Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry

The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.

Download Full-text

Classification of Time Series Using Singular Values and Wavelet Subband Analysis with ANN and SVM Classifiers

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2006.p0498 ◽

2006 ◽

Vol 10 (4) ◽

pp. 498-503 ◽

Cited By ~ 3

Author(s):

Balázs Benyó ◽

◽

Péter Somogyi ◽

Béla Paláncz ◽

◽

...

Keyword(s):

Blood Flow ◽

Singular Values ◽

Gaussian Kernel ◽

Support Vector ◽

Spectral Matrix ◽

Support Vector Classifier ◽

Brain States ◽

Cerebral Circulation Disorders ◽

Blood Flow Patterns

Oscillation of cerebral blood flow (CBF) in physiological or pathophysiological brain states is common, therefore it is promising to identify cerebral circulation disorders based on CBF signal classification. To characterize temporal blood flow patterns, we applied two feature extractions, spectral matrix and wavelet subband analysis. To distinguish between different physiological states, two different classifications have been developed - the radial basis function-based neural network and a support vector classifier with a Gaussian kernel. Feature extraction and classification are evaluated and their efficiency compared. Calculation was done using <I>Mathematica</I> 5.1 and its <I>Wavelet Application</I>.

Download Full-text

Classification of Soils into Hydrologic Groups Using Machine Learning

Data ◽

10.3390/data5010002 ◽

2019 ◽

Vol 5 (1) ◽

pp. 2 ◽

Cited By ~ 4

Author(s):

Shiny Abraham ◽

Chau Huynh ◽

Huy Vu

Keyword(s):

Machine Learning ◽

Water Conservation ◽

Large Scale ◽

Performance Metrics ◽

Gaussian Kernel ◽

Support Vector ◽

K Nearest Neighbors ◽

Group B ◽

Soil Groups

Hydrologic soil groups play an important role in the determination of surface runoff, which, in turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into appropriate hydrologic groups is based on the judgement of soil scientists, primarily relying on their interpretation of guidelines published by regional or national agencies. As a result, large-scale mapping of hydrologic soil groups results in widespread inconsistencies and inaccuracies. This paper presents an application of machine learning for classification of soil into hydrologic groups. Based on features such as percentages of sand, silt and clay, and the value of saturated hydraulic conductivity, machine learning models were trained to classify soil into four hydrologic groups. The results of the classification obtained using algorithms such as k-Nearest Neighbors, Support Vector Machine with Gaussian Kernel, Decision Trees, Classification Bagged Ensembles and TreeBagger (Random Forest) were compared to those obtained using estimation based on soil texture. The performance of these models was compared and evaluated using per-class metrics and micro- and macro-averages. Overall, performance metrics related to kNN, Decision Tree and TreeBagger exceeded those for SVM-Gaussian Kernel and Classification Bagged Ensemble. Among the four hydrologic groups, it was noticed that group B had the highest rate of false positives.

Download Full-text

Chronological age assessment based on wrist radiograph processing – Some novel approaches

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-190779 ◽

2021 ◽

Vol 40 (5) ◽

pp. 8651-8663

Author(s):

N. Shobha Rani ◽

C. R. Yadhu ◽

U. Karthik

Keyword(s):

Age Groups ◽

Gaussian Process Regression ◽

Bone Age ◽

Gaussian Kernel ◽

Chronological Age ◽

Support Vector ◽

Image Analyzer ◽

Neural Net ◽

Radiological Society

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

Download Full-text

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209316 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 137-145

Author(s):

Yubin Xia ◽

Dakai Liang ◽

Guo Zheng ◽

Jingling Wang ◽

Jie Zeng

Keyword(s):

Fault Diagnosis ◽

Planetary Gear ◽

Gaussian Kernel ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Energy Characteristics ◽

Gear Fault ◽

Channel Information ◽

Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Download Full-text

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.1495 ◽

2011 ◽

Vol 131 (8) ◽

pp. 1495-1501

Author(s):

Dongshik Kang ◽

Masaki Higa ◽

Hayao Miyagi ◽

Ikugo Mitsui ◽

Masanobu Fujita ◽

...

Keyword(s):

Support Vector Machine ◽

Acoustic Signals ◽

Support Vector ◽

Amplitude Difference

Download Full-text

Using watermark visibility measurements to select an optimized pair of spot colors for use in a binary watermark

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.15.color-195 ◽

2020 ◽

Vol 2020 (15) ◽

pp. 197-1-197-7

Author(s):

Alastair Reed ◽

Vlado Kitanovski ◽

Kristyn Falkenstern ◽

Marius Pedersen

Keyword(s):

Food Packaging ◽

Red Light ◽

Human Observer ◽

Viewing Distance ◽

Point Of Sale ◽

Complementary Color ◽

Low Visibility ◽

Packaging Industry ◽

Spot Color ◽

Selection Of

Spot colors are widely used in the food packaging industry. We wish to add a watermark signal within a spot color that is readable by a Point Of Sale (POS) barcode scanner which typically has red illumination. Some spot colors such as blue, black and green reflect very little red light and are difficult to modulate with a watermark at low visibility to a human observer. The visibility measurements that have been made with the Digimarc watermark enables the selection of a complementary color to the base color which can be detected by a POS barcode scanner but is imperceptible at normal viewing distance.

Download Full-text