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Robust Digital Watermarking Algorithm Based on DCT-SVD and QR Code 20

Robust Digital Watermarking Algorithm Based on DCT-SVD and Q...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Zhao Xiaolei Wang Guangqin Xu Xibin Sun Yat Sen Univ Sch Informat Sci Xinhua Coll 19 Huamei Rd Guangzhou Peoples R China Guangzhou Coll Commerce Sch Management 206 Jiulong Ave Guangzhou Peoples R China Guangdong Engn Polytech Educ Technol & Informat Ctr 18 Yuxing Rd Guangzhou Peoples R China

ISBN: (纸本)9781450375511

Digital watermarking technology is an important technology for copyright protection. In practical applications, the robustness of digital watermarking technology is more demanding. In order to resist attacks and improve invisibility, a new data watermarking scheme is proposed. First, watermark local feature regions will be determined by an improved method which contains two steps: texture complexity roughly location and SIFT precisely location. Second, DCT transformation on these regions is applicated and a set of low frequency coefficients of chosen regions are selected to construct a DCT coefficient matrix. Last, SVD decomposition is performed on DCT coefficient matrix and a QR code carrying watermark information is embedded by the additive rule. The watermark detection algorithm is the reverse process of embedding. The experimental results show that the algorithm has good robustness to common attacks.

关键词： Digital watermark Robustness SIFT DCT SVD QR code

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

ISBN: (纸本)9783030691424

The proceedings contain 55 papers. The special focus in this conference is on Information and Communication Technology and Applications. The topics include: Application of Supervised Machine Learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems;anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers;Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF;A Conceptual Hybrid Model of Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) for Masquerade Attack Detection;An Automated Framework for Swift Lecture Evaluation Using Speech recognition and NLP;DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal;hausa Intelligence Chatbot System;an Empirical Study to Investigate Data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced Data;a Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value;application of Big Data Analytics for Improving Learning Process in Technical Vocational Education and Training;validation of Student Psychological Player Types for Game-Based Learning in University Math Lectures;outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score;an Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence;optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm;identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary pattern Features;feature Weighting and Classification Modeling for Network Intrusion Detection Using Machine Learning Algorithms;comparative Performance Analysis of Anti-virus Software.

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

引用

3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Discriminative BULBPH Descriptor with KDA for Palmprint recognition 3rd

Discriminative BULBPH Descriptor with KDA for Palmprint Reco...

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3rd international conference on Computer Vision and Image Processing (CVIP)

作者： Tamrakar, Deepti Khanna, Pritee Jabalpur Engn Coll Jabalpur India PDPM Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813290884;9789813290877

This work proposes Block-wise uniform local binary pattern histogram (BULBPH) followed by kernel discrimination analysis (KDA) as descriptor for palmprint recognition. BULBPH provides distribution of uniform patterns (such as line and wrinkles) in local region and can be better used as palmprint features. KDA is applied on BULBPH to reduce dimension and enhance discriminative capability using chi-RBF kernel. The experiments are conducted on four palmprint databases and performance is compared with related descriptors. It is observed that KDA on BULBPH descriptor achieves more than 99% accuracy with 4.04 decidability index on four palmprint databases.

关键词： Palmprint Blockwise uniform local binary pattern Kernel discriminant analysis

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3D Modeling of Riverbeds Based on NURBS Algorithm 20

3D Modeling of Riverbeds Based on NURBS Algorithm

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Yang, Kaiyuan Zhong, Caili Zhang, Xiaotian Zhu, Xiaohui Yue, Yong Xian Jiaotong Liverpool Univ Suzhou Peoples R China

ISBN: (纸本)9781450375511

Modelling and visualization of riverbeds can provide topographic features and sedimentation distribution of river systems, which is essential to support water environment management. We developed a novel approach for building 3-dimensional (3D) models and visualization of riverbeds based on a non-uniform Rational B-Spline (NURBS) algorithm. We used an Unmanned Surface Vehicle (USV) to collect water depth and GPS positions of a river system for modelling. A data reduction method was proposed to accelerate the modelling process while keeping the model accuracy. To obtain a more realistic 3D model of a riverbed, we applied an algorithm to optimize weight factors of control points. We achieved the algorithm on MATLAB, and experimental results show that the algorithm can visualize topographic features and sedimentation distribution of riverbeds in 3D models.

