Density-based Approaches Research Articles

Assessing physical access to healthy food across United Kingdom: A systematic review of measures and findings.

BackgroundExisting research suggests that physical access to food can affect diet quality and thus obesity rates. When defining retail food environment (RFE) quantitatively, there is a little agreement on how to measure “lack of healthy food” and what parameters to use, resulting in a heterogeneity of study designs and outcome measures. In turn, this leads to a conflicting evidence base being one of the many barriers to using evidence in policy‐making.AimsThis systematic review aimed to identify and describe methods used to assess food accessibility in the United Kingdom (UK) to overcome heterogeneity by providing a classification of measures.Materials & MethodsThe literature search included electronic and manual searches of peer‐reviewed literature and was restricted to studies published in English between January 2010 and March 2021. A total of 9365 articles were assessed for eligibility, of which 44 articles were included in the review. All included studies were analysed with regards to their main characteristics (e.g., associations between variables of interest, setting, sample, design, etc.) and definition of RFE and its metrics. When defining these metrics, the present review distinguishes between a point of origin (centroid, address) from which distance was calculated, summary statistic of accessibility (proximity, buffer, Kernel), and definition of distance (Euclidean, network distance). Trends, gaps and limitations are identified and recommendations made for food accessibility research in UK.ResultsMultiple theoretical and methodological constructs are currently used, mostly quantifying distance by means of Euclidean and ring‐buffer distance, using both proximity‐ and density‐based approaches, and ranging from absolute to relative measures. The association between RFE and diet and health in rural areas, as well as a spatiotemporal domain of food access, remains largely unaccounted.DiscussionEvidence suggests that the duration of exposure may bear a greater importance than the level of exposure and that density‐based measures may better capture RFE when compared with proximity‐based measures, however, using more complex measures not necessarily produce better results. To move the field forward, studies have called for a greater focus on causality, individual access and the use of various measures, neighbourhood definitions and potential confounders to capture different aspects and dimensions of the RFE, which requires using univariate measures of accessibility and considering the overall context in terms of varying types of neighbourhoods.ConclusionIn order to render ongoing heterogeneity in measuring RFE, researchers should prioritise measures that may provide a more accurate and realistic account of people’s lives and follow an intuitive approach based on convergence of results until consensus could be reached on using some useful standards.

Alternating optimization of design and stress for stress-constrained topology optimization

Handling stress constraints is an important topic in topology optimization. In this paper, we introduce an interpretation of stresses as optimization variables, leading to an augmented Lagrangian formulation. This formulation takes two sets of optimization variables, i.e., an auxiliary stress variable per element, in addition to a density variable as in conventional density-based approaches. The auxiliary stress is related to the actual stress (i.e., computed by its definition) by an equality constraint. When the equality constraint is strictly satisfied, an upper bound imposed on the auxiliary stress design variable equivalently applies to the actual stress. The equality constraint is incorporated into the objective function as linear and quadratic terms using an augmented Lagrangian form. We further show that this formulation is separable regarding its two sets of variables. This gives rise to an efficient augmented Lagrangian solver known as the alternating direction method of multipliers (ADMM). In each iteration, the density variables, auxiliary stress variables, and Lagrange multipliers are alternatingly updated. The introduction of auxiliary stress variables enlarges the search space. We demonstrate the effectiveness and efficiency of the proposed formulation and solution strategy using simple truss examples and a dozen of continuum structure optimization settings.

Grain Density-Based Approaches to Predict the Mechanical, Thermal and Hygric Properties of Carbon-Negative Aggregate Concretes

The suitability of replacing mineral aggregate with carbon-negative ones mainly depends on the properties of the aggregates produced from waste recycling, reducing CO2 emissions. This study aimed to investigate the predictive approaches adapted to concrete mixtures where mineral aggregates are replaced by carbonated aggregates (at different substitution rates from 25 to 100% with aggregates of various origins). A large experimental campaign of aggregates and carbonated aggregate concretes highlighted their physical, mechanical, thermal and hygric properties and the influence of density and porosity of aggregates on these properties. Thanks to these results, predictive approaches were formulated to establish the main engineering properties: mechanical compressive strength, elasticity modulus, thermal conductivity, thermal mass capacity and hygric diffusivity. These empirical and analytical models were based on the density of aggregates. Maximum deviations of around 15% were obtained with the experimental data, highlighting the influence of grain density on carbonated aggregate concretes. These models could then be used to optimize the formulation of concrete mixtures with carbonated aggregates, replacing international standards adapted to mineral aggregates.

