Shape Descriptors Research Articles

Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrieval

Erstwhile shape description schemes lack primarily in establishing trade-offs with accuracy and computational load. Accordingly, a lightweight shape descriptor offering precise definition and compaction of high-frequency features is contributed in this paper using a simple geometrical shape for localization and shape characterization. Initially, the input image is octagonally tessellated and triangularly decomposed into sub-regions whose side-wise differences are evaluated and subjected to second-order differentiation to produce three high-frequency values representing triangle corners. The resultant is processed by the law of sines to yield localized shape features exhibiting congruence and is reiterated on the residual regions, followed by a novel octal encoding scheme encompassing maximal variations in the localized regions. The resulting features are globally fabricated into shape histograms in a non-overlapping manner representing the shape vector. This scheme validated on widely popular benchmark shape datasets demonstrates superior retrieval and recognition accuracies greater than 93% which is lacking in its competitors.

Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms

This study aims to develop several novel machine learning (ML) evolutionary algorithms for the prediction of small strain shear modulus (Gmax) of clean sands and sand-fines binary mixtures. To this end, five key features of isotropic confining pressure (p), void ratio (e), uniformity coefficient (Cu), particle shape descriptor (ρ), and non-plastic fines content (FC) are adopted as the inputs to artificial neural network (ANN) models as well as genetic programming (GP) algorithm so as to render the maximum shear modulus of granular soils as the output. Accordingly, a comprehensive dataset containing 1055 Gmax data points is exploited to develop ML simulations. The validity of ML-based models in estimating the Gmax of clean sands and sand-silt mixtures is rigorously examined through various statistical indices and measurement criteria. The results show that a novel ML model utilizing ANN-Levenberg Marquardt (LM) in conjunction with an evolutionary optimization method named Success History-based Adaptive Differential Evolution with Linear population size reduction (LSHADE) is capable of predicting Gmax data with a very high precision rendering R2 values of 0.9833, 0.9841, 0.9802, and 0.9835 for the whole, training, validation, and test datasets, respectively. Meanwhile, using the well-established GP algorithm, a new practical model is proposed to predict the Gmax of clean sands and sand-fines mixtures containing non-cohesive silt inclusion with R2 values of 0.9323, 0.9351, and 0.9312 for the whole, training, and test datasets, respectively. Finally, the proposed models of ANN-LSHADE-LM and GP are shown to be appreciably superior, in terms of accuracy, to all commonly used empirical correlations in the literature for Gmax estimation.

Pancreaticoduodenectomy with superior mesenteric artery first-approach combined total meso-pancreas excision for periampullary malignancies: A high-volume single-center experience with short-term outcomes.

Pancreaticoduodenectomy (PD) is the only radical treatment for periampullary malignancies. Superior mesenteric artery (SMA) first approach combined with total meso-pancreas (MP) excision was conducted to improve the oncological results. There has not been any previous research of a technique that combines the SMA first approach and total MP excision with a detailed description of the MP macroscopical shape. We prospectively assessed 77 patients with periampullary malignancies between October 2020 and March 2022 (18 months). All patients had undergone PD with SMA first approach combined total MP excision. The perioperative indications, clinical data, intra-operative index, R0 resection rate of postoperative pathological specimens (especially mesopancreatic margin), postoperative complications, and follow-up results were evaluated. The median operative time was 289.6 min (178-540 min), the median intraoperative blood loss was 209 mL (30-1,600 mL). Microscopically, there were 19 (24.7%) cases with metastatic MP, and five cases (6.5%) with R1-resection of the MP. The number of lymph nodes (LNs) harvested and metastatic LNs were 27.2 (maximum was 74) and 1.8 (maximum was 16), respectively. Some (46.8%) patients had pancreatic fistula, but mostly in grade A, with 7 patients (9.1%) who required re-operations. Some 18.2% of cases developed postoperative refractory diarrhea. The rate of in-hospital mortality was 1.3%. The PD with SMA first approach combined TMpE for periampullary malignancies was effective in achieving superior oncological statistics (rate of MP R0-resection and number of total resected LNs) with non-inferior short-term outcomes. It is necessary to evaluate survival outcomes with long-term follow-up.

