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image manipulation detection using harris corner and ANMS 1

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1st international conference on Latest Advances in machine learning and Data Science, LAMDA 2017

作者： Shyam Prakash, Choudhary Maheshkar, Sushila Maheshkar, Vikas Dhanbad India Netaji Subhas Institute of Technology Delhi India

ISBN: (数字)9789811085697

ISBN: (纸本)9789811085680

Due to the availability of media editing software, the authenticity and reliability of digital images are important. Region manipulation is a simple and effective method for digital image forgeries. Hence, the potential to identify the image manipulation is current research issue these days and copy–move forgery detection (CMFD) is a main domain in image authentication. In copy–move forgery, one region is simply copied and pasted over other region in the same image for manipulating the image. In this paper, we have proposed a method based on Harris corner and adaptive non-maximal Suppression (ANMS). Initially, the input image is taken, and then Harris corner detection algorithm is used to detect the interest points, and ANMS is adopted to control the number of Harris points in an image. This gives an appropriate number of interest points for different size of images and gives the assurance for finding the manipulated region in manageable time. For each extracted interest point, SIFT is used for calculating the descriptors. Now obtained descriptors are matched using the outlier rejection with nearest neighbour. Here RANSAC is used to find the best set of matches to identify the manipulated regions. Experimental results show the robustness against different transformation and post-processing operations. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Edge detection

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Selecting significant marker genes from microarray data by filter approach for cancer diagnosis 1

Selecting significant marker genes from microarray data by f...

引用

1st international conference on Intelligent Computing in Data Sciences (ICDS)

作者： Bouazza, Sara Haddou Auhmani, Khalid Zeroual, Abdelouhab Hamdi, Nezha Cadi Ayyad Univ Dept Phys Fac Sci Semlalia Marrakech Morocco Natl Sch Appl Sci Dept Ind Engn Cadi Ayyad Safi Morocco

In machine learning, feature subset selection phase is the process of selecting a small subset of the most relevant features for use in a model construction. The main goal of this paper is to perform a comparative study between features subset selection methods applied to the DNA microarray dataset and to investigate the strength of each method. The studied methods are: F test, T test, Signal to noise ratio (S/R), ReliefF and Pearson product-moment correlation coefficient (CC). This study is carried out using the dataset of the five cancers;Leukemia, Lung, Lymphoma, Central Nervous System and Ovarian cancers. The Evaluation of the studied methods has been done by using the supervised classifiers: K Nearest Neighbors (KNN), Support Vector machines (SVMs), Linear Discriminant Analysis (LDA), Decision Tree for Classification (DTC) and Naive Bayes classifier (NV). The purpose of classification is to predict the presence of cancer. The classification accuracy is measured for each selected subset of genes. Results show that the combination between S/R selection method and the KNN classifier present the highest accuracies for different cancers;100% for only 13 relevant genes in Leukemia cancer, 100% for 21 genes in Lung cancer, 100% for 4 genes in Lymphoma cancer, 76.7% for 6 genes in CNS cancer, 100% for 30 genes in ovarian cancer. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： Biochip data feature selection classification Data mining image processing

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Automated Chest X-ray image View Classification using Force Histogram 1st

Automated Chest X-ray Image View Classification using Force ...

引用

1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Santosh, K. C. Wendling, Laurent Univ South Dakota Dept Comp Sci 414 E Clark St Vermillion SD 57069 USA LIPADE Univ Paris Descartes Paris V 45 Rue St Peres F-75270 Paris 06 France

ISBN: (纸本)9789811048593;9789811048586

To advance and/or ease computer aided diagnosis (CAD) system, chest X-ray (CXR) image view information is required. In other words, separating CXR image view: frontal and lateral can be considered as a crucial step to effective subsequent processes, since the techniques that work for frontal CXRs may not equally work for lateral ones. With this motivation, in this paper, we present a novel machine learning technique to classify frontal and lateral CXR images, where we introduce a force histogram to extract features and apply three different state-of-the-art classifiers: support vector machine (SVM), random forest (RF) and multi-layer perceptron (MLP). We validated our fully automatic technique on a set of 8100 images hosted by National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%.

关键词： Automation Chest X-ray Force histograms image view Classification

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A Fast and Accurate 3D Fine-Tuning Convolutional Neural Network for Alzheimer's Disease Diagnosis 1st

A Fast and Accurate 3D Fine-Tuning Convolutional Neural Netw...