Extracting Shallow-Water Bathymetry from Lidar Point Clouds Using Pulse Attribute Data: Merging Density-Based and Machine Learning Approaches

To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML 1 ) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space.

TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams

In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data.

A New Method for Automatic Determining of the DBSCAN Parameters

Abstract Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

A new approach for instance selection: Algorithms, evaluation, and comparisons

Several approaches for instance selection have been put forward as a primary step to increase the efficiency and accuracy of algorithms applied to mine big data. The instance selection task scales indeed big data down by removing irrelevant, redundant, and unreliable data, which, in turn, reduces the computational resources necessary for completing the mining task. The local density-based approaches are recently acknowledged as feasible approaches in terms of reduction rate, effectiveness, and computation time metrics. However, these approaches endure low classification accuracy results compared with other approaches.In this manuscript, we propose a new layered and operational approach to address these limitations as well as advance the state-of-the-art by balancing among classification accuracy, reduction rate, and time complexity. We commence by designing a new algorithm (called GDIS) that selects most relevant instances using a global density and relevance functions. This enable us to consider a global view overall a data set to get a better classification accuracy results than current density-based approaches. We design another novel algorithm (called EGDIS), which maintains the effectiveness results of the GDIS algorithm while improving reduction rate results. Moreover, we compare our algorithms against three state-of-the-art algorithms to validate their performance. We develop a Java toolkit called ISTK on the top of the GDIS and EGDIS algorithms, the density-based approaches, and the state-of-the-art algorithms. We also develop a suitable user interface and its management and validation capabilities to ease-of-use and visualize results and data sets. We evaluate and test the performance of our algorithms in terms of four metrics (reduction rate, classification accuracy, effectiveness, and computation time) using twenty-four standard data sets and conduct an intensive set of experiments. The experimental results proved that the GDIS algorithm outperforms the density-based approaches in terms of classification accuracy and effectiveness, the EGDIS algorithm outperforms the density-based approaches in terms of reduction rate and effectiveness, and the GDIS and EGDIS algorithms outperform the state-of-the-art algorithms in terms of achieving a good results in both the effectiveness and computation time metrics. We finally test the scalability and compute experimentally the polynomial-time complexity of our algorithms.

Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets

Clustering is the most widely used unsupervised machine learning technique, having extensive applications in statistical analysis. We have multiple clustering algorithms available in theory and many more implementations available in practice. A bunch of literatures can be found focusing on the quality of clustering algorithms using various internal and external evaluation techniques. The motivation behind this work is the scarcity of literatures dealing with performance of clustering algorithms in terms of turnaround time. This paper summarizes the experimental analysis conducted on the performance of multiple clustering algorithms based on cardinality and dimensionality. The analysis is performed in R, which is a free and open source programming language mainly used for statistical computing. This work evaluates nine key algorithms coming under partitioning, hierarchical, density-based and model-based clustering approaches using different social media data sets. We captured performance trends of these algorithms in terms of turnaround time by varying the cardinality and dimensionality parameters of the data sets. Based on our experiments, CLARA, CLARANS, and k-means algorithms demonstrate best performances with varying cardinality. It is also observed that changes in dimensionality do not impact hierarchical clustering approaches whereas there is a positive influence on the execution time for partitioning, density-based and model-based clustering approaches.

Hybrid Models for Adaptive Allocation of Electricity for Households

In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.

Abstract A27: Deep learning for analysis of tumor-lymphocyte interactions in pancreatic ductal adenocarcinoma

