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Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single image Super Resolution 4

Six Convolutional Layered Deep Convolutional Neural Network ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Devi, M. Shyamala Pandian, J. Arun thakur, Rahul Kumar Vemuri, Vinod Babu Bharath, Javvaji Naga Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350300888

the super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier Graphics processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. the proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak Signal-to-Noise Ratio. the dataset extracted from ***(http://***/Research/Projects/CS/vision/grouping/BSR/BSR_***). the dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 ∗ 300 pixels. the proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. the processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak Signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak Signal to Noise Ratio of 48 dB when compared to other optimized output layers. © 2023 IEEE.

关键词： Graphics processing unit

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A Comprehensive Survey on image Captioning Techniques

A Comprehensive Survey on Image Captioning Techniques

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Artificial Intelligence, Internet and Digital Economy (ICAID), international conference on

作者： Qiheng Sun Key Laboratory of Linguistic and Cultural Computing Ministry of Education Nothwest Minzu University Lanzhou China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

image captioning technology is an intersection of computer vision and natural language processing, which utilizes computer vision-related techniques to extract semantic information embedded in images, and then transforms this semantic information into human-understandable text descriptions through natural language processing-related techniques, which promotes innovations in the fields of humancomputer interaction, accessibility assistive technology, and intelligent multimedia retrieval. In this paper, according to the time course of the development of image captioning technology, the technology is classified into template-based methods, retrievalbased methods, and deep learning-based methods according to the different image description methods proposed in 21 literatures, and these methods are introduced and summarized in turn, followed by the introduction of commonly used scoring metrics and datasets in image captioning technology.

关键词： Surveys Measurement Learning systems computer vision Technological innovation Reviews Semantics Transforms Natural language processing Internet

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FundusGAN: A One-Stage Single Input GAN for Fundus Synthesis 5th

FundusGAN: A One-Stage Single Input GAN for Fundus Synthesis

引用

5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Cai, Chao Xia, Xue Fang, Yuming Jiangxi Univ Finance & Econ Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Annotating medical images, especially fundus images that contain complex structures, needs expertise and time. To this end, fundus image synthesis methods were proposed to obtain specific categories of samples by combining vessel components and basic fundus images, during which well-segmented vessels from real fundus images were always required. Being different from these methods, We present a one-stage fundus image generating network to obtain healthy fundus images from scratch. First, we propose a basic attention Generator to present both global and local features. Second, we guide the Generator to focus on multi-scale fundus texture and structure features for better synthesis. third, we design a self-motivated strategy to construct a vessel assisting module for vessel refining. By integrating the three proposed sub-modules, our fundus synthesis network, termed as FundusGAN, is built to provide one-stage fundus image generation without extra references. As a result, the synthetic fundus images are anatomically consistent with real images and demonstrate both diversity and reasonable visual quality.

关键词： Medical image processing Retinal fundus Generative adversarial networks Synthesis

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Improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection 4th

Improved Classification Techniques Using Convolutional Neura...

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Saxena, Swati Gohil, Bhavesh N. Sardar Vallabhbhai Natl Inst Technol SVNIT Surat Gujarat India

ISBN: (纸本)9783031837951;9783031837968

Rice, one of the most important crops globally, serves as a vital nutritional source for over 50% of the world's population. However, it is highly susceptible to various diseases that can severely impact both the quality and quantity of the harvest, sometimes causing a reduction of 20%-40% in crop yield. Timely detection and accurate identification of rice plant diseases are critical for ensuring a healthy harvest. Farmers must be able to recognize these diseases visually, but daily field surveys are labor-intensive, time-consuming and costly, ultimately driving up the price of rice for consumers. Rice leaves are prone to several bacterial, viral and fungal diseases, which significantly affect rice production. To meet the growing global demand for rice, effective disease detection is essential. However, challenges in disease recognition arise due to background noise and varying capture conditions in images. Convolutional Neural Network (CNN)-based models have significantly advanced the detection and identification of rice plant diseases. this paper aims to achieve efficient and timely detection and identification of rice plant diseases by exploring the CNN models, both with and without segmentation techniques, for diseases such as Brown Spot, Leaf Blast and Neck Blast.

关键词： Rice Disease image processing Segmentation Techniques Convolutional Neural Networks

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Knowledge Distillation with Attention Mechanism for Anomaly Detection 4

Knowledge Distillation with Attention Mechanism for Anomaly ...

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4th international conference on Signal processing and computer Science, SPCS 2023

作者： Li, Mohan Lyu, Xiang Guo, Xuan DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781510672505

In the industrial domain, accurate detection and localization of abnormal images are crucial factors for ensuring production efficiency and quality. In recent years, methods in the field of unsupervised anomaly detection have predominantly centered around knowledge distillation models with a teacher-student structure. Although the application of knowledge distillation has significantly improved the precise identification of abnormal regions compared to traditional methods, the currently best-performing knowledge distillation models exhibit identical architectures for both the teacher and student networks. this limitation hampers the effective lightweighting of the student network and poses challenges in distinguishing between background and target objects in certain data with backgrounds, thereby affecting accuracy and wasting computational resources. To address this issue, we propose an innovative approach that integrates knowledge distillation with attention mechanisms for identifying abnormal regions in industrial anomaly images. Our method comprises two key features: firstly, transferring knowledge from a pretrained teacher network to a student network to enhance performance;secondly, incorporating attention mechanisms to direct the model's focus towards potential abnormal regions, thereby enhancing detection accuracy. Our approach innovatively combines knowledge distillation with attention mechanisms, offering a novel solution for industrial anomaly image recognition. through experimental validation, our method demonstrates superior performance compared to the original models, providing fresh insights for the field of industrial anomaly image analysis. © 2023 SPIE.