A Discontinuous Galerkin Finite Element Method with Physical Modal Basis for the Neutron Transport Equation on Arbitrary Polygonal Meshes

The deterministic methods are efficient for solving the neutron transport equation (NTE), but suffer from discretization errors. The increasingly complex geometric models make spatial discretization errors the primary source of discretization errors. Considering that spatial discretization errors come from inaccurate geometric shape descriptions and low-accuracy numerical schemes, this paper develops a Discontinuous Galerkin Finite Element Method for the NTE on unstructured polygonal meshes to reduce spatial discretization errors. In this method, the physical modal basis is adopted to handle the polygonal mesh and to achieve high-order accuracy in a uniform and efficient way. The numerical results of various fixed-source and k-eigenvalue benchmarks demonstrate that the method developed in this paper can give accurate solutions on polygonal meshes with high convergence rates.

Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

Abstract For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological, non physiological and sensing system parameters on the shape of the simulated sEMG signal.

Relationship between white matter lesion coalescence and plasma cystatin C in an ethnically diverse population

AbstractBackgroundBrain and kidney share anatomical similarities in terms of the small vessels serving their irrigation. Previous work have shown associations between markers of cerebral small vessel disease (CSVD) e.g. white matter hyperintensities (WMH) and markers of renal function e.g. cystatin C. Few studies have however investigated the relationship between renal function and more detailed descriptions of lesion shape and distributionMethod768 individuals from the tri‐ethnic SABRE cohort underwent brain MRI including T1w and FLAIR sequences. WMH were automatically segmented and an overall measure of coalescence was derived from the lesion map ranging from 0 (only punctuate lesions) to 4 (high level of coalescence). Regional coalescence measures according to distance from the ventricles were further derived in 4 equidistant layers. Clinic blood samples were available for 731 individuals (45% female, mean age 71.3 (SD 6.4) years) including from 311 European; 172 African Caribbean; and 248 South Asian ethnicity. Blood measures include plasma cystatin C, a marker of glomerular filtration rate, white blood cell count (WBC) and HbA1c. Associations between cystatin C and coalescence score or lesion volume expressed as percentage of total intracranial volume were investigated using robust linear regression adjusting for age, ethnicity, sex, HbA1c and WBC; all factors known to be associated with cystatin C levels and WMH.ResultCystatin C was positively associated with male sex, WBC and HbA1c, and differed across ethnicities being highest in South Asians and lowest in African Caribbeans. After adjusting for potential confounders, cystatin C was positively associated with overall coalescence score (b = 0.026 [0.004;0.047], p = 0.018) despite unconvincing evidence of an association with overall lesion volume (b = 0.003 [‐0.0003; 0.007], p = 0.066). Associations between cystatin C and coalescence score were stronger in the South Asian group (b = 0.066 [0.027;0.105], p interaction = 0.001). Associations between cystatin C and coalescence score were similar across different layers.ConclusionShape patterns of WMH, rather than overall WMH volume, may provide a more sensitive measure to assess the relationship with markers of small vessel disease in the kidneys.

Relationship between white matter lesion coalescence and plasma cystatin C in an ethnically diverse population.

AbstractBackground: Brain and kidney share anatomical similarities in terms of the small vessels serving their irrigation. Previous work have shown associations between markers of cerebral small vessel disease (CSVD) e.g. white matter hyperintensities (WMH) and markers of renal function e.g. cystatin C. Few studies have however investigated the relationship between renal function and more detailed descriptions of lesion shape and distributionMethod768 individuals from the tri‐ethnic SABRE cohort underwent brain MRI including T1w and FLAIR sequences. WMH were automatically segmented and an overall measure of coalescence was derived from the lesion map ranging from 0 (only punctuate lesions) to 4 (high level of coalescence). Regional coalescence measures according to distance from the ventricles were further derived in 4 equidistant layers. Clinic blood samples were available for 731 individuals (45% female, mean age 71.3 (SD 6.4) years) including from 311 European; 172 African Caribbean; and 248 South Asian ethnicity. Blood measures include plasma cystatin C, a marker of glomerular filtration rate, white blood cell count (WBC) and HbA1c. Associations between cystatin C and coalescence score or lesion volume expressed as percentage of total intracranial volume were investigated using robust linear regression adjusting for age, ethnicity, sex, HbA1c and WBC; all factors known to be associated with cystatin C levels and WMH.ResultCystatin C was positively associated with male sex, WBC and HbA1c, and differed across ethnicities being highest in South Asians and lowest in African Caribbeans. After adjusting for potential confounders, cystatin C was positively associated with overall coalescence score (b = 0.026 [0.004;0.047], p = 0.018) despite unconvincing evidence of an association with overall lesion volume (b = 0.003 [‐0.0003; 0.007], p = 0.066). Associations between cystatin C and coalescence score were stronger in the South Asian group (b = 0.066 [0.027;0.105], p interaction = 0.001). Associations between cystatin C and coalescence score were similar across different layers.ConclusionShape patterns of WMH, rather than overall WMH volume, may provide a more sensitive measure to assess the relationship with markers of small vessel disease in the kidneys.

