Selection of optimal external filter for colorimetric camera

2021 ◽  
Vol 2021 (29) ◽  
pp. 141-147
Author(s):  
Michael J. Vrhel ◽  
H. Joel Trussell
Keyword(s):  
Figure Of Merit ◽  
Band System ◽  
Image Data ◽  
Optimal Filter ◽  
Training Data ◽  
Noise Model ◽  
Data Set ◽  
Testing Data ◽  
And Training ◽  
Selection Of

A database of realizable filters is created and searched to obtain the best filter that, when placed in front of an existing camera, results in improved colorimetric capabilities for the system. The image data with the external filter is combined with image data without the filter to provide a six-band system. The colorimetric accuracy of the system is quantified using simulations that include a realistic signal-dependent noise model. Using a training data set, we selected the optimal filter based on four criteria: Vora Value, Figure of Merit, training average ΔE, and training maximum ΔE. Each selected filter was used on testing data. The filters chosen using the training ΔE criteria consistently outperformed the theoretical criteria.

An Evaluation of the Channel Effect on Detecting the Preictal Stage in Patients With Epilepsy

2020 ◽  
pp. 155005942096643
Author(s):  
Erhan Bergil ◽  
Mehmet Recep Bozkurt ◽  
Canan Oral
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Training Data ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Testing Data ◽  
Patients With Epilepsy ◽  
Electrode Selection ◽  
And Training ◽  
Selection Of

Decreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

scholarly journals On Realistically Attacking Tor with Website Fingerprinting

2016 ◽  
Vol 2016 (4) ◽  
pp. 21-36 ◽  
Author(s):  
Tao Wang ◽  
Ian Goldberg
Keyword(s):  
Background Noise ◽  
Laboratory Tests ◽  
Training Data ◽  
Web Traffic ◽  
Training Set ◽  
Data Set ◽  
Laboratory Conditions ◽  
Testing Data ◽  
In The Wild ◽  
New Algorithms

Abstract Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, in laboratory tests we collect the training data set together with the testing data set, so the training data set is fresh, but an attacker may not be able to maintain a fresh data set. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include background noise from other types of web traffic. These differences adversely affect website fingerprinting under realistic conditions. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. We describe several new algorithms for tackling background noise. With our techniques, we are able to build the first website fingerprinting system that can operate directly on packet sequences collected in the wild.

EFFICIENT CLASSIFICATION OF SCANNED MEDIA USING SPATIAL STATISTICS

2010 ◽  
Vol 24 (06) ◽  
pp. 917-946
Author(s):  
GOZDE UNAL ◽  
GAURAV SHARMA ◽  
REINER ESCHBACH
Keyword(s):  
Spatial Statistics ◽  
High Reliability ◽  
Image Data ◽  
Training Data ◽  
Automated Classification ◽  
Data Set ◽  
Statistical Measures ◽  
Scanned Image ◽  
Scanned Images

Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

A Comparative Study of Impact Localisation in Composite Structures Using Neural Networks under Environmental and Operational Variations

2019 ◽  
Vol 827 ◽  
pp. 410-415
Author(s):  
Aldyandra Hami Seno ◽  
Ferri M.H. Aliabadi
Keyword(s):  
Composite Structures ◽  
Real Life ◽  
Training Data ◽  
Data Set ◽  
Operational Conditions ◽  
Feature Extraction Method ◽  
Set Size ◽  
Artificial Neural Network Ann ◽  
Training Impact ◽  
And Training

In this study we compare the effectiveness of the Normalised Smoothed Envelope (NSET) method for Artificial Neural Network (ANN) based impact localisation under simulated environmental and operational conditions with respect to other ANN based localisation methods developed by other studies. It is shown that when the testing and training impact case is the same, most studies give comparably good accuracy of localisation irrespective of feature extraction method or structure geometry. However, when the testing and training impact cases are not the same, only the NSET method is able to negate the variations caused by various impact cases and provide good localisation accuracy for an ANN trained using only a single impact case thus allowing for smaller training data set size requirements and increasing feasibility for real life application.

STING Algorithm Used English Sentiment Classification in a Parallel Environment

2017 ◽  
Vol 31 (07) ◽  
pp. 1750021 ◽  
Author(s):  
Nguyen Duy Dat ◽  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran ◽  
Vo Thi Ngoc Chau ◽  
Tuan A. Nguyen
Keyword(s):  
Sentiment Classification ◽  
Training Data ◽  
Network Environment ◽  
Data Set ◽  
Political Activities ◽  
New Model ◽  
Commercial Activities ◽  
Testing Data ◽  
Parallel Network ◽  
English Training

Sentiment classification is significant in everyday life of everyone, in political activities, activities of commodity production, commercial activities. In this research, we propose a new model for Big Data sentiment classification in the parallel network environment. Our new model uses STING Algorithm (SA) (in the data mining field) for English document-level sentiment classification with Hadoop Map (M)/Reduce (R) based on the 90,000 English sentences of the training data set in a Cloudera parallel network environment — a distributed system. In the world there is not any scientific study which is similar to this survey. Our new model can classify sentiment of millions of English documents with the shortest execution time in the parallel network environment. We test our new model on the 25,000 English documents of the testing data set and achieved on 61.2% accuracy. Our English training data set includes 45,000 positive English sentences and 45,000 negative English sentences.

scholarly journals Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection

2020 ◽  
Vol 4 (2) ◽  
pp. 40-49
Author(s):  
Harianto Harianto ◽  
◽  
Andi Sunyoto ◽  
Sudarmawan Sudarmawan ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Data Set ◽  
System Data ◽  
And Training

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.

scholarly journals Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

2015 ◽  
Vol 7 (4) ◽  
pp. 3383-3408 ◽  
Author(s):  
F. Khan ◽  
F. Enzmann ◽  
M. Kersten
Keyword(s):  
Support Vector Machine ◽  
Image Data ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Beam Hardening ◽  
Reconstruction Procedure ◽  
Data Set ◽  
Corrected Image ◽  
Computed Microtomography

Abstract. In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

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