Blind CFA Interpolation Detection Based on the Entropy

2012 ◽  
Vol 532-533 ◽  
pp. 787-791
Author(s):  
Xiao Zhong Pan ◽  
Jian Xie
Keyword(s):  
Digital Image ◽  
Matrix Equation ◽  
Digital Camera ◽  
High Accuracy ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Svm Classifier ◽  
Cfa Interpolation ◽  
Pixel Matrix

Blind CFA interpolation detection，which identifies the demosaicing method used in digital camera by analyzing output images, provides many efficient tools for digital image forensics．In this paper, we proposes an approach of blind CFA interpolation detection based on the entropy of the correlative coefficients．By solving the pixel matrix equation, the CFA interpolation coefficients are calculated and the entropy of the coefficients are obtained, and they are further fed to SVM classifier to identify forgery. The experimental results show a high accuracy on blind CFA interpolation detection．Compared with existing ones，the proposed method in this paper indicates a better performance on the robnstness especially against lossy JPEG compression.

Digital Image Correlation as a Tool for Monitoring Crack Networks on the Surface of MgO-Based Refractory Castables

2014 ◽  
Vol 92 ◽  
pp. 242-247
Author(s):  
Rafael G.M. Saracura ◽  
Rodrigo B. Canto ◽  
Victor Carlos Pandolfelli ◽  
Nicolas Schmitt ◽  
François Hild
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Displacement Field ◽  
Digital Camera ◽  
Experimental Results ◽  
Image Correlation ◽  
Post Processing ◽  
Climatic Chamber ◽  
Refractory Castables

This paper deals with the characterization of crack networks on the surface of refractory castable slabs, which are induced by stresses arising during the curing and drying (CD) stages. Images of the surface of castable samples placed in a house-made climatic chamber were recorded during the CD stages with a digital camera and processed by Digital Image Correlation (DIC) tomeasure the displacement field. Post-processing of DIC results were performed to estimate the length, orientation and opening of cracks in networks. The methodology is detailed and first analyses of experimental results are shown.

Digital Camera Source Identification Through JPEG Quantisation

2011 ◽  
pp. 291-313 ◽  
Author(s):  
Matthew James Sorrell
Keyword(s):  
Source Identification ◽  
Digital Camera ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Experimental Results ◽  
Photographic Image ◽  
Source Camera Identification ◽  
Camera Identification ◽  
High Level ◽  
Camera Source Identification

We propose that the implementation of the JPEG compression algorithm represents a manufacturer and model-series specific means of identification of the source camera of a digital photographic image. Experimental results based on a database of over 5,000 photographs from 27 camera models by 10 brands shows that the choice of JPEG quantisation table, in particular, acts as an effective discriminator between model series with a high level of differentiation. Furthermore, we demonstrate that even after recompression of an image, residual artefacts of double quantisation continue to provide limited means of source camera identification, provided that certain conditions are met. Other common techniques for source camera identification are also introduced, and their strengths and weaknesses are discussed.

A Blind Watermarking Algorithm for Digital Image

2013 ◽  
Vol 333-335 ◽  
pp. 1110-1113
Author(s):  
Jun Jie Wang ◽  
Qian Mo ◽  
Dong Xia Mei ◽  
Su Li
Keyword(s):  
Digital Image ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Blind Watermarking ◽  
Noise Interference ◽  
Host Image ◽  
Original Host ◽  
The Matrix ◽  
Watermark Algorithm

Based on the method of SVD, a novel watermark algorithm is designed in this paper. Firstly, the SVD is performed on each segment of the original host image; Secondly, the watermark bits are formed by chaotic encrypting;Thirdly, the secret bits are embedded into the S component of the matrix by quantization; Lastly, the inverse SVD transformation is performed on each block and the watermarked image is constructed. The original host image is not required in the secret watermark recovery. Experimental results show that the watermarked image has transparent feature, and the quality of the recovered watermark is satisfying. The algorithm is strongly robust to many attacks, such as JPEG compression attack, Noise Interference and so on..

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

2016 ◽  
Vol 56 (12) ◽  
pp. 2060 ◽  
Author(s):  
Serkan Ozkaya ◽  
Wojciech Neja ◽  
Sylwia Krezel-Czopek ◽  
Adam Oler
Keyword(s):  
Image Analysis ◽  
Body Length ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Camera ◽  
Body Measurements ◽  
Analysis Software ◽  
Body Area ◽  
Image Analysis Software

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

scholarly journals High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Davide Dardari ◽  
Nicoló Decarli ◽  
Anna Guerra ◽  
Ashraf Al-Rimawi ◽  
Víctor Marín Puchades ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
High Accuracy ◽  
Experimental Results ◽  
Localization Error ◽  
Localization System ◽  
Dynamic Conditions ◽  
The Road ◽  
Road Users

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

Identification and Compensation of Geometric and Elastic Errors in Large Manipulators: Application to a High Accuracy Medical Robot

1998 ◽  
Author(s):  
Marco A. Meggiolaro ◽  
Constantinos Mavroidis ◽  
Steven Dubowsky
Keyword(s):  
High Accuracy ◽  
Measured Data ◽  
Experimental Results ◽  
Geometric Errors ◽  
Medical Robot ◽  
Error Sources ◽  
End Effector ◽  
Positioning Errors ◽  
Structural Deformations ◽  
Manufacturing Tolerances

Abstract A method is presented to identify the source of end-effector positioning errors in large manipulators using experimentally measured data. Both errors due to manufacturing tolerances and other geometric errors and elastic structural deformations are identified. These error sources are used to predict, and compensate for, the end-point errors as a function of configuration and measured forces. The method is applied to a new large high accuracy medical robot. Experimental results show that the method is able to effectively correct for the errors in the system.

scholarly journals Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhijun Wang
Keyword(s):  
Wireless Network ◽  
Kernel Function ◽  
Digital Image ◽  
Learning Algorithm ◽  
Deep Level ◽  
Multitask Learning ◽  
Experimental Results ◽  
Spatial Consistency ◽  
Discrimination Rate ◽  
Edge Sampling

Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in-depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep-level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.

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