Abstract The interaction of tumor, stroma, and immune cells in pancreatic ductal adenocarcinoma (PDAC) is complex and difficult to quantify in patient samples. Recently, deep learning algorithms have shown successes in identifying tumor and lymphocytes regions on whole-slide images derived from routinely collected histopathologic specimens. The Cancer Genome Atlas (TCGA) in particular has generated whole-slide images as well as paired molecular data, thus allowing for combined spatial and molecular analyses of tumor-lymphocyte interactions. We have previously highlighted this resource by computationally mapping tumor-infiltrating lymphocytes (TILs) on digital images across 13 tumor types. To achieve this, convolutional neural networks were trained on lymphocyte images annotated by expert pathologists and then used to detect spatial TIL patterns. This led to identification of four qualitative TIL pattern categories, which varied depending on tumor type as well as molecular immune subtype, demonstrating the potential of these spatial structures to provide further insights into tumor microenvironments and their relationship to overall survival. We have now extended this deep learning pipeline to include identification of tumor regions in PDAC, allowing study of TIL patterns in the context of their relative spatial localization to tumors. Using the deep learning algorithm to define the tumor region, we applied erosion and dilation operations to further capture the peritumoral region, the outer and inner regions of the tumor, as well as desmoplasia far from the tumor cells. We thus defined lymphocytes by their spatial localization as being internal, tumoral, peritumoral, or outer with these masks. We then used nearest-neighbor and density-based approaches to quantify TIL infiltration patterns with respect to tumor. These features vary significantly across the previously identified TIL patterns and may serve as additional parameters to define the microenvironment conditions in patient samples. Here we demonstrate that features extracted using our pipeline recapitulate canonical histologic properties. Using immune cell abundance estimates from gene expression generated by CIBERSORT, we find that samples with tumor TIL densities above median have more M1 macrophages, while those below median have more M2 macrophages. We also observe that slides with a higher peritumoral TIL density relative to tumoral TIL density have higher Treg fractions. Ongoing work on improving the resolution and cell specificity of our pipeline will allow us to ask more specific questions and permit higher granularity in linking clinical outcomes to spatial immune phenotypes. Citation Format: Soma Kobayashi, Han Le, Lucie Chrastecka, Rajarsi Gupta, Le Hou, Shahira Abousamra, Danielle Fassler, Kenneth R. Shroyer, Dimitris Samaras, Tahsin Kurc, Richard A. Moffitt, Joel H. Saltz. Deep learning for analysis of tumor-lymphocyte interactions in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A27.

Bi-material Topology Optimization Using Analysis Mesh-Independent Point-Wise Density Interpolation

This paper extends the independent point-wise density interpolation to the bi-material topology optimization to improve the structural static or dynamic properties. In contrast to the conventional elemental density-based topology optimization approaches, this method employs an analysis-mesh-separated material density field discretization model to describe the topology evolution of bi-material structures within the design domain. To be specific, the density design variable points can be freely positioned, independently of the field points used for discretization of the displacement field. By this means, a material interface description of relatively high quality can be achieved, even when unstructured finite element meshes and irregular-shaped elements are used in discretization of the analysis domain. Numerical examples, regarding the minimum static compliance design and the maximum fundamental eigen-frequency design, are presented to demonstrate the validity and applicability of the proposed formulation and numerical techniques. It is shown that this method is free of numerical difficulties such as checkerboard patterns and the “islanding” phenomenon.

NURBS hyper-surfaces for 3D topology optimization problems

A general topology optimization strategy is presented in this study for three-dimensional applications. The approach is based on the combination of a well-established density-based method and the Non-uniform rational basis spline (NURBS) hyper-surfaces formalism. The topology of the structure to be optimized can be embedded in a reference domain and it is opportunely described by means of a suitable NURBS hyper-surface. This choice implies several advantages. On the one hand, the number of design variables (control points and weights of the NURBS hyper-surface) is drastically reduced, when compared to classical density-based approaches, and it is unrelated to the mesh of the finite element model. On the other hand, the topology is described through a purely geometrical entity, while the mesh is utilized only to perform the computation of the relevant physical quantities for the problem at hand. A sensitivity analysis with respect to the new variables set is carried out. The effectiveness of the method as well as the effects of some discrete parameters tuning the NURBS hyper-surface shape are investigated on meaningful benchmarks and results are compared with those provided by the classical Solid Isotropic Material with Penalization approach.