Deep learning based automatic detection algorithm of Frank’s sign and its application

AbstractBackgroundFrank’s sign (FS) is a diagonal crease in the ear lobe extending from the tragus across the lobule to the rear edge of the auricle. Many of previous research have studied that FS is associated with the risk of cardiovascular, cerebrovascular diseases, cognitive impairment and aging. However, existing studies have lack of work in the application of FS. Meanwhile, cutting edge deep learning segmentation techniques allow to build large‐scale trainable models that have the capacity to learn the optimal features with great speed and accuracy. This study is the first to analyze 3D Nifti FS dataset by applying a deep learning approach and developing the automatic detection algorithm of FS (ADAFS).MethodWe used 700 brain MRI dataset and manually segmented FS masks of 550 older adults with FS. 400 out of 550 datasets were used into the developing ADAFS. Rest of them and 150 older adults without FS served to validate the ADAFS. The preprocessed 3D datasets were fed into the candidate models including 3D U‐net, 3D Attention U‐net, 3D Nested U‐net, and 3D USE‐net in order to train ADAFS (figure 1). We adapted 5‐fold cross‐validation test which can estimate the general effectiveness of an algorithm on an independent dataset. Furthermore, we classified the FS‐detected mask in 4 cases; left lateral, right lateral, bi‐lateral and no FS.ResultOn development of ADAFS, Nested U‐net out of 4 different models showed the highest average dice similar coefficient (DSC; 0.714 ± 0.160) across test folds. Given validating the developed ADAFS, the multi‐label classification and segmentation results of receiver operating characteristic curve (ROC) analysis were below: accuracy = 0.907, average area under ROC curve = 0.940, and DSC = 0.736 ± 0.172.ConclusionADAFS may support the faster decisions of FS presence, and considerably similar FS segmentation performance. This may serve a variety of FS shape descriptors and further study including an association of cognitive aspects and them.

Decentralized Coordination of a Multi-UAV System for Spatial Planar Shape Formations

Motivated by feedback from firefighters in Normandy, this work aims to provide a simple technique for a set of identical drones to collectively describe an arbitrary planar virtual shape in a 3D space in a decentralized manner. The original problem involved surrounding a toxic cloud to monitor its composition and short-term evolution. In the present work, the pattern is described using Fourier descriptors, a convenient mathematical formulation for that purpose. Starting from a reference point, which can be the center of a fire, Fourier descriptors allow for more precise description of a shape as the number of harmonics increases. This pattern needs to be evenly occupied by the fleet of drones under consideration. To optimize the overall view, the drones must be evenly distributed angularly along the shape. The proposed method enables virtual planar shape description, decentralized bearing angle assignment, drone movement from takeoff positions to locations along the shape, and collision avoidance. Furthermore, the method allows for the number of drones to change during the mission. The method has been tested both in simulation, through emulation, and in outdoor experiments with real drones. The obtained results demonstrate that the method is applicable in real-world contexts.

Probing conformational properties of conjugated polymers in dilute solutions under variable solvent quality via coarse‐grained modeling

AbstractConjugated polymers (CPs), characterized by rigid conjugation backbones and flexible peripheral side chains, hold significant promise in various organic optoelectronic applications. In this study, we employ coarse‐grained molecular dynamics (CG‐MD) simulations to investigate the intricate interplay of solvent quality, temperature, and chain architecture (e.g., side‐chain length and molecular mass) on the conformational behaviors of CPs in dilute solutions. Our research uncovers distinctive conformational behaviors under varying solvent conditions, highlighting the versatile nature of polymer chains, which can adopt extended configurations in good solvents and transition to aggregated states in poor solvents. Additionally, the mass scaling exponent , a robust structural descriptor, consistently described CPs behavior across diverse architectures and solvent conditions. Furthermore, our study shows that a CP with longer side‐chain exhibits improved solubility, which is further confirmed by experimental observations. Moreover, our analysis of the shape descriptor provided valuable insights into the symmetry and dimensionality of CPs under varying solvent conditions. These findings offer a comprehensive understanding of conformational behaviors of CPs in dilute solution, which are helpful in guiding the conformational design of polymer for specific applications.

