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Development of Plant-Leaf Disease Classification Model using Convolutional Neural Network 4

Development of Plant-Leaf Disease Classification Model using...

引用

4th International conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Shanker, Ravi Sharma, Diwakar Bhattacharya, Mahua Visual Information Processing Lab ABV-IIITM Gwalior M.P. Gwalior India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Agriculture is India's primary source of income and the second largest producer of agriculture products. Crop production is influenced by various factors, like weather, soil conditions, and multiple illnesses in plant leaves. We have presented plant disease detection in this work utilising image processing techniques. Automated plant disease detection research is critical in agriculture for tracking broad areas of crops. Consequently, it detects illness signals as soon as they emerge on plant leaves. The existing research makes complex use of image preprocessing and is unable to detect plant leaf disease efficiently. We have developed a method for the classification of leaves in healthy and disease-affected leaves using a convolutional neural network (CNN) of 28 layers. The models were trained and validated using an available open dataset of 87.7K RGB images of various plants in a set of 38 different classes of disease and healthy leaf images of plants. We have achieved 98.13% accuracy for the classification of leaves. The suggested deep learning model effectively classifies the plant leaves. © 2022 ieee.

关键词： Convolutional neural networks

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Automatic image Generation of Peking Opera Face using StyleGAN2

Automatic Image Generation of Peking Opera Face using StyleG...

引用

2022 International conference on Culture-Oriented Science and Technology, CoST 2022

作者： Xin, Xiaoyu Shen, Yinghua Xiong, Rui Lin, Xiahan Yan, Ming Jiang, Wei Communication University of China School of Information and Communication Engineering Beijing China Center for Ethnic and Folk Literature and Art Development Ministry of Culture and Tourism of the P.R.C Beijing China Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

image generation technology, which is often used in various applications of intelligent image generation, can learn the feature distribution of real images and sample from the distribution to obtain the generated images with high fidelity. This paper focuses on the feature extraction and intelligent generation techniques of Peking opera face with Chinese cultural characteristics. Based on the creation of a Peking opera face dataset, this paper compares the impact of different variants of a Style-based generator architecture for Generative Adversarial Networks (StyleGAN2) and different sizes of datasets on the quality of face generation. The experimental results verify that the synthetic images generated by StyleGAN2 with the addition of the Adaptive Discriminator Augmentation (ADA) module are visually better and have good local randomness when the dataset is small and unbalanced in distribution. © 2022 ieee.

关键词： Generative adversarial networks

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Diabetic Retinopathy Diagnosis with image processing

Diabetic Retinopathy Diagnosis with Image Processing

引用

ieee Signal processing and communications Applications (SIU)

作者： Mehmet Alper Şahin Ömer Faruk Beyca Büyük Veri ve İş Analitiği İstanbul Teknik Üniversitesi İstanbul Türkiye Endüstri Mühendisliğ İstanbul Teknik Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

This study focuses on developing deep learning-based models and improving their performance through deep learning techniques for the early diagnosis of “Diabetic Retinopathy”, a disease that develops due to diabetes and may cause vision loss in the eye in later stages. Deep learning models were developed using image processing methods on the limited access “Brazilian Multi-Label Ophthalmological Dataset”. Deploying AlexNet model, classification of diabetic retinopathy, macular edema, scar, nevus, drusens, increased cup disc, myopic fundus and age-related macular degeneration made based on fundus photographs. Models were trained with three different approaches for diabetic retinopathy and other eye diseases that may be associated with diabetic retinopathy. The developed method is aimed to facilitate decision-making by expert ophthalmologists in the diagnosis of diabetic retinopathy and other eye diseases and to prevent possible misdiagnosis. Using sensitivity-precision parameters as model success metrics in unbalanced test data sets has increased the accuracy in diagnosing diseased data. For the balanced test data set, the diagnosis of diabetic retinopathy was predicted with an accuracy of 0.748 and an f1 score of 0.726.

