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DCGAN-based data augmentation for document classification 2

DCGAN-based data augmentation for document classification

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2nd IEEE international conference on Electronics, Control, Optimization and computer Science, ICECOCS 2020

作者： Jadli, Aissam Hain, Mustapha Chergui, Adil Jaize, Abderrahman University Hassan II SISM Team I2SI2E Laboratory ENSAM Casablanca Casablanca Morocco University Hassan II ENSAM Casablanca Casablanca Morocco University Hassan i LAVET Laboratory FST Settat Settat Morocco

ISBN: (纸本)9781728169217

Document classification is a relevant task within every intelligent document processing system. With the advances in deep learning and computer vision techniques, this task has become a painless and straightforward process. However, the need for labelled data is always a hurdle to tackle before constructing, training, and validating classification models. The use of Generative modelling implies using a model to generate new samples that are similar to the training set but individually different from existing records. In this paper, we investigate using deep convolutional adversarial networks (DCGAN) to generate fake document images using existing scanned documents dataset. Moreover, we used a dataset created of generated images alongside original images to train an image classifier using a convolutional neural network (CNN) to classify documents, and we compared the overall accuracy with a model constructed on an original dataset of the same size. The constructed model performed as well as the model trained with authentic data (with an accuracy score of 90% and 91% respectively). This excellent performance can permit using DCGANs to augment existing datasets in case of a lack of labelled data while maintaining similar performance levels. © 2020 IEEE.

关键词： computer vision

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Weakly Supervised image Classification Based on Attention Mechanism 2

Weakly Supervised Image Classification Based on Attention Me...

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2nd IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Cheng, Xiaohui Liu, Pengfei Chen, Shouxue Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin Peoples R China

ISBN: (纸本)9781728199481

At present, the research of image classification mainly focuses on the detection and classification of objects. However, most of the current classification methods still rely on a large number of pixel level manual annotation samples, which often has a greater impact on practical problems. This paper will focus on the task of image classification in the weakly supervised mode. By combining attention mechanism with convolution neural network, the model can extract salient regions in the image circularly, and then use the multi branch VGG deep neural network model to adjust the classification of the extracted features, so that the model can focus on the features of the salient region, so as to improve the ability of this model. The experimental results show that the improved weak supervised learning model can achieve 87.2% and 85.7% classification accuracy under the premise of reducing the use of artificial features, which means that it can achieve better image classification effect when compared with other advanced image classification methods.

关键词： image classification convolutional neural network attention mechanism weakly supervised learning computer vision

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Person Recognition Based on FaceNet under Simulated Prosthetic vision 2

Person Recognition Based on FaceNet under Simulated Prosthet...

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2019 2nd international Symposium on Big Data and Applied Statistics, ISBDAS 2019

作者： Zhao, Ying Yu, AiPing Xu, DanTong School of Information Engineering Inner Mongolia University of Science and Technology Baotou China

Face information is important information for identifying people, but at low resolution, face information is not well recognized. A psychological physics experiment of person recognition in daily life was designed to discover the best strategy for the limited number of stimulating electrodes, and provide useful visual information. The real-time image-processing strategy based on FaceNet were used to optimize the person information by turning complex face information into simple Chinese character information. Noted that all processed target faces were obscured by separate Chinese characters which are the target people's last names. The psychological results showed that the image-processing strategy based on FaceNet improved recognition accuracy. The proposed strategy, to convert complex face information into simple Chinese character information, could help subjects to use their own first knowledge to identify the person who need to be identified more faster and accurately. CCS Concepts •Human-centered computing Human computer interaction;•Computing methodologies Artificial intelligence computer vision computer vision tasks;•Social and professional topics. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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An adaptive image processing model of plant disease diagnosis and quantification based on color and texture histogram 2

An adaptive image processing model of plant disease diagnosi...

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2nd international conference on computer and Information Sciences, ICCIS 2020

作者： Ismail, Waqar Khan, Muhammad Attique Shah, Syed Adnan Javed, Muhammad Younus Rehman, Amjad Saba, Tanzila HITEC University Department of CS and E Taxila Pakistan University of Engineering and Technology Department of CS Taxila Pakistan AIDA Lab CCIS Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781728154671

In this paper, a new approach for the detection and classification of potato plant disease is implemented using computer vision techniques. Most of the existing algorithms based on plant disease detection and classification are limited to common types of feature extraction methods. However, feature extraction is an important area as the classification of diseases of any leaf. The proposed method is based on color and texture features. The implemented method processed in four steps- In the preprocessing and segmentation, LAB color space and Delta E color difference method are applied. Later, features are extracted based on RGB, HSV and Local Binary Patterns (LBP). The extracted patterns are finally classified by Multi Support Vector Machine (SVM). Moreover, we compare the results of feature subsets of RGB and HSV color features with the addition of LBP texture features and found a classification difference of 3.6% between RGB and HSV color feature extractors. The overall results show our method outperforms as compared to existing techniques. © 2020 IEEE.