关键词： Anomaly detection

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Deep Learning for Fine-Grained image recognition: A Comprehensive Study 2022

Deep Learning for Fine-Grained Image Recognition: A Comprehe...

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4th Asia Pacific Information Technology conference, APIT 2022

作者： Kang, Yuhua Chao, Guoqing Hu, Xin Tu, Zhiying Chu, Dianhui Department of Computer Science Harbin Institute of Technology Weihai China

ISBN: (纸本)9781450395571

In computer vision, image recognition is a noteworthy and hot research area which develops rapidly. the principal task of this technique is to automatically predict which pre-defined categories an image might belong to. Traditional image recognition targets to classify images into diversified highly distinguished categories. However, Fine-Grained image recognition (FGIR) aims to recognize the variances among images categorized in subordinate classes, e.g., species of birds, types of cars or species of flowers, which are equivalent to "species"in Taxonomy in certain aspects. As a result, models of FGIR are required to pick out features from finer granularity. Conventional methods apply special feature encoding to explore discernible attributes, while recent methods of FGIR makes great advancement with assistance of deep learning which has obtained the remarkable development nowadays. In this paper, we provide a new integration of the current leading FGIR models according to how they improve the development of FGIR. We classified them into five main categories and then compared their performance on three popular datasets and analyzed the results. To advance the further development of this topic, we point out some open problems worth further exploring. © 2022 ACM.

关键词： computer vision

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A Method of Cell Counting Based on computer vision 12

A Method of Cell Counting Based on Computer Vision

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12th international conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Ding, Shihong Zhao, Huanyu Han, Zhi Tang, Yandong Shenyang Ligong University CO Shenyang110158 China The First Affiliated Hospital of China Medical University CO Shenyang110002 China Shenyang Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Robotics CO Shenyang110016 China Institutcs for Robotics and Intelligent Manufacturing Chinese Academy of Sciences CO Shenyang110168 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

For a long time, making machines have the ability of pattern recognition is the direction of scientists' continuous research. the study of pattern recognition is the fundamental way to understand the essence of artificial intelligence. At present, computer vision has been used to identify and count cells. In view of the problems existing in the field of cell counting, such as difficulties in identifying cells, dense cells, viscosity, difficult cell counting, inaccurate judgment and so on, this paper realizes the identification and counting through the technology of computer vision, determines the types of cells, and explores the characteristics of dense and sticky cells, At the same time, based on the characteristics of dense and sticky cells, a color based threshold segmentation algorithm is introduced to optimize the performance of cell classification. At the same time, the recognition and classification of different regions of adhesive cells are carried out, and the adhesive cells in different regions are recognized and judged by the technology of computer vision. through gray-scale transformation, threshold segmentation, morphological processing and LOG algorithm filtering of medical cell image, the cell region is extracted and counted. the cell counting is realized by calculating the connected domain. the experimental results preliminarily verify the effectiveness of this method, but it still needs to be improved. © 2022 IEEE.

关键词： Cells

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Application of image segmentation technology in TCM eye diagnosis 16

Application of image segmentation technology in TCM eye diag...

引用

16th international conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Peng, Hong Niu, Ning Zhang, Yilin Wang, Guanjun Xue, Chenyang Huang, Mengxing Hainan University Haikou City China North University of China Taiyuan City China

ISBN: (纸本)9781510667341

image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network, merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features © 2023 SPIE.

关键词： image segmentation

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Capturing Prior Knowledge in Soft Labels for Classification with Limited or Imbalanced Data 5th

Capturing Prior Knowledge in Soft Labels for Classification ...

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5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhong, Zhehao Zhao, Shen Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Key Lab Machine Intelligence & Adv Comp MOE Guangzhou Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Successful applications of deep learning often depend on large amount of training data. However, in practical image recognition tasks, available training data are often limited or imbalanced across classes, causing the over-fitting issue or the prediction bias issue during model training. In this paper, based on word embedding models from studies in natural language processing, the prior knowledge about the relationships between image classes is utilized to help train more generalizable classifiers under the condition of limited or class-imbalanced training data. Such inter-class relational knowledge is captured in the word embedding vectors for the textual names of image classes. Using these word embedding vectors as soft labels for corresponding image classes, the feature extractor part of a deep learning model can be guided to learn to extract visual features which contain both class-specific and class-shared information. Experiments on multiple image classification datasets confirm that the proposed learning framework helps improve model performance when training data is limited or class-imbalanced.

关键词： Prototype learning image classification Limited data Imbalance data Soft label

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Weighted Features Based Classification of Polarimetric SAR images

Weighted Features Based Classification of Polarimetric SAR I...

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Iranian Machine vision and image processing (MVIP)

作者： Fatemeh Saneipour Maryam Imani Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. the weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. the experiments on the L-band AIRSAR dataset show appropriate classification results.

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