关键词： Deep learning Measurement Macular degeneration Diabetic retinopathy Accuracy image processing Decision making

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JNDMix: Jnd-Based Data Augmentation for No-Reference image Quality Assessment

JNDMix: Jnd-Based Data Augmentation for No-Reference Image Q...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiamu Sheng Jiayuan Fan Peng Ye Jianjian Cao Academy for Engineering and Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China

Despite substantial progress in no-reference image quality assessment (NR-IQA), previous training models often suffer from over-fitting due to the limited scale of used datasets, resulting in model performance bottlenecks. To tackle this challenge, we explore the potential of leveraging data augmentation to improve data efficiency and enhance model robustness. However, most existing data augmentation methods incur a serious issue, namely that it alters the image quality and leads to training images mismatching with their original labels. Additionally, although only a few data augmentation methods are available for NR-IQA task, their ability to enrich dataset diversity is still insufficient. To address these issues, we propose a effective and general data augmentation based on just noticeable difference (JND) noise mixing for NR-IQA task, named JNDMix. In detail, we randomly inject the JND noise, imperceptible to the human visual system (HVS), into the training image without any adjustment to its label. Extensive experiments demonstrate that JNDMix significantly improves the performance and data efficiency of various state-of-the-art NR-IQA models and the commonly used baseline models, as well as the generalization ability. More importantly, JNDMix facilitates MANIQA to achieve the state-of-the-art performance on LIVEC and KonIQ-10k.

关键词： image quality Training visual systems Signal processing Data models Robustness Acoustics

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Activation Map-based Vector Quantization for 360-degree image Semantic Communication

Activation Map-based Vector Quantization for 360-degree Imag...

引用

ieee conference on Global communications (GLOBECOM)

作者： Yang Ma Wenchi Cheng Jingqing Wang Wei Zhang State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Electrical Engineering and Telecommunications The University of New South Wales Sydney Australia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus enhancing the visual experience of the user. However, the voluminous data generated by 360-degree images poses challenges in network storage and bandwidth. To address these challenges, we propose a novel Activation Map-based Vector Quantization (AM-VQ) framework, which is designed to reduce communication overhead for wireless transmission. The proposed AM-VQ scheme uses the Deep Neural Networks (DNNs) with vector quantization (VQ) to extract and compress semantic features. Particularly, the AM-VQ framework utilizes an activation map to adaptively quantize semantic features, thereby reducing data distortion caused by quantization. To further enhance the reconstruction quality of the 360-degree image, adversarial training with a Generative Adversarial Networks (GANs) discriminator is incorporated. Numerical results show that our proposed AM-VQ scheme achieves better performance than the existing Deep Learning (DL) based coding and the traditional coding schemes under the same transmission symbols.

关键词： Training Wireless communication visualization image coding Codecs Vector quantization Artificial neural networks Feature extraction Semantic communication Encoding

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A Novel image Deep Learning for Recognition of Silicon Photonics Redistribution Layer

A Novel Image Deep Learning for Recognition of Silicon Photo...

引用

Electronic communications, Internet of Things and Big Data (ICEIB), International conference on

作者： Jen-Kuang Fang Chao-Kai Hung Peng Yang Wen-Long Lu Chien-Wei Chang Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Due to the design requirements for miniaturization and high-density placement of silicon photonics (SiPh) components in integrated circuit (IC) packaging, the system in package (SiP) process and equipment have become important but challenging. Automatic optical inspection (AOI) detection equipment in IC packaging lines is also important in identifying redistribution layer (RDL) anomalies that affect signal transmission. If the AOI misrecognition rate is too high, the production yield and overall equipment performance are lowered. To solve the above problems, we proposed a novel Pix2Pix generative adversarial network (GAN) algorithm based on machine learning methods. The new feature extraction method has demonstrated highly sensitive image detection capabilities after processing tens of thousands of huge SiPh RDL image databases.

关键词： Deep learning System-in-package image recognition Production Generative adversarial networks Silicon photonics Optical imaging

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A Cognitive Information processing Pipeline for Multiple-Access Vehicular Camera Communication 8

A Cognitive Information Processing Pipeline for Multiple-Acc...

