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image augmentation-Based Food Recognition with Convolutional Neural Networks

Computers, Materials & Continua 2019年第4期59卷 297-313页

作者： Lili Pan Jiaohua Qin Hao Chen Xuyu Xiang Cong Li Ran Chen College of Computer Science and Information Technology Central South University of Forestry and TechnologyChangsha410004China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China

image retrieval for food ingredients is important work,tremendously tiring,uninteresting,and *** vision systems have extraordinary advancements in image retrieval with CNNs *** it is not feasible for small-size food datasets using convolutional neural networks *** this study,a novel image retrieval approach is presented for small and medium-scale food datasets,which both augments images utilizing image transformation techniques to enlarge the size of datasets,and promotes the average accuracy of food recognition with state-of-the-art deep learning ***,typical image transformation techniques are used to augment food *** transfer learning technology based on deep learning is applied to extract image ***,a food recognition algorithm is leveraged on extracted deepfeature *** presented image-retrieval architecture is analyzed based on a smallscale food dataset which is composed of forty-one categories of food ingredients and one hundred pictures for each *** experimental results demonstrate the advantages of image-augmentation architecture for small and medium datasets using deep *** novel approach combines image augmentation,ResNet feature vectors,and SMO classification,and shows its superiority for food detection of small/medium-scale datasets with comprehensive experiments.

关键词： image augmentation small-scale dataset deep feature deep learning convolutional neural network

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OffSig-SinGAN: A Deep Learning-Based image augmentation Model for Offline Signature Verification

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Computers, Materials & Continua 2023年第7期76卷 1267-1289页

作者： M.Muzaffar Hameed Rodina Ahmad Laiha Mat Kiah Ghulam Murtaza Noman Mazhar Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur46400Malaysia Department of Computer Science Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Sukkur Institute of Business Administration UniversitySukkur65200Pakistan

Offline signature verification(OfSV)is essential in preventing the falsification of *** learning(DL)based OfSVs require a high number of signature images to attain acceptable ***,a limited number of signature samples are available to train these models in a real-world *** researchers have proposed models to augment new signature images by applying various ***,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature ***,augmenting a sufficient number of signatures with variations is still a challenging *** study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature *** proposed model is capable of augmenting better quality signatures with diversity from a single signature image *** is empirically evaluated on widely used public datasets;*** quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy *** results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation *** improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

关键词： Signature forgery detection offline signature verification deep learning image augmentation generative adversarial networks

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Knowing Your Dog Breed:Identifying a Dog Breed with Deep Learning

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International Journal of Automation and computing 2021年第1期18卷 45-54页

作者： Punyanuch Borwarnginn Worapan Kusakunniran Sarattha Karnjanapreechakorn Kittikhun Thongkanchorn Faculty of Information and Communication Technology Mahidol UniversityNakhon Pathom 73170Thailand

Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural *** paper presents a solution for identifying dog breeds using their images of their *** proposed method applies a deep learning based approach in order to recognize their *** method begins with a transfer learning by retraining existing pretrained convolutional neural networks(CNNs)on the public dog breed ***,the image augmentation with various settings is also applied on the training dataset,in order to improve the classification *** proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental *** proposed model achieves a promising accuracy of 89.92%on the published dataset with 133 dog breeds.

关键词： Computer vision deep learning dog breed classification transfer learning image augmentation

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Multilevel augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2273-2288页

作者： A.Deepak Kumar T.Sasipraba Faculty of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India

Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andﬁnding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overﬁt-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identiﬁcation using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.

关键词： image segmentation diabetic retinopathy image augmentation semantic segmentation CNN

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