关键词： Feature extraction

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2nd international conference on Machine Learning, image processing, Network Security and Data Sciences, MInd 2020

2nd International Conference on Machine Learning, Image Proc...

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2nd international conference on Machine Learning, image processing, Network Security and Data Sciences, MInd 2020

ISBN: (纸本)9789811563140

The proceedings contain 83 papers. The special focus in this conference is on Machine Learning, image processing, Network Security and Data Sciences. The topics include: Distance Invariant RGB-D Object Recognition Using DSMS System;Single Look SAR image Segmentation Using Local Entropy, Median Low-Pass Filter and Fuzzy Inference System;An Edge Detection and Sliding Window Based Approach to Improve Object Localization in YOLOv3;automatic Detection of Pneumonia from Chest X-Rays Using Deep Learning;an Improved Accuracy Rate in Microaneurysms Detection in Retinal Fundus images Using Non-local Mean Filter;emotion Recognition from Periocular Features;a Comparative Study of Computational Intelligence for Identification of Breast Cancer;segmentation of Blood Vessels from Fundus image Using Scaled Grid;head Pose Estimation of Face: Angle of Roll, Yaw, and Pitch of the Face image;identifying Fake Profile in Online Social Network: An Overview and Survey;enhancing and Classifying Traffic Signs Using computer vision and Deep Convolutional Neural Network;diabetic Retinopathy Detection on Retinal Fundus images Using Convolutional Neural Network;medical image Fusion Based on Deep Decomposition and Sparse Representation;rice Plant Disease Detection and Classification Using Deep Residual Learning;Flood Detection Using Multispectral images and SAR Data;Handwritten Character Recognition Using KNN and SVM Based Classifier over Feature Vector from Autoencoder;a ConvNet Based Procedure for image Copy-Move Forgery Detection;a Novel Invisible Watermarking Approach for Double Authentication of image Content;remote Sensing Signature Classification of Agriculture Detection Using Deep Convolution Network Models;buddhist Hasta Mudra Recognition Using Morphological Features.

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Effect of Blurring on Identification of Aerial images Using Convolution Neural Networks 2nd

Effect of Blurring on Identification of Aerial Images Using ...

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2nd international conference on Recent Innovations in Computing (ICRIC)

作者： Mahajan, Palak Abrol, Pawanesh Lehana, Parveen K. Univ Jammu Jammu Jammu & Kashmir India

ISBN: (纸本)9783030294076;9783030294069

The emergence of deep learning in the field of computer vision has led to extensive deployment of convolutional neural networks (CNNs) in visual recognition systems for feature extraction. CNNs provide learning through hierarchical inferencing by providing multilayer architecture. Due to high processing capability of CNNs in multidimensional signals like images, they are considered to be predominant artificial neural networks. CNNs are extensively used in computer vision such as in image recognition where the intent is to automatically learn features followed by generalization and eventually recognizing the learned features. In this paper, we investigate the efficiency of CNNs: AlexNet and GoogLeNet under the effect of blurring which occurs frequently during image capturing process. Here, Gaussian blurring is used since it minimizes the noise embedded into the image. For experimentation, UC Merced Land Use aerial dataset is used to evaluate CNNs' performance. The focus is to train these CNNs and classifying an extensive range of classes accurately under the influence of Gaussian blurring. Accuracy and loss are the parameters of classification considered for evaluating the performance of CNNs. Experimental results validated the susceptibility of CNNs towards blurring effect with GoogLeNet being more fluctuating to varied degrees of Gaussian blurring than AlexNet.

关键词： Deep learning Convolution neural networks Gaussian blurring image classification

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Design and implementation of aluminum profile cross-section comparison system 2020

Design and implementation of aluminum profile cross-section ...

