Counting Datasets Research Articles

Blood cell classification using interpretable shape features: A Comparative study of SVM models and CNN-Based approaches

Background and Objective: Models to count blood cells have been extensively studied, but there are very few for classifying potential blood cells. This is crucial for verifying that what these systems are counting are in fact the correct object of interest. Furthermore, we have very little insight into which features are important for differentiating these classes in computational models.Methods:This paper presents a competitive solution for classifying red blood cells, white blood cells, and platelets from each other.Results:We were able to achieve an overall classification rate of about 99% and outperformed convolutional neural networks by about 10% by using the Blood Cell Count Dataset with a support vector machine, a polynomial kernel, and interpretable and explainable features. Further, we are also able to identify that the important features for classifying the three classes are the number of corners, the proportion of the area of the cell over the relevant area the cell occupies, the major axis relative length of the cell, and the relevant area surrounding the cell.Conclusion: We built a model for blood cell classification that is more interpretable and explainable while also outperforming the state-of-the-art.

Locality-Aware Crowd Counting

Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias. Our proposed method is locality-aware in two aspects. First, we introduce a locality-aware data partition (LADP) approach to group the training data into different bins via locality-sensitive hashing. As a result, a more balanced data batch is then constructed by LADP. To further reduce the training bias and enhance the collaboration with LADP, a new data augmentation method called locality-aware data augmentation (LADA) is proposed where the image patches are adaptively augmented based on the loss. The proposed method is independent of the backbone network architectures, and thus could be smoothly integrated with most existing deep crowd counting approaches in an end-to-end paradigm to boost their performance. We also demonstrate the versatility of the proposed method by applying it for adversarial defense. Extensive experiments verify the superiority of the proposed method over the state of the arts.

A network-based systems biology approach for identification of shared Gene signatures between male and female in COVID-19 datasets.

The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17–5p, mir-20a-5p, mir-93–5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.

Graph Regularized Flow Attention Network for Video Animal Counting From Drones.

In this paper, we propose a large-scale video based animal counting dataset collected by drones (AnimalDrone) for agriculture and wildlife protection. The dataset consists of two subsets, i.e., PartA captured on site by drones and PartB collected from the Internet, with rich annotations of more than 4 million objects in 53, 644 frames and corresponding attributes in terms of density, altitude and view. Moreover, we develop a new graph regularized flow attention network (GFAN) to perform density map estimation in dense crowds of video clips with arbitrary crowd density, perspective, and flight altitude. Specifically, our GFAN method leverages optical flow to warp the multi-scale feature maps in sequential frames to exploit the temporal relations, and then combines the enhanced features to predict the density maps. Moreover, we introduce the multi-granularity loss function including pixel-wise density loss and region-wise count loss to enforce the network to concentrate on discriminative features for different scales of objects. Meanwhile, the graph regularizer is imposed on the density maps of multiple consecutive frames to maintain temporal coherency. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, compared with several state-of-the-art counting algorithms. The AnimalDrone dataset is available at https://github.com/VisDrone/AnimalDrone.

Learning a deep network with cross-hierarchy aggregation for crowd counting

Crowd counting, a significant but challenging task in computer vision, aims at estimating the number of people in an image or video. Recent methods for crowd counting have obtained promising performance due to deep neural networks but most of them ignore the abundant conducive information in hierarchical features. In this paper, a novel Cross-Hierarchy Aggregation Network (CHANet) is proposed to exploit multi-hierarchy information in the crowd features from each hierarchy and aggregate cross-hierarchy features to generate reasonable density maps. Firstly, we propose a CHA module to fully extract local hierarchical features and capture maximum information of the crowd features. The CHA module combines residual and dense connections without over-assigning parameters for feature reuse. Then, we utilize the global hierarchical features from the shallow hierarchies to obtain a more powerful representation ability with a global residual connection. Experimental evaluations on four publicly available crowd counting datasets (ShanghaiTech, UCF-QNRF, WorldExpo’10, and Beijing BRT) demonstrate that the proposed CHANet achieves superior performance compared to other state-of-the-art methods.

A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.

