compute mean curvatures based on the Laplace-Beltrami operator on triangular meshes, followed by computing a spherical parameterization through mean curvature flow. Finally, we compute the Spherical Harmonics decomposition to obtain invariant energy coefficients. Our invariant descriptors provide an embedding into a fixed-dimensional feature space that can be used for various applications, e.g., as input features for deep and shallow learning techniques or as input for dimension reduction schemes to provide a visual reference for clustering shape collections. We demonstrate the capabilities of our framework in the context of visual analysis and unsupervised classification of 2D histology images and 3D nuclear envelopes extracted from serial section electron microscopy stacks.

Tau forms oligomeric complexes on microtubules that are distinct from tau aggregates

Tau is a microtubule-associated protein, which promotes neuronal microtubule assembly and stability. Accumulation of tau into insoluble aggregates known as neurofibrillary tangles (NFTs) is a pathological hallmark of several neurodegenerative diseases. The current hypothesis is that small, soluble oligomeric tau species preceding NFT formation cause toxicity. However, thus far, visualizing the spatial distribution of tau monomers and oligomers inside cells under physiological or pathological conditions has not been possible. Here, using single-molecule localization microscopy, we show that tau forms small oligomers on microtubules ex vivo. These oligomers are distinct from those found in cells exhibiting tau aggregation and could be precursors of aggregated tau in pathology. Furthermore, using an unsupervised shape classification algorithm that we developed, we show that different tau phosphorylation states are associated with distinct tau aggregate species. Our work elucidates tau's nanoscale composition under nonaggregated and aggregated conditions ex vivo.

Simulation and Experimental Research on Transverse Random Vibration of Typical Valve for Launch Vehicle

In order to effectively suppress the transverse random vibration of a class of irregular shape and structure valve for launch vehicle in the ground test, adopting the method of combining finite element simulation and experiment, the influence of different structure tools on the transverse vibration of the valve is investigated. Moreover, the impact of arrangement position of valve and semi-circular tool assembly on transverse vibration of valve is studied. Based on the analysis of the influence of tooling of different structures on the transverse random vibration of the valve, it is proposed that semi-circular fixture can effectively reduce the transverse vibration for this kind of valve. Furthermore, when placed in the center of vibration table or near the vibration source, can decrease transverse vibration of the valve remarkably.

Two-dimensional clusters of colloidal tetramers via droplet emulsion templating

In this report, using a kinetic Monte Carlo simulation method we considered a hierarchical assembly of planar square tetramers, constrained in a plane, via droplet evaporation. We found that colloidal tetramers undergo a structural transition from initial square-shape tetramers to diamond-like tetramers. A variety range of novel cluster configurations belongs to a particular class of geometrical shape, that has not been previously reported to the clusters of single colloids. Considering potential applications of colloidal clusters in electronics, photonic materials, our result could provide an effective way to control the formation of complex colloidal cluster structures.

Image processing and machine learning for telehealth craniosynostosis screening in newborns.

The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was also assessed. Cranial shape classification software accuracy was compared to that of blinded craniofacial specialists using a data set of open-source (n = 40) and retrospectively collected newborn orthogonal top-down cranial images, with or without additional facial views (n = 339), culled between April 1, 2008, and February 29, 2020. Based on image quality, midface visibility, and visibility of the cranial equator, 351 image sets were deemed acceptable. Accuracy, sensitivity, and specificity were calculated for the software versus specialist classification. Software agreement with optical craniometrics was assessed with intraclass correlation coefficients. The cranial shape classification software had an accuracy of 93.3% (95% CI 86.8-98.8; p < 0.001), with a sensitivity of 92.0% and specificity of 94.3%. Intraclass correlation coefficients for measurements of the cephalic index and cranial vault asymmetry index compared to optical measurements were 0.95 (95% CI 0.84-0.98; p < 0.001) and 0.67 (95% CI 0.24-0.88; p = 0.003), respectively. These results support the use of image processing-based neonatal cranial deformity classification software for remote screening of nonsyndromic craniosynostosis in a newborn population and as a substitute for optical scanner- or CT-based craniometrics. This work has implications that suggest the potential for the development of software for a mobile platform that would allow for screening by telemedicine or in a primary care setting.

중년 여성 복부 돌출 정도에 따른 토르소 형태 분류

중년 여성 복부 돌출 정도에 따른 토르소 형태 분류

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.

Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

Depending on the prevailing researches, the classification of face shapes could be deployed for numerous applications. This paper intends to develop a new method for face shape classification using intelligent approaches. The presented method includes three stages namely, (i) Face Detection (ii) Pre-processing (iii) Feature extraction (iv) Classification. The face detection process is used for identifying the most significant objects in the face, probably eyes, nose, etc. which is done using the Viola-Jones algorithm. Moreover, the pre-processing stage includes the Histogram Equalization (HE) model for enhancing the contrast of the image. The classification of face shapes is performed by a hybrid classifier that links Convolutional Neural Network (CNN) and Neural Network (NN). For performing the CNN-based classification, the images are directly given as input. On the other hand, NN-based classification requires features as input. Hence, the pre-processed image is again subjected to the feature extraction process, where the features are extracted using the Active Appearance Model (AAM) and the Active Shape Model (ASM). For reducing the length of extracted features, the optimal feature selection process is adopted, which is done by improved Grey Wolf Optimization (GWO) algorithm. As the main contribution, the features, number of hidden neurons in the convolutional layer of CNN, and training of NN (weight update) is optimally chosen by improved GWO so-called as Fitness Sorted Grey Wolf Update (FS-GU) model. Finally, the average of two outcomes from both CNN, and NN provides the classified five categories of face shapes like heart, oblong, oval, round, and square. The performance of the proposed classification model is finally validated by comparing over the conventional models by analyzing the relevant performance metrics.