关键词： Riverbed visualization NURBS 3D modelling USVs Data reduction

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Proceedings of the 2020 3rd international conference on Artificial Intelligence and pattern recognition, AIPR 2020

Proceedings of the 2020 3rd International Conference on Arti...

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3rd international conference on Artificial Intelligence and pattern recognition, AIPR 2020

ISBN: (纸本)9781450375511

The proceedings contain 43 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret minimization for Bayesian optimization with student’s-t processes;a mining frequent itemsets algorithm in stream data based on sliding time decay window;experimental and theoretical scrutiny of the geometric derivation of the fundamental matrix;dual-precision deep neural network;annotating documents using active learning methods for a maintenance analysis application;offline handwritten Chinese character recognition based on improved Googlenet;a network combining local features and attention mechanisms for vehicle re-identification;and a spatial attention-enhanced multi-timescale graph convolutional network for skeleton-based action recognition.

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Proceedings of the 2020 3rd international conference on Artificial Intelligence and pattern recognition, AIPR 2020

Proceedings of the 2020 3rd International Conference on Arti...

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3rd international conference on Artificial Intelligence and pattern recognition, AIPR 2020

ISBN: (纸本)9781450375511

The proceedings contain 39 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret minimization for Bayesian optimization with student’s-t processes;research on unbalanced data processing algorithm base Tomeklinks-Smote;a mining frequent itemsets algorithm in stream data based on sliding time decay window;experimental and theoretical scrutiny of the geometric derivation of the fundamental matrix;annotating documents using active learning methods for a maintenance analysis application;offline handwritten Chinese character recognition based on improved Googlenet;a network combining local features and attention mechanisms for vehicle re-identification;and a spatial attention-enhanced multi-timescale graph convolutional network for skeleton-based action recognition.

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Experimental and Theoretical Scrutiny of the Geometric Derivation of the Fundamental Matrix 20

Experimental and Theoretical Scrutiny of the Geometric Deriv...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Basta, Tayeb Al Ghurair Univ Coll Engn & Comp Dubai U Arab Emirates

ISBN: (纸本)9781450375511

In this paper, we prove mathematically that the geometric derivation of the fundamental matrix.. of the two-view reconstruction problem is flawed. Although the fundamental matrix approach is quite classic, it is still taught in universities around the world. Thus, analyzing the derivation of F now is a non-trivial subject. The geometric derivation of E is based on the cross product of vectors in R-3. The cross product (or vector product) of two vectors is x x y where.. = < x(1), x(2), x(3)> and y = < y(1), y(2), y(3)> in R-3. The relationship between the skew-matrix of a vector.. in R-3 and the cross product is [t](x) y = t x y for any vector t in R3. In the derivation of the essential matrix we have E = [t](x).. which is the result of replacing t x R by [t](x) R, the cross product of a vector t and a 3x3 matrix R. This is an undefined operation and therefore the essential matrix derivation is flawed. The derivation of F, is based on the assertion that the set of all points in the first image and their corresponding points in the second image are protectively equivalent and therefore there exists a homography H-pi between the two images. An assertion that does not hold for 3D non-planar scenes.

关键词： Cross product skew-matrix essential matrix fundamental matrix epipolar geometry stereo vision 3D reconstruction

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Local Binary pattern Histogram for Face recognition in Student Attendance System 3

Local Binary Pattern Histogram for Face Recognition in Stude...

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3rd international conference on Computer and Informatics Engineering, IC2IE 2020

作者： Alexander, Allan D Salkiawati, Ratna Lubis, Hendarman Rahman, Fathur Herlawati, Herlawati Handayanto, Rahmadya Trias Universitas Bhayangkara Jakarta Raya Bekasi Dept. of Informatics Engineering Bekasi Indonesia Universitas Islam 45 Bekasi Dept. of Computer Engineering Bekasi Indonesia

ISBN: (纸本)9781728182476

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy. © 2020 IEEE.

关键词： Face recognition

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A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet 3

A Facial Expression Recongnition Method Based on Dlib, RI-LB...

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3rd international conference on Computer Information Science and Application Technology (CISAT)

作者： Yang, Jianxing Adu, Jianhua Chen, Hongguang Zhang, Jie Tang, Jianfeng Chengdu Univ Informat Technol Sch Software Engn Chengdu 611230 Sichuan Peoples R China

A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.

关键词： Local binary pattern

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