Improved Method to Capture a Broader Array of High Density Lipoprotein Particles Including Those of Intestinal Origin

The isolation of high density lipoprotein (HDL) particles by density‐based methods using a density range of 1.063–1.21 g/mL is considered to be the gold standard. However, it is known that several HDL subclasses, including discoidal/nascent HDL particles, as well as ApoE‐containing, and intestinally derived ApoA‐IV‐containing HDL particles, are found at a higher density of up to 1.25 g/mL. In addition, using density‐based approaches alone it is not possible to purify HDL particles from a range of other particles such as extracellular vesicles, which overlap in density but not size. In this study, a novel method was optimized to isolate a broader array of HDL particles in a purified fraction that minimizes contamination by extracellular vesicles. The method utilizes density‐based sequential flotation ultracentrifugation followed by size exclusion chromatography with an FPLC system to isolate HDL fractions of different sizes, which were then analyzed for their cholesterol efflux capacity and protein content. The results demonstrate a higher yield of HDL and an increased level of HDL from earlier fractions using the 1.25 density cut‐off compared to the 1.21 density cut‐off. The cholesterol efflux capacity of early and late eluting fractions from the 1.25 density cut‐off method is increased, suggesting that both smaller and larger higher‐density particles, both with increased efflux capacity, are being isolated with the new method. Gel electrophoresis and Western Blot results from these fractions indicate enrichment of ApoE as well as ApoA‐IV in HDL fractions collected using the 1.25 density cut‐off method, particularly from plasma samples collected in the postprandial vs. the fasted state. In conclusion, we demonstrate that the use of the optimized HDL isolation method yields a broader array of HDL particles, which may be lost when the 1.21 density cut‐off is used.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Spatial Competition in the Iowa Corn Market: Informing the Pricing Behavior of Corporate and Cooperative Grain Merchants

We study spatial competition in the agricultural input procurement sector by explaining variability in corn cash price offers from grain merchants in Iowa. While rail access, river access, size, and ethanol production exhibit the expected positive relationships to corn cash prices, the impact of ownership structure (corporate or cooperative) is complex. Taking distance-based and density-based approaches to spatial competition, we find corporate and cooperative grain merchants differ in their pricing behavior when confronted with spatial competitors. Consistent with theoretical expectations, cooperative grain merchants play a yardstick role in relation to both corporate and cooperative grain merchants.

Particle-conserving dynamics on the single-particle level.

We generalize the particle-conserving dynamics method of de las Heras etal. [J. Phys.: Condens. Matter 28, 244024 (2016)JCOMEL0953-898410.1088/0953-8984/28/24/244024] to binary mixtures and apply this to hard rods in one dimension. Considering the case of one species consisting of only one particle enables us to address the tagged-particle dynamics. The time-evolution of the species-labeled density profiles is compared to exact Brownian dynamics and (grand-canonical) dynamical density functional theory. The particle-conserving dynamics yields improved results over the dynamical density functional theory and well reproduces the simulation data at short and intermediate times. However, the neglect of a strict particle order (due to the fundamental statistical assumption of ergodicity) leads to errors at long times for our one-dimensional setup. The isolated study of that error makes clear the fundamental limitations of (adiabatic) density-based theoretical approaches when applied to systems of any dimension for which particle caging is a dominant physical mechanism.

Topology optimization with supershapes

This work presents a method for the continuum-based topology optimization of structures whereby the structure is represented by the union of supershapes. Supershapes are an extension of superellipses that can exhibit variable symmetry as well as asymmetry and that can describe through a single equation, the so-called superformula, a wide variety of shapes, including geometric primitives. As demonstrated by the author and his collaborators and by others in previous work, the availability of a feature-based description of the geometry opens the possibility to impose geometric constraints that are otherwise difficult to impose in density-based or level set-based approaches. Moreover, such description lends itself to direct translation to computer aided design systems. This work is an extension of the author’s group previous work, where it was desired for the discrete geometric elements that describe the structure to have a fixed shape (but variable dimensions) in order to design structures made of stock material, such as bars and plates. The use of supershapes provides a more general geometry description that, using a single formulation, can render a structure made exclusively of the union of geometric primitives. It is also desirable to retain hallmark advantages of existing methods, namely the ability to employ a fixed grid for the analysis to circumvent re-meshing and the availability of sensitivities to use robust and efficient gradient-based optimization methods. The conduit between the geometric representation of the supershapes and the fixed analysis discretization is, as in previous work, a differentiable geometry projection that maps the supershapes parameters onto a density field. The proposed approach is demonstrated on classical problems of 2-dimensional compliance-based topology optimization.

