scholarly journals A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Santiago Tello-Mijares ◽  
Francisco Flores
Keyword(s):  
Multilayer Perceptron ◽  
Cross Validation ◽  
Mean Shift ◽  
Automated Detection ◽  
Automated Identification ◽  
Fibonacci Series ◽  
Microscopic Images ◽  
Pollen Species ◽  
Novel Method ◽  
The Mean

The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation.

scholarly journals Estimation of the Monthly Dynamics of Surface Water in Wetlands from Satellite and Secondary Hydro-Climatological Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2380
Author(s):  
Antonio-Juan Collados-Lara ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez ◽  
Leticia Baena-Ruiz
Keyword(s):  
Surface Water ◽  
Regression Models ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Climatological Data ◽  
Explanatory Variables ◽  
Squared Error ◽  
Historical Series ◽  
Novel Method ◽  
The Mean

Satellites produce valuable information for studying the surface water in wetlands, but in many cases the period covered, the spatial resolution and/or the revisit frequency is not enough to produce long historical series. In this paper we propose a novel method which uses regression models that include climatic and hydrological variables to complete the satellite information. We used this method in the Lagunas de Ruidera wetland (Spain). We approached the monthly dynamic of the surface water for a long period (1984–2015). Information from LANDSAT (30-m resolution) and MODIS (250-m resolution) satellites were tested but, due to the size of some lagoons, only the LANDSAT approach produced satisfactory results. An ensemble of regression models based on hydro-climatological explanatory variables was defined to complete the gaps in the monthly surface water. It showed a root mean squared error of around 476 pixels (0.4 Km2) in the cross-validation analysis. Our analysis showed that the explanatory variables with a more significant participation in the regression ensemble are the aquifer discharge, the effective precipitation and the surface water from the previous month. From January to June, the mean surface water in Lagunas de Ruidera is around 4.3 Km2. In summer a reduction of around 13% of the surface water can be observed, which is recovered during the autumn.

The mean shift algorithm and its relation to kernel regression

2016 ◽  
Vol 348 ◽  
pp. 198-208 ◽  
Author(s):  
Youness Aliyari Ghassabeh ◽  
Frank Rudzicz
Keyword(s):  
Mean Shift ◽  
Kernel Regression ◽  
Mean Shift Algorithm ◽  
The Mean

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 26 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Hanguang Xiao
Keyword(s):  
Support Vector Machine ◽  
Input Impedance ◽  
Recursive Algorithm ◽  
Artery Stenosis ◽  
Support Vector ◽  
Arterial Tree ◽  
Novel Method ◽  
The Mean ◽  
Fold Cross Validation ◽  
Systemic Arterial Tree

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Haitham Issa ◽  
Sali Issa ◽  
Wahab Shah
Keyword(s):  
Cross Validation ◽  
Image Feature ◽  
Emotional States ◽  
Time Frequency ◽  
Brain Signals ◽  
Average Accuracy ◽  
Gender And Age ◽  
Novel Method ◽  
Fold Cross Validation ◽  
Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

scholarly journals Mapping intended spinal site of care from the upright to prone position: an interexaminer reliability study

2014 ◽  
Vol 22 (1) ◽  
Author(s):  
Robert Cooperstein ◽  
Morgan Young
Keyword(s):  
Prone Position ◽  
Mean Squared Error ◽  
Thought Experiment ◽  
Spinous Process ◽  
Intraclass Correlation ◽  
Manual Muscle Testing ◽  
Muscle Testing ◽  
Interexaminer Reliability ◽  
Novel Method ◽  
The Mean

Abstract Background Upright examination procedures like radiology, thermography, manual muscle testing, and spinal motion palpation may lead to spinal interventions with the patient prone. The reliability and accuracy of mapping upright examination findings to the prone position is unknown. This study had 2 primary goals: (1) investigate how erroneous spine-scapular landmark associations may lead to errors in treating and charting spine levels; and (2) study the interexaminer reliability of a novel method for mapping upright spinal sites to the prone position. Methods Experiment 1 was a thought experiment exploring the consequences of depending on the erroneous landmark association of the inferior scapular tip with the T7 spinous process upright and T6 spinous process prone (relatively recent studies suggest these levels are T8 and T9, respectively). This allowed deduction of targeting and charting errors. In experiment 2, 10 examiners (2 experienced, 8 novice) used an index finger to maintain contact with a mid-thoracic spinous process as each of 2 participants slowly moved from the upright to the prone position. Interexaminer reliability was assessed by computing Intraclass Correlation Coefficient, standard error of the mean, root mean squared error, and the absolute value of the mean difference for each examiner from the 10 examiner mean for each of the 2 participants. Results The thought experiment suggesting that using the (inaccurate) scapular tip landmark rule would result in a 3 level targeting and charting error when radiological findings are mapped to the prone position. Physical upright exam procedures like motion palpation would result in a 2 level targeting error for intervention, and a 3 level error for charting. The reliability experiment showed examiners accurately maintained contact with the same thoracic spinous process as the participant went from upright to prone, ICC (2,1) = 0.83. Conclusions As manual therapists, the authors have emphasized how targeting errors may impact upon manual care of the spine. Practitioners in other fields that need to accurately locate spinal levels, such as acupuncture and anesthesiology, would also be expected to draw important conclusions from these findings.

The mean shift method of chaotic sequences in the study of compressive sensing

2016 ◽  
Vol 8 (5) ◽  
pp. 1643-1654 ◽  
Author(s):  
Guoming Chen ◽  
Qiang Chen ◽  
Shun Long ◽  
Weiheng Zhu
Keyword(s):  
Compressive Sensing ◽  
Mean Shift ◽  
Shift Method ◽  
Chaotic Sequences ◽  
The Mean

scholarly journals Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator

2020 ◽  
Author(s):  
Filip Potempski ◽  
Andrea Sabo ◽  
Kara K Patterson
Keyword(s):  
Deep Learning ◽  
Motor Performance ◽  
Data Extraction ◽  
Effective Means ◽  
Technical Note ◽  
Novel Method ◽  
Synchronous Condition ◽  
The Mean ◽  
Repeated Training

AbstractDance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.

Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net

2021 ◽  
Author(s):  
Luke A Matkovic ◽  
Tonghe Wang ◽  
Yang Lei ◽  
Oladunni O Akin-Akintayo ◽  
Olayinka A Abiodun Ojo ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Cross Validation ◽  
Ct Images ◽  
Second Step ◽  
Prostate Segmentation ◽  
Positron Emission ◽  
Pet Ct ◽  
The Mean ◽  
And Training ◽  
Fold Cross Validation

Abstract Focal dose boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. We propose a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network (DAN), is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated from PET/CT by the trained network. To evaluate the proposed method, we retrospectively investigated 49 PET/CT datasets. On each dataset, the prostate and DILs were delineated by physicians and set as the ground truths and training targets. The proposed method was trained and evaluated using a five-fold cross-validation and a hold-out test. The mean surface distance and DSC values were 0.666±0.696mm and 0.932±0.059 for the prostate and 1.209±1.954mm and 0.757±0.241 for the DILs among all 49 patients. The proposed method has demonstrated great potential for improving the efficiency and reducing the observer variability of prostate and DIL contouring for DIL focal boost prostate radiation therapy.

Dynamic Determination of Histogram Resolution for Object Tracking using the Mean-Shift Tracking Algorithm

2011 ◽  
Author(s):  
Takayuki Nishimori ◽  
Toyohiro Hayashi ◽  
Shuichi Enokida ◽  
Toshiaki Ejima
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
The Mean
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