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AI-based Detection of Pest Infected Crop and Leaf 3

AI-based Detection of Pest Infected Crop and Leaf

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3rd International conference on Signal processing and Communication (ICPSC)

作者： Ahmed, Mustafa Mahajan, Tushar Sharma, Bhupender Datt Kumar, Mahendra Singh, Sandeep Kumar Natl Inst Technol Dept Elect & Commun Hamirpur India

ISBN: (纸本)9781665428644

Plant infection/disease is one of the ongoing challenges for farmers, which imposes a threat on their income and food security. Detecting infection in plants or crops is an onerous task because the analysis of each crop in large fields takes too much time, effort, work force and expertise. In this paper, we have proposed AI-based detection of pest infected crop and leaf. The proposed method is helpful in analyzing crops in a time-efficient manner and gives more accurate results. The classification of the crops is done on the basis of their images. A publicly available dataset is used to analyze the proposed methodology. image processing methods are used in order to analyze the crops, further convolutional neural networks are applied to differentiate the healthy crops from the ones that are infected from some disease and also show some visual remarks.

关键词： image processing CNN plant disease detection crop

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A Guided Upsampling Network for Short wave Infrared images Using Graph Regularization

A Guided Upsampling Network for Short wave Infrared Images U...

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

作者： Frank Sippel Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Exploiting the infrared area of the spectrum for classification problems is getting increasingly popular, because many materials have characteristic absorption bands in this area. However, sensors in the short wave infrared (SWIR) area and even higher wavelengths have a very low spatial resolution in comparison to classical cameras that operate in the visible wavelength area. Thus, in this paper an upsampling method for SWIR images guided by a visible image is presented. For that, the proposed guided upsampling network (GUNet) uses a graph-regularized optimization problem based on learned affinities is presented. The evaluation is based on a novel synthetic near-field visible-SWIR stereo database. Different guided upsampling methods are evaluated, which shows an improvement of nearly 1 dB on this database for the proposed upsampling method in comparison to the second best guided upsampling network. Furthermore, a visual example of an upsampled SWIR image of a real-world scene is depicted for showing real-world applicability.

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression

Fundus Image Classification for Glaucoma using U-Net Archite...

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International conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing (PCEMS)

作者： Vanshika Bajaj Deepali M. Kotambkar Shelke Dept. of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas.

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Experimenting Encoder-Decoder Architecture for visual image Captioning 8th

Experimenting Encoder-Decoder Architecture for Visual Image ...

引用

8th International conference on Cognition and Recognition, ICCR 2021

作者： Asif, Hasan State University of New York Buffalo Buffalo United States

ISBN: (纸本)9783031224041

image captioning is an essential task in artificial intelligence that predicts the description of a given input image. In recent years, both computer vision and natural language processing witnessed huge advancement making it capable to extract high-level semantic information and process it to structure new sentences based on that. A myriad of research has been done discussing the use of deep learning in image caption tasks. This paper explores the various combination of CNN and RNN modules in Encoder-decoder architecture to find the best image caption generator. The different models were compared using BLEU and CIDEr metrics. The proposed model with fine-tuned parameters showed a BLEU as high as 67.2, 59.8, 53, 44.7, and CIDEr score was 46 with Restnet50v2 model as an encoder and GRU as a decoder. After a little model hyperparameter tuning on Batch size and learning rate, an improvement of 15% and 12.5% was achieved in CIDEr and BLEU-4score. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Enhanced Satellite image Fusion Using Deep Learning and Feature Extraction Techniques: A Survey 1st

Enhanced Satellite Image Fusion Using Deep Learning and Feat...

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1st International conference on Intelligent Systems in Computing and communications, ISCComm 2023

作者： Nallagachu, Swathi Sandanalakshmi, R. Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India