DeepSSM: A blueprint for image-to-shape deep learning models

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

Proportions of conical motifs: Optimal packing via the spherical image.

We present a scheme for calculating the shape of two well-known conical motifs: the d-Cone and the e-Cone. Each begins as a thin, flat disk, before buckling during loading into a deformed shape with distinctive, asymmetrical conical features and a localised apex. Various deformed equilibrium models rightly assume a developable shape, with a particular focus on determining how much of the disk detaches from how it is supported during buckling; they are, nevertheless, extensively curated analytically, and must confront (some, ingeniously) the question of singular, viz., infinite properties at the conical apex. In this study, we find an approximate description of shape that reveals the extent of detachment, from an analogous mobile vertex that packages optimally according to its constraints. To this end, we further develop the usage of Gauss's Mapping and the associated spherical image, which has been used previously, but only to confirm known properties of deformed shape. Despite the simplicity of our approach, remarkably good predictions are availed, perhaps because such problems of extreme deformation are geometrically (rather than equilibrium) dominated.

Walk in views: Multi-view path aggregation graph network for 3D shape analysis

The graph-based multi-view methods have achieved state-of-the-art results in 3D shape analysis tasks by taking advantage of graph convolutional networks (GCN) to process discrete data. However, the homogeneity of the traditional GCN aggregation operator leads to a problem in aggregating neighborhood information, i.e., if several views have the same neighbors, the same node embeddings will be generated, resulting in feature redundancy. To address this problem, we propose a Multi-view Path Aggregation Graph Network (MVPNet) for 3D shape analysis, which aims to extract a particular path from a graph composed of multiple views and aggregate it into an effective 3D shape descriptor. Specifically, we first extract a path in the graph through dynamic walking, and update the path status while searching for new nodes during the walking. Then we embed the position information of the nodes in the order of the nodes in the path. Finally, we propose to aggregate the features of a path employing a Path Transformer that is capable of handling ordered sequences. A path contains richer semantic and structural information than a traditional subgraph. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on three benchmark datasets, namely ModelNet, ShapeNetCore55 and MCB, and these experiments prove that the method outperforms the current methods in 3D shape classification and retrieval tasks.

Empirical Shape-Based Estimation of Settling Microplastic Particles Drag Coefficient

Microplastics (MP) are found in nature in various forms. The transport and settling process of MP particles in water streams is highly influenced by particle size and shape. In order to investigate the impact of particle shape, an experimental analysis of settling behavior of flat square particles and 3D cubic particles in flowing water was conducted. The analysis is based on tracking the settling trajectories of particles of various size and using this information for calculating the drag coefficient cd in relation to the Reynolds number Re in the range of 80 to 280 for cubes and 50 to 110 for flat particles. The results were correlated with two shape parameters, sphericity ϕ and a newly proposed shape descriptor dimensionality δ. The final output is a model for particle shape-based cd estimation, mainly serving as an illustration of the potential of the proposed methodology.

A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids

The settlement drag coefficient of irregular particles in power-law fluids is a crucial parameter in the field of petroleum engineering. However, the irregular shape of the particle and the non-Newtonian rheological properties of the fluid make it challenging to predict the settlement drag coefficient. In this study, the spherical and irregular particle sedimentation processes in power-law fluids have been analyzed using a visual device and a high-speed camera system. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of 114 spherical particle experimental results, and a prediction model of the drag coefficient of spherical particles in the power-law fluid was established with a mean relative error of 3.85%. On this basis, considering the influence of geometric shape on the law of particle sedimentation, a new irregular particle sedimentation resistance coefficient model in power-law fluid is established via the incorporation of the parameter circularity of 2D shape description c into the spherical particle sedimentation resistance coefficient predictive model. The parameters in the new irregular particle sedimentation resistance coefficient predictive model can be obtained via nonlinear data fitting of the 211 groups of irregular particles using experimental results in the power-law fluid. The model has high prediction accuracy for the drag coefficient of irregular particles in power-law fluid, with a mean relative error of 4.47, and expands the scope of engineering applications, which is of great significance for fracturing scheme design and wellbore cleaning.