Characterization of Particulates from Australian Underground Coal Mines

The re-identification of coal workers’ pneumoconiosis in Queensland in 2015 has prompted improvements in exposure monitoring and health surveillance in Australia. The potential consequences of excessive exposure to respirable dust may depend upon the size, shape and mineralogical classes of the dust. Technology has now advanced to the point that the dust characteristics can be explored in detail. This research collected respirable dust samples from four operating underground coal mines in Australia for characterization analysis using scanning electron microscopy (SEM) with energy dispersive X-ray (EDX). The research found multiple mineralogical classes present with their own particle size distributions. The variation between mines appears to have had a larger effect on particle size distribution than the differences in mining processes within individual mines. This may be due to variations in the geologic conditions, seam variation or mining conditions.

PALYNOLOGICAL INVESTIGATIONS ON SOME SELECTED BEE FORAGE PLANTS OF FAMILY FABACEAE USING LIGHT AND SCANNING ELECTRON MICROSCOPY FROM HIMACHAL PRADESH, INDIA

The study aimed to investigate and describe the pollen morphology of six nectariferous and polleniferous members of family fabaceae by using light and scanning electron microscope. Pollen grains of Albizia lebbeck, Bauhinia variegata, Caesalpinia decalpetala, Cassia fistula, Dalbergia sissoo and Robinia pseudoacacia were examined for the morphological characterization. The pollen grains were analysed in terms of aggregation, shape, shape class, size, aperture, polarity, symmetry, surface pattern and exine complexity. All the pollen had solitary grains, except Albizzia lebbeck having compound grains. The shape of pollen grains varies from circular-ovoid in Albizia lebbeck, triangular in Bauhinia variegata, oval in Caesalpinia decapetala, oval / round in Cassia fistula and triangular/round in Dalbergia sissoo and triangular/elongated oval Robinia pseudoacacia. The pollens were either prolate-spheroidal or oblate spheroidal. The size of pollen grains studied ranged from small, medium to large sized among different members of family fabaceae. Most of pollens observed were tricolporate, except pollens of Dalbergia sissoo which were tricolpate and triporate. Exine ornamentation include psilate/faveolate in Albizzia lebbeck, psilate in Dalbergia sissoo and Robinia pseudoacacia, straite –reticulate in Bauhinia variegata, reticulate-rugulate in Cassia fistula and Caesalpinia decapetala. All the pollens observed were isopolar and radially symmetrical. The purpose of this study is to increase knowledge of the aspects related to the variability and to characterize the pollen morphology of some members of family fabaceae and to evaluate the utility of pollen features in order to develop an additional alternative plant material identification tool. Moreover, such studies can be of utmost important to students, researchers, and beekeeping industry, horticulture and forest departments.

SCANNING ELECTRON MICROSCOPY STUDIES ON PLECTRANTHUS- AN IMPORTANT BEE FORAGE PLANT OF HIMACHAL PRADESH, INDIA

The present investigations were conducted on two important nectar and pollen yielding plants viz. Plecranthus coesta and Plecranthus rugosus from different agro-climatic zones of Himachal Pradesh. The bee forage plants were collected, identified, classified and diffentiating characters of two species were noted. Apart from this, pollen grains of P. coesta and P. rugosus were also studied using light and scanning electron microscope. The pollen grains were observed in terms of aggregation, shape, shape class, size, aperture, polarity, symmetry, surface pattern/exine complexity. Both P. coesta and P. rugosus had solitary medium sized, hexacolpate, isopolar and radially symmetric grains. However, the pollen grain of two species varies in shape and exine complexity. P. coesta pollens were round/ circular and were prolate, whereas in P. rugosus pollens were round/oval and subprolate-prolate. Variation was also found in the exine complexity of the two species. Exine was tectate, tectum was nearly reticulate / scabrate in P. coesta. But in P. rugosus and exine was either tectate or semitectate, tectum was reticulate, microreticulate.

Lateral migration of explosive hazards during maar eruptions constrained from crater shapes

Maar volcanoes are produced by subsurface phreatomagmatic explosions that can move vertically and laterally during an eruption. Constraining the distances that maar-forming explosions move laterally, and the number of relocations common to these eruptions, is vital for informing hazard scenarios and numerical simulations. This study uses 241 intact Quaternary maar crater shapes to establish global trends in size and spacing of explosion position relocations. Maar craters are sorted into shape classes based on the presence of uniquely identifiable combinations of overlapping circular components in their geometry. These components are used to recognize the minimum number of explosion locations responsible for observed crater shapes. Craters with unique solutions are then used to measure the size and spacing of the explosion footprints, the circular area of the largest crater produced by a single explosion of a given energy, that produce the crater shape. Thus, even in the absence of abundant observations of maar-type eruptions, the typical range, size and spacing of explosion positions are derived from maar crater shapes. This analysis indicates that most Quaternary maar eruptions involved at least three different explosion locations spanning distances of 200–600 m that did not always follow the trend of the dike feeding the eruption. Additional evaluation of larger maars, consistent with stratigraphic studies, indicates that centers of explosive activity, and thus the origin of ballistic and density current hazards, can move as many as twenty times during a maar-forming eruption. These results provide the first quantitative constraints on the scale and frequency of lateral migration in maar eruptions and these values can directly contribute to hazard models and eruption event trees in advance of future maar-type eruptions.