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1st CCF international conference on Artificial Intelligence (CCF-ICAI)

作者： Tang, Hao Yao, Erlin Tan, Guangming Guo, Xiuhua Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (纸本)9789811321221;9789811321214

The fast and accurate diagnosis of Alzheimer's Disease (AD) plays a significant part in patient care, especially at the early stage. The main difficulty lies in the three-class classification problem with AD, Mild Cognitive Impairment (MCI) and Normal Cohort (NC) subjects, due to the high similarity on brain patterns and image intensities between AD and MCI's Magnetic Resonance Imaging (MRI). So far, many studies have explored and applied various techniques, including static analysis methods and machine learning algorithms for Computer Aided Diagnosis (CAD) of AD. But there is still lack of a balance between the speed and accuracy of existing techniques, i.e., fast methods are not accurate while accurate algorithms are not fast enough. This paper proposes a new deep learning architecture to achieve the tradeoff between the speed and accuracy of AD diagnosis, which predicts three binary and one three-class classification in a unified architecture named 3D fine-tuning convolutional neural network (3D-FCNN). Experiments on the standard Alzheimer's disease Neuroimaging Initiative (ADNI) MRI dataset indicated that the proposed 3D-FCNN model is superior to conventional classifiers both in accuracy and robustness. In particular, the achieved binary classification accuracies are 96.81% and AUC of 0.98 for AD/NC, 88.43% and AUC of 0.91 for AD/MCI, 92.62% and AUC of 0.94 for MCI/NC. More importantly, the three-class classification for AD/MCI/NC achieves the accuracy of 91.32%, outperforming several state-of-the-art approaches.

关键词： Alzheimer's disease Deep learning Brain MRI

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Non-regularized state Preserving Extreme learning machine for Natural Scene Classification

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1st international conference on Computer Vision and image processing (CVIP)

作者： Sidike, Paheding Alom, Md. Zahangir Asari, Vijayan K. Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9789811021046;9789811021039

Scene classification remains a challenging task in computer vision applications due to a wide range of intraclass and interclass variations. A robust feature extraction technique and an effective classifier are required to achieve satisfactory recognition performance. Herein, we propose a nonregularized state preserving extreme learning machine (NSPELM) to perform scene classification tasks. We employ a Bag-of-Words (BoW) model for feature extraction prior to performing the classification task. The BoW feature is obtained based on a regular grid method for point selection and Speeded Up Robust Features (SURF) technique for feature extraction on the selected points. The performance of NSPELM is tested and evaluated on three standard scene category classification datasets. The recognition accuracy is compared with the standard extreme learning machine classifier and it shows that the proposed NSPELM algorithm yields better accuracy.

关键词： Scene classification Non-regularization Extreme learning machine Bag-of-words

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Hand-Drawn Symbol recognition in Immersive Virtual Reality Using Deep Extreme learning machines 1st

Hand-Drawn Symbol Recognition in Immersive Virtual Reality U...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Cecotti, Hubert Boumedine, Cyrus Callaghan, Michael Ulster Univ Fac Comp & Engn Magee CampusNorthland Rd Derry BT48 7JL Londonderry North Ireland

ISBN: (纸本)9789811048593;9789811048586

Typical character and symbol recognition systems are based on images that are drawn on paper or on a tablet with actual physical contact between the pen and the surface. In this study, we investigate the recognition of symbols that are written while the user is immersed inside a room scale virtual reality experience using a consumer grade head-mounted display and related peripherals. A novel educational simulation was developed consisting of a virtual classroom with whiteboard where users can draw symbols. A database of 30 classes of hand-drawn symbols created from test subjects using this environment is presented. The performance of the symbol recognition system was evaluated with deep extreme learning machine classifiers, with accuracy rates of 94.88% with a single classifier and 95.95% with a multiple classifier approach. Further analysis of the results obtained support the conclusion that there are a number of challenges and difficulties related to drawing in this type of environment given the unique constraints and limitations imposed by virtual reality and in particular the lack of physical contact and haptic feedback between the controller and virtual space. Addressing the issues raised for these types of interfaces opens new challenges for both human-computer interaction and symbol recognition. Finally, the approach proposed in this paper creates a new avenue of research in the field of document analysis and recognition by exploring how texts and symbols can be analyzed and automatically recognized in virtual scenes of this type.