ESDTW: Extrema-based shape dynamic time warping

As a nonlinear distance measurement, Dynamic time warping (DTW) is widely used to solve the alignment problem of time series. However, due to local distortion of signal, noise interference, and high computational cost, DTW and its variants present some limitations in terms of classification accuracy or speed. In this paper, we develop the extrema-based shape dynamic time warping (ESDTW) for the purpose of fast and accurate alignment of time series. The proposed ESDTW introduces local extrema to represent the original time series, and further uses DTW to align the descriptors of extrema shape. By considering the location information of local extrema, the proposed measure avoids unreliable time series alignment caused by local maxima and minima separately in LEDTW. The experiments on some artificially simulated pairs of aligned sequences demonstrate that ESDTW can obtain more accurate warping paths than comparison methods. Moreover, combining with the nearest neighbor classifier, ESDTW achieves higher classification accuracy than other distance measures on the 84 UCR time series datasets. We also confirm significant computational efficiency gains on long time series.

Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors

Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets.

Crystal plasticity model for creep and relaxation deformation of OFP copper

ABSTRACT We demonstrate a dislocation density-based crystal plasticity (CP) model approach for simulating mesoscale deformation and damage. The existing CP framework is extended to be compatible with the oxygen-free phosphorous copper microstructure that is the focus of this study. The key aim is to introduce relevant plastic deformation mechanisms and to develop a failure model capable of depicting creep damage in the material. The effect of local variations in material is evaluated, and the model response is compared with experiments and characterisation. The basis of this work is CP material modelling, including grain orientation and size, obtained using electron backscatter diffraction and experimental test data of real relaxation test specimens. This will yield a realistic description of texture and grain shape and, ultimately, accurate stress–strain response at the microstructural level for further evaluation of performance with respect to material creep(−fatigue) damage.

Predicting 3D particles shapes based on 2D images by using convolutional neural network

In order to accurately determine the three-dimensional (3D) shape of granular particles, one laborious approach is to employ X-ray microtomography and image processing techniques. As an alternative, in this study a 3D convolutional neural network (3D-CNN) methodology is presented to generate realistic 3D models of particles from a number of distinct two-dimensional (2D) projections. Initially, 3D synthetic particles with the targeted statistical distributions of the relevant geometric parameters, such as sphericity, elongation, aspect ratio, and flatness, were randomly generated using Fourier shape descriptors (FSDs). The database based on the statistical distributions of geometric parameters of Avicel® PH 200, obtained through dynamic image analysis. Following that, projections from multiple angles of 2D particle images were extracted for model training. By using a convolutional neural network (CNN) the shape of the particles was predicted, which involves the following sequential steps: a) model training on 2D images; b) a decoder is built to generate the 3D voxel model after an encoder extracts the properties of 2D images; c) a marching cube algorithm that converts the particle 3D voxel model into a mesh model; d) the accuracy of the prediction is evaluated by comparing the geometric parameters of the prediction models that were generated by using FSDs. The methodology was validated using a database of 3D synthetic particles with targeted statistical distributions of geometric parameters, such as sphericity, elongation, aspect ratio, and flatness. The mean absolute percentage error (MAPE) of the AI model for these parameters was 2.24%, 7.01%, 10.93%, and 7.67%, respectively. The 3D-CNN methodology has the potential for broad applications in various fields, such as rock and mineral analysis, battery materials, pharmaceuticals, and space exploration, offering a practical and affordable solution for shape analysis of relevant materials. It can significantly improve the efficiency and accuracy of numerical simulations and analyses, leading to new insights and innovations in a wide range of fields.

Fully spherical 3D datasets on sedimentary particles: Fast measurement and evaluation

AbstractRecently it became increasingly evident that the statistical distributions of size and shape descriptors of sedimentary particles reveal crucial information on their evolution and may even carry the fingerprints of their provenance as fragments. However, to unlock this trove of information, measurement of traditional geophysical shape descriptors (mostly detectable on 2D projections) is not sufficient; fully spherical 3D imaging and mathematical algorithms suitable to extract new types of inherently 3D shape descriptors are necessary. Available 3D imaging technologies force users to choose either speed or full sphericity. Only partial morphological information can be extracted in the absence of the latter (e.g., LIDAR imaging). In the case of fully spherical imaging, speed was proved to be prohibitive for obtaining meaningful statistical samples, and inherently 3D shape descriptors were not extracted. Here we present a new method by complementing a commercial, portable 3D scanner with simple hardware to quickly obtain fully spherical 3D datasets from large collections of sedimentary particles. We also present software for the automated extraction of 3D shapes and automated measurement of inherently 3D-shape properties. This technique allows for examining large samples without the need for transportation or storage of the samples, and it may also facilitate the collaboration of geographically distant research groups. We validated our software on a large sample of pebbles by comparing previously hand-measured parameters with the results of automated shape analysis. We also tested our hardware and software tools on a large pebble sample in Kawakawa Bay, New Zealand.