引用

8th International conference on Smart Computing and communications (ICSCC)

作者： Ashraf, Khadija Ashok, Ashwin Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781728196879

The ability to perceive safety-critical events by virtually seeing through vehicles and other obstructions on the road can be a very useful driving assistance feature for vehicles. In this paper, we first hypothesize that such a feature can be achieved by vehicles driving ahead proactively communicate information about the visual scenery they perceive using dashboard cameras. Using such a multiple access setting, and considering brake-light light emitting diode (LED) to camera communication as the enabler, in this paper, we position an information processing pipeline for predicting the non-line-of-sight (NLOS) safety event. In particular, we present the algorithmic design of the cognitive information processing system that will continuously warn the host driver about the line-of-sight (LOS) and non-line-sight road situations. We position the use-case of our proposed information processing pipeline through a case-study analysis for a real-world driving scenario.

关键词： multiple access visible Light Communication (VLC) cognitive information processing driving Advanced driver-assistance systems (ADAS)

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Indoor visible light positioning algorithm based on point and line feature visual information in occluding environment

Indoor visible light positioning algorithm based on point an...

引用

Networks, communications and Intelligent Computing (NCIC), International conference on

作者： Xiangyun Ji Xu Bao School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (数字)9798350371949

ISBN: (纸本)9798350371956

This paper proposes a method to restore the pixel coordinate points under different occlusions of rectangular lights, which can solve the problem of capturing incomplete light emitting diode(LED) images and unable to deal with positioning. The experimental results show that compared with the unoccluded situation, the positioning accuracy under general occlusion can reach an average positioning error of 2.09cm, and the positioning accuracy under severe occlusion can reach an average positioning error of 2.43cm. Noise in visual positioning affects the positioning results, algorithm processing and mathematical modeling can optimize the positioning accuracy, reducing the influence of positioning error caused by image noise by 60%.

关键词： visualization Noise Light emitting diodes Mathematical models Data models image restoration

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Active Perception System for Enhanced visual Signal Recovery Using Deep Reinforcement Learning

Active Perception System for Enhanced Visual Signal Recovery...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gaurav Chaudhary Laxmidhar Behera Tushar Sandhan Department of Electrical Engineering IIT Kanpur

Deep neural networks have demonstrated excellent object detection and segmentation performance from RGB data. However, these models can only recognize and predict segmentation masks with great accuracy when RGB data have sufficient information about the objects of interest. In this paper, we suggest an intelligent, active perception system that can adjust its 3D position to improve signal acquisition. The segmentation score of cluttered scene is improved a lot due to this proposed system, which can also enhance grasp pose detection for the robotic manipulator. The ResNet-50 backbone of the proposed perception system is initialized using pre-trained weights to extract a latent state from an RGB image of the cluttered scene. A Reinforcement Learning (RL) agent uses these retrieved states to reposition the visual perception system for enhancement of the underlying computer vision tasks such as segmentation of the cluttered scene. Our trained RL agent can anticipate the better position of the visual perception system, which ensures enhanced signal recovery. The effectiveness of the proposed approach is tested in a pybullet simulation environment.

关键词： Deep learning visualization image segmentation Computer vision Three-dimensional displays Active perception Reinforcement learning

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Influence of visual Observations’ Dimensionality Reduction on a Deep Reinforcement Learning Controlled Terrestrial Robot

Influence of Visual Observations’ Dimensionality Reduction ...

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ieee International conference on Automation Science and Engineering (CASE)

作者： Luiz Afonso Marão Luís Eduardo Cintra Glauco Caurin AeroTech Lab of the São Carlos School of Engineering University of São Paulo São Carlos SP Brazil

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This article presents simulation results addressing the use of dimensionality reduction, on the visual observations, for a terrestrial robot in a challenge task. The robot is controlled by Deep Reinforcement Learning trained agents, with observations based on the robot’s dynamics sensoring and grayscaled images acquired by a frontal camera. The performances and computational costs of agents implementing convolutional neural networks for the visual observations processing are compared to those from agents that receive encoded versions of these images. Even though the results shows significant reduction in the success rates for the agents receiving encoded images, the computational costs of training them are greatly reduced, offering a viable alternative for hardware limitations.

关键词： Training Dimensionality reduction visualization Simulation Robot vision systems Process control Deep reinforcement learning Hardware Computational efficiency image reconstruction

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