Mixture model modal clustering

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high density separated from each other by zones of lower density, so that they are closely related to certain regions around the density modes. If the true density is indeed in the assumed class of mixture densities, then mixture model clustering allows to scrutinize more subtle situations than modal clustering. However, when mixture modeling is used in a nonparametric way, taking advantage of the denseness of the sieve of mixture densities to approximate any density, then the correspondence between clusters and mixture components may become questionable. In this paper we introduce two methods to adopt a modal clustering point of view after a mixture model fit. Examples are provided to illustrate that mixture modeling can also be used for clustering in a nonparametric sense, as long as clusters are understood as the domains of attraction of the density modes. Finally, a simulation study reveals that the new methods are extremely efficient from a computational point of view, while at the same time they retain a high level of accuracy.

Density-Based Multiscale Analysis for Clustering in Strong Noise Settings With Varying Densities

Finding meaningful clustering patterns in data can be very challenging when the clusters are of arbitrary shapes, different sizes, or densities, and especially when the data set contains high percentage (e.g., 80%) of noise. Unfortunately, most existing clustering techniques cannot properly handle this tough situation and often result in dramatically deteriorating performance. In this paper, a purposefully designed clustering algorithm called Density-Based Multiscale Analysis for Clustering (DBMAC)-II is proposed, which is an improved version of the latest strong-noise clustering algorithm DBMAC. DBMAC is proposed under the assumption that all clusters are homogeneous and cannot work well when the data set contains clusters of varying densities. DBMAC-II overcomes the limitation of DBMAC by executing the multiscale analysis iteratively and can conduct strong noise-robust clustering without any strict assumption on the shapes and densities of clusters. In DBMAC-II, each data point or object is mapped into a feature space using its ${r}$ -neighborhood statistics with different ${r}$ (radius) values, which is similar to DBMAC. In general, the higher the value of ${r}$ -neighborhood statistics, the more likely the object is considered as a “clustered” object. Instead of trying to find a single optimal ${r}$ value, a set of radius values appropriate for separating “clustered” objects and “noisy” objects is identified, using a formal statistical method for multimodality test, referred to as multiscale analysis. For clusters with varying densities, multiscale analysis is applied to extract the clusters with the highest density from the current data set iteratively. Moreover, a statistical uniformity test for measuring clustering tendency is used as the self-adaptive stopping criterion of the iteration. Comprehensive experimental studies on a series of challenging benchmark data sets demonstrate that DBMAC-II is not only superior to classical density-based clustering approaches, including DBSCAN, OPTICS, and HDBSCAN, but also can consistently outperform the latest strong-noise robust clustering techniques, such as Skinny-dip.

Integrating Global Sensitivity Approaches to Deconstruct Spatial and Temporal Sensitivities of Complex Spatial Agent-Based Models

Spatial agent-based models (ABMs) can be powerful tools for understanding individual level decision-making. However, in an attempt to represent realistic decision-making processes, spatial ABMs often become extremely complex, making it difficult to identify and quantify sources of model sensitivity. This paper implements a coastal version of the economic agent-based urban growth model, CHALMS, to investigate both space- and time-varying sensitivities of simulated coastal development dynamics. We review the current state of spatially- and temporally-explicit global sensitivity analyses (GSA) for environmental modeling in general, and build on the innovative but nascent efforts to implement these approaches with complex spatial ABMs. Combined variance- and density-based approaches to GSA were used to investigate the partitioning, magnitude, and directionality of model output variance. Time-varying GSA revealed sensitivity of multiple outputs to storm frequency and cyclical patterns of sensitivity for other input parameters. Spatially-explicit GSA showed diverging sensitivities at landscape versus (smaller-scale) zonal levels, reflecting trade-offs in residential housing consumer location decisions and spatial ‘spill-over’ interactions. More broadly, when transitioning from a conceptual to empirically parameterized model, sensitivity analysis is a helpful step to prioritize parameters for data collection, particularly when data collection is costly. These findings illustrate unique challenges of and need to perform comprehensive sensitivity analysis with dynamic, spatial ABMs.

Matching of 3D CAD models with density-based approaches: An experimental evaluation of the invariance, bin size and noise robustness

Shape density-based approaches have been extensively studied for 3D model matching due to its simplicity and explicit geometry descriptions. While most studies are concerned with either the matching capabilities or the theoretical aspects of the algorithms, this study experimentally investigates the descriptors’ affine transformation invariance and noise robustness, as well as bin size effect. Six 3D density-based shape descriptors are derived and implemented. It has been found that the shape density descriptors depend on the bin size, all the descriptors are invariant to affine transformations, and all except Convex Hull are robust to noise.