The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcomes simultaneously. The MPDN model uses the minus log-likelihood of a Poisson distribution as a loss function, in hidden layers for capturing nonlinear patterns using the rectified linear unit (RELU) activation function and, in the output layer, the exponential activation function was used for producing outputs on the same scale of counts. The proposed MPDN model was compared to conventional generalized Poisson regression models and univariate Poisson deep learning models in two experimental data sets of count data. We found that the proposed MPDL outperformed univariate Poisson deep neural network models, but did not outperform, in terms of prediction, the univariate generalized Poisson regression models. All deep learning models were implemented in Tensorflow as back-end and Keras as front-end, which allows implementing these models on moderate and large data sets, which is a significant advantage over previous GS models for multivariate count data.

A multi-scale and multi-level feature aggregation network for crowd counting

Recently, crowd counting has drawn widespread attention in computer vision, but it is extremely challenging because of the varying scales and densities. Many existing methods focus on improving the multi-scale representation by utilizing multi-column or multi-branch architectures with different kernel sizes. However, such networks cannot extract the feature maps with large receptive fields due to limitation of depth. In addition, the importance of utilizing the multi-level feature information in a deep network is ignored. In this paper, we propose a multi-scale and multi-level features aggregation network (MFANet) for accurate and efficient crowd counting, and it can be trained by end-to-end. A vital component of the network is the scale and level aggregation module (SLAM), which can extract multi-scale features and make full use of multi-level feature information for more accurate estimation. When six SLAMs are stacked together and applied to our network, our method can achieve the best performance. Furthermore, we introduce a new loss function called normalized Euclidean loss (NEL) to balance the contribution of all samples to network training. To demonstrate the performance of the proposed method, extensive experiments are conducted on four benchmark crowd counting datasets, including ShanghaiTec Part A/B, UCF-CC-50, Mall, and UCF-QNRF. Experimental results show that our MFANet achieves state-of-the-art performance in crowd counting and crowd localization.

A new mixed-effects regression model for the analysis of zero-modified hierarchical count data.

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero-modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)-(b) and (b)-(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed-effects regression model based on the hurdle version of the Poisson-Lindley distribution. In this framework, the zero-modification is incorporated by assuming that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well-established mixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian -value and the randomized quantile residuals were considered for modeldiagnostics.

Robust statistical methods for point of sale designs, the example of healthcare snack and drink vending

PurposeThe experiment introduces nutritional labelling, healthier products and product placement designs to the hospital vending machines, to promote healthy lifestyles.Design/methodology/approachThe site where this experiment happens is a major London hospital, serving over a million patients every year. In the experiment, the hospital’s snack and drink vending machines are redesigned. The impact on product sales are then analysed using robust statistical methods.FindingsNutritional labelling has a statistically significant impact on product sales. Less of the unhealthiest products are sold. Healthier products and product placement designs have a larger impact but with less statistical significance. They require further testing.Research limitations/implicationsExperts in service operations can use this experiment’s regression modelling methods. The methods are ideal for measuring change over time in counting data sets in complex real world environments.Practical implicationsThere are suggestions for practical vending service change in this research. They are in line and add a practical example to Government policy guidance.Social implicationsPeople using the redesigned vending machines have more opportunity for healthy lifestyle choices.Originality/valueThe experiment provides statistical evidence in support of catering for healthier lifestyles.

Counting From Sky: A Large-Scale Data Set for Remote Sensing Object Counting and a Benchmark Method

Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task. Significant efforts have been devoted to addressing this problem and achieved great progress, yet counting the number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in a natural scene, this task is challenging in the following factors: large scale variation, complex cluttered background, and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset with remote sensing images, which contains four important geographic objects: buildings, crowded ships in harbors, large-vehicles and small-vehicles in parking lots. We then benchmark the dataset by designing a novel neural network that can generate a density map of an input image. The proposed network consists of three parts namely attention module, scale pyramid module and deformable convolution module to attack the aforementioned challenging factors. Extensive experiments are performed on the proposed dataset and one crowd counting datset, which demonstrate the challenges of the proposed dataset and the superiority and effectiveness of our method compared with state-of-the-art methods.

