Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field

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Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽

10.3390/s21103527 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3527

Author(s):

Melanija Vezočnik ◽

Roman Kamnik ◽

Matjaz B. Juric

Keyword(s):

Principal Component Analysis ◽

Human Body ◽

Inertial Sensor ◽

Principal Component ◽

Step Length ◽

Component Analysis ◽

Indoor Positioning ◽

Estimation Model ◽

Length Estimation ◽

Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

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Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

Communications Biology ◽

10.1038/s42003-021-01924-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Wenzhong Shi ◽

Chengzhuo Tong ◽

Anshu Zhang ◽

Bin Wang ◽

Zhicheng Shi ◽

...

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Spatiotemporal Variations ◽

Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

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Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Sensors ◽

10.3390/s21113896 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3896

Author(s):

Dat Ngo ◽

Gi-Dong Lee ◽

Bongsoon Kang

Keyword(s):

Objective Function ◽

Density Estimation ◽

Digital Terrain Model ◽

Single Image ◽

Image Dehazing ◽

Terrain Model ◽

Digital Terrain ◽

Proposed Model ◽

Synthetic Datasets ◽

Computation Analysis

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

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