关键词： Virtual reality Symbol recognition Extreme learning machine Multiple classifier systems

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Malware Class recognition Using image processing Techniques 1

Malware Class Recognition Using Image Processing Techniques

引用

1st IEEE international conference on Data Management, Analytics and Innovation (ICDMAI)

作者： Makandar, Aziz Patrot, Anita Karnataka State Womens Univ Dept Comp Sci Vijayapura Karnataka India

ISBN: (纸本)9781509040834

Increasing suspicious instructions of various malware through a challenge to the malware analysts to identify and classify samples belongs to the malicious family. They have witnessed the very fast increase in both the number and complexity of malware set of instructions. Malware invest profoundly in technology and capability to reorganize the process of building and mutate existing malware set of instructions to avoid traditional protection. Classify malware variants by applying image processing techniques. The textures play an important role in many image processing applications. In this paper we proposed the Support Vector machine (SVM) multiclass malware image classification challenge from an image processing perspective. The multi-resolution and wavelets are used to build effective texture feature vector using Gabor Wavelet, GIst and Discrete wavelet Transform and other features. The proposed algorithm experimented on Malimg Dataset of malware total 12,470 samples are used. In that 1610 samples are trained and 1710 samples are tested on 8 malware family which is randomly selected from the dataset. We compare this approach to existing malware classification approaches previously published research work. This is an efficient and more accurate malware detection algorithm using Wavelet Transform with machine learning classifiers techniques to detect malware samples more capably compare to existing work.

关键词： Gabor Wavelet Discrete Wavelet Transform Malware Support Vector machine Texture Analysis

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Robust Parametric Twin Support Vector machine and Its Application in Human Activity recognition

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1st international conference on Computer Vision and image processing (CVIP)

作者： Khemchandani, Reshma Sharma, Sweta South Asian Univ Fac Math & Comp Sci Dept Comp Sci New Delhi India

ISBN: (纸本)9789811021046;9789811021039

This paper proposes a novel and Robust Parametric Twin Support Vector machine (RPTWSVM) classifier to deal with the heteroscedastic noise present in the human activity recognition framework. Unlike Par-nu-SVM, RPTWSVM proposes two optimization problems where each one of them deals with the structural information of the corresponding class in order to control the effect of heteroscedastic noise on the generalization ability of the classifier. Further, the hyperplanes so obtained adjust themselves in order to maximize the parametric insensitive margin. The efficacy of the proposed framework has been evaluated on standard UCI benchmark datasets. Moreover, we investigate the performance of RPTWSVM on human activity recognition problem. The effectiveness and practicability of the proposed algorithm have been supported with the help of experimental results.

关键词： Human activity recognition Twin support vector machines Heteroscedastic noise machine learning

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international conference on Intelligent Computing and Applications 1st ed. 2018

引用

丛书名： Advances in Intelligent Systems and Computing

2018年

作者： Subhransu Sekhar Dash Swagatam Das Bijaya Ketan Panigrahi

ISBN: (数字)9789811055201

ISBN: (纸本)9789811055195

The bookis a collection of best papers presented in international conference on Intelligent Computing and Applications (ICICA 2016) organized by Department of Computer Engineering, D. Y. Patil College of Engineering, Pune, India during 20-22 December 2016. The book presents original work, information, techniques and applications in the field of computational intelligence,power and computing technology. This volume also talks about image language processing, computer vision and pattern recognition, machine learning, data mining and computational life sciences, management of data including Big Data and analytics, distributed and mobile systems including grid and cloud infrastructure.

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recognition of Speed Limits on Speed-Limit Signs by Using machine learning 24

Recognition of Speed Limits on Speed-Limit Signs by Using Ma...

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24th international conference on Mechatronics and machine Vision in Practice (M2VIP)

作者： Miyata, Shigeharu Kindai Univ Sch Engn Dept Robot Takayaumenobe 1 Higashihiroshima 7392116 Japan

ISBN: (纸本)9781509065462

This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using Hue, Saturation and Value (HSV) color spaces for extracting the speed limit numbers on the identified speed-limit signs, and finally (3) a method for recognition of the extracted numbers using a neural network. The method of traffic sign recognition previously proposed by the author consisted of extracting geometric shapes from the sign and recognizing them based on their aspect ratios. This method cannot be used for the numbers on speed-limit signs because the numbers all have the same aspect ratios. This study focuses on considering the following three points. (1) Make it possible to detect only the speed-limit sign in an image of scenery using a single process focusing on the local patterns of speed limit signs. (2) Make it possible to separate and extract the two-digit numbers on a speed-limit sign in cases when the two-digit numbers are incorrectly extracted as a single area due to the light environment. (3) Make it possible to identify the numbers using a neural network by focusing on three feature quantities. This study also used the proposed method with still images in order to validate it.

关键词： pattern recognition image processing computer vision traffic sign machine learning local binary pattern neural network

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