In-field cotton boll counting based on a deep neural network of density level classification

Development of computer vision technologies has been widely used to increase the level of agricultural intelligence. Crop counting, an application of image counting, plays a fundamental role in agricultural information automation. However, the complex cotton field environment is likely lead to incorrect detection of the target position or fragmentation of the segmentation results, resulting in a decrease in counting accuracy. Despite this, computer vision technologies have shown great potential to effectively solve this task. To solve the problem of multimode cotton boll counting in a complicated environment, an in-field cotton boll counting algorithm based on density classification is proposed. First, the algorithm encodes the global context information with a density level classification estimator. Then, the input images are converted into high-dimensional feature maps by density map estimator with a multicolumn structure. Finally, through the feature fusion neural network, the classification information is combined with high-dimensional feature maps to generate a high-quality density map, and then the cotton bolls are counted. In particular, we collected and labeled a cotton boll counting dataset with 758 high-resolution images for experiment and comparison, which can be divided by different environmental conditions and observations sites. In addition, the relationship of cotton yield against the cotton bolls counting can be assessed by adopting our proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves a lower counting error and better effectiveness and robustness than other comparative algorithms.

Standardising pollen monitoring: quantifying confidence intervals for measurements of airborne pollen concentration

Measurement of atmospheric pollen concentrations is inexact, yet pollen concentrations are universally reported without estimate of accuracy. These imprecise values are nevertheless used for modelling, forecasting and public health decision-making. Estimation of the variability in reported pollen concentrations would help resolve associations between weather and pollen aerobiology as well as associations between pollen exposure and health. For any given daily atmospheric pollen level, a statistical variability would be expected in the set of possible measures. This variability is introduced and compounded by many factors including human error, classification error or instrument variability, as well as variability derived from strategies used to count and scale the airborne pollen sample. Here, we performed numeric simulations of pollen deposition and modelled the variability in contemporary pollen density estimates. Statistical distribution of the mean and variance of these simulated counts was compared with an existing pollen count dataset. Both simulations and actual pollen data showed that a significant range of atmospheric pollen concentrations could be inferred from the same daily pollen collection. The range of possible concentrations varied both with the atmospheric pollen density and with the portion of the daily pollen sample that is counted. Furthermore, pollen concentration data were shown to be non-normal and heteroscedastic, which has implications for a variety of tests (e.g. ANOVA), for regression analysis, and for pollen forecasting and forecast verification. These results reinforce the importance of counting as much of the collected pollen impaction surface as feasible to minimise the uncertainty in reported pollen levels. The outcomes of this study suggest that confidence intervals for daily pollen concentrations should be reported.

Poisson Weighted Ishita Distribution: Model for Analysis of Over-Dispersed Medical Count Data

Abstract A new over-dispersed discrete probability model is introduced, by compounding the Poisson distribution with the weighted Ishita distribution. The statistical properties of the newly introduced distribution have been derived and discussed. Parameter estimation has been done with the application of the maximum likelihood method of estimation, followed by the Monte Carlo simulation procedure to examine the suitability of the ML estimators. In order to verify the applicability of the proposed distribution, a real-life set of data from the medical field has been analysed for modeling a count dataset representing epileptic seizure counts.

A comparison of statistical methods for the analysis of binary repeated measures data with additional hierarchical structure

The objective of the study was to compare statistical methods for the analysis of binary repeated measures data with an additional hierarchical level. Random effects true models with autocorrelated ($\rho=1$, 0.9 or 0.5) subject random effects were used in this simulation study. The settings of the simulation were chosen to reflect a real veterinary somatic cell count dataset, except that the within--subject time series were balanced, complete and of fixed length (4 or 8 time points). Four fixed effects parameters were studied: binary predictors at the subject and cluster levels, respectively, a linear time effect, and the intercept. The following marginal and random effects statistical procedures were considered: ordinary logistic regression (OLR), alternating logistic regression (ALR), generalized estimating equations (GEE), marginal quasi-likelihood (MQL), penalized quasi-likelihood (PQL), pseudo likelihood (REPL), maximum likelihood (ML) estimation and Bayesian Markov chain Monte Carlo (MCMC). The performance of these estimation procedures was compared specifically for the four fixed parameters as well as variance and correlation parameters. The findings of this study indicate that in data generated by random intercept models ($\rho=1$), the ML and MCMC procedures performed well and had fairly similar estimation errors. The PQL regression estimates were attenuated while the variance estimates were less accurate than ML and MCMC, but the direction of the bias depended on whether binomial or extra-binomial dispersion was assumed. In datasets with autocorrelation ($\rho<1$), random effects estimates procedures gave downwards biased estimates, while marginal estimates were little affected by the presence of autocorrelation. The results also indicate that in addition to ALR, a GEE procedure that accounts for clustering at the highest hierarchical level is sufficient.

On Poisson-exponential-Tweedie models for ultra-overdispersed count data

We introduce a new class of Poisson-exponential-Tweedie (PET) mixture in the framework of generalized linear models for ultra-overdispersed count data. The mean–variance relationship is of the form $$m+m^{2}+\phi m^{p}$$ , where $$\phi$$ and p are the dispersion and Tweedie power parameters, respectively. The proposed model is equivalent to the exponential-Poisson–Tweedie models arising from geometric sums of Poisson–Tweedie random variables. In this respect, the PET models encompass the geometric versions of Hermite, Neyman Type A, Polya–Aeppli, negative binomial and Poisson–inverse Gaussian models. The algorithms we shall propose allow to estimate the real power parameter, which works as an automatic distribution selection. Instead of the classical Poisson, zero-shifted geometric is presented as the reference count distribution. Practical properties are incorporated into the PET of new relative indexes of dispersion and zero-inflation phenomena. Simulation studies demonstrate that the proposed model highlights unbiased and consistent estimators for large samples. Illustrative practical applications are analysed on count data sets, in particular, PET models for data without covariates and PET regression models. The PET models are compared to Poisson–Tweedie models showing that parameters of both models are adopted to data.

A new one-parameter discrete distribution with associated regression and integer-valued autoregressive models

Abstract This study introduces the Poisson-Bilal distribution and its associated two models for modeling the over-dispersed count data sets. The Poisson-Bilal distribution has tractable properties and explicit forms for its statistical properties. A new over-dispersed count regression model and integer-valued autoregressive process with flexible innovation distribution are defined and studied comprehensively. Two real data sets are analyzed to prove empirically the importance of proposed models. Empirical findings show that the Poisson-Bilal distribution has important application fields in time series and regression modeling.

A new bivariate Poisson distribution via conditional specification: properties and applications

ABSTRACT In this article, we discuss a bivariate Poisson distribution whose conditionals are univariate Poisson distributions and the marginals are not Poisson which exhibits negative correlation. Some useful structural properties of this distribution namely marginals, moments, generating functions, stochastic ordering are investigated. Simple proofs of negative correlation, marginal over-dispersion, distribution of sum and conditional given the sum are also derived. The distribution is shown to be a member of the multi-parameter exponential family and some natural but useful consequences are also outlined. Parameter estimation with maximum likelihood is implemented. Copula-based simulation experiments are carried out using Bivariate Normal and the Farlie–Gumbel–Morgenstern copulas to assess how the model behaves in dealing with the situation. Finally, the distribution is fitted to seven bivariate count data sets with an inherent negative correlation to illustrate suitability.

The Oxygen of Publicity: Explaining U.S. Media Coverage of International Kidnapping

What explains U.S. media coverage of Americans kidnapped abroad? While some hostages receive national media attention, others hardly make the local news. Using an original, event count dataset of newspaper stories about Americans kidnapped abroad since 2001, this article tests the oft-cited, under-measured assumption that “terrorism” receives more media attention than other violence. Controlling for variation across kidnapping, I show that those framed as “terrorism” receive more coverage. Challenging existing literature, I also demonstrate that incidents with more victims receive less coverage than those incidents with fewer victims, and that there is no “missing white woman syndrome” in international kidnapping.

Counting Crowds with Perspective Distortion Correction via Adaptive Learning.

The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance.

MH-MetroNet-A Multi-Head CNN for Passenger-Crowd Attendance Estimation.

Knowing an accurate passengers attendance estimation on each metro car contributes to the safely coordination and sorting the crowd-passenger in each metro station. In this work we propose a multi-head Convolutional Neural Network (CNN) architecture trained to infer an estimation of passenger attendance in a metro car. The proposed network architecture consists of two main parts: a convolutional backbone, which extracts features over the whole input image, and a multi-head layers able to estimate a density map, needed to predict the number of people within the crowd image. The network performance is first evaluated on publicly available crowd counting datasets, including the ShanghaiTech part_A, ShanghaiTech part_B and UCF_CC_50, and then trained and tested on our dataset acquired in subway cars in Italy. In both cases a comparison is made against the most relevant and latest state of the art crowd counting architectures, showing that our proposed MH-MetroNet architecture outperforms in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE) and passenger-crowd people number prediction.