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2nd international conference on Artificial Intelligence and Advanced Manufacture, AIAM 2020

作者： Hongwei, Tian Xuelan, Liu Department of Computer Science Applied Technology College of Soochow University Kunshan China School of Agricultural Information Jiangsu Agri-animal Husbandry Vocational College Taizhou China

ISBN: (纸本)9781450372022

Aiming at the situation that QR code identification and RFID identification cannot effectively used in aluminum profile warehouse management, it is proposed to use image processing technology to compare and identify aluminum profiles. The contour of the aluminum profile cross sections is obtained by spatial domain filtering and morphological methods. And then is positioned by artificial selection of seed points and flood filling. Finally, the Hu moment is used for profile matching. The entire algorithm is implemented using VS2017 and OpenCV to achieve the preliminary comparison goal. © 2020 ACM.

关键词： image processing

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Classification of Microscopic Algae: An Observational Study with AlexNet 2nd

Classification of Microscopic Algae: An Observational Study ...

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2nd international conference on Soft Computing and Signal processing, ICSCSP 2019

作者： Pardeshi, Rajmohan Deshmukh, Prapti D. Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University AurangabadMS India MGM Dr. G. Y. Pathrikar College of Computer Science and IT AurangabadMS India

ISBN: (纸本)9789811524745

Classification of algae is one of challenging task due to its micro-size and similarity in shapes. Therefore, the manual taxonomic classification of algae is very difficult and needs high-level expertise. In this case, it becomes error prone and tidy task for biologist. In this paper, we presented high end, fast, accurate and efficient system based on image processing and machine learning algorithms for classification of microscopic algae. We have applied convolutional neural networks by using AlexNet architecture on publicly available dataset of microscopic algae. We have achieved the encouraging results with very small training dataset, while using the deep learning. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image classification

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3AU-Net: Triple Attention U-Net for Retinal Vessel Segmentation 2

3AU-Net: Triple Attention U-Net for Retinal Vessel Segmentat...

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2nd IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Jin, Logan Suzhou China

ISBN: (纸本)9781728199481

Retinal Vessel segmentation is an indispensable part of the task of the automatic detection of retinopathy through fundus images, while there are several challenges, such as lots of noise, low distinction between blood vessels and environment, and uneven distribution of thick and thin blood vessels. Deep learning-based methods represented by U-Net performs very well on the task of retinal vessel segmentation. As the attention mechanism has made breakthroughs in many computer vision tasks, it has attracted the attention from the researcher. This paper proposed a kind of U-Net network based on triple attention mechanism-3AU-Net to overcome the problems of retinal vessel segmentation. We follow the framework of U-Net 's full convolution and skip connection, integrating spatial attention mechanism with channel attention mechanism and context attention mechanism. Spatial attention allows the segmentation network to find the blood vessel region that needs attention, thereby suppressing noise. Channel attention can make the expression of features more diverse and highlight the feature channels with key information. The context attention can integrate the context information to make the network to focus on the key pixels. Experimental consequences have indicated that 3AU-Net can greatly improve the results of the segmentation of retinal blood vessels, and this method surpasses other deep learning-based methods in many indicators on the DRIVE and STARE fundus image data sets. On the DRIVE data set, the 3A-UNet model achieved excellent performance on multiple evaluation indicators, with an ACC score of 0.9592, an AUC score of 0.9770, and a sensitivity score of 0.8537.

关键词： deep learning medical image Fundus image processing retinal vessel segmentation

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Answering Questions in Natural Language About images Using Deep Learning 2nd

Answering Questions in Natural Language About Images Using D...

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2nd international conference on Futuristic Trends in Networks and Computing Technologiess, FTNCT 2019

作者： Singh, Vedant Doshi, Vatsal Dave, Mitali Desai, Ankit Agrawal, Smith Shah, Jash Kanani, Pratik Dwarkadas J. Sanghvi College of Engineering University of Mumbai Mumbai India

ISBN: (纸本)9789811544507

Visual Question Answering is a perfect mix of issues enveloping different spaces including Natural Language processing, computer vision and knowledge portrayal. The problem involves giving an image and a natural language question as an input to the computer, process them together and give an accurate answer to the question in the context of the image as the output. The answer can be a single word answer, phrase or sentence depending on the question and the image. We explore the various approaches used by global teams to deal with this problem and the specifications of the publicly available dataset in order to analyze the feasibility and scope of this domain. This technology finds its use in helping blind people in object recognition using voice commands. It may also be used by physicians and medical practitioners to confirm or validate their diagnosis about medical imagery. Since this field is relatively new the possibilities are endless when it comes to datasets, algorithms and accuracy achieved. We aim at understanding this expanse of possibilities at hand and develop conclusive ideas about its further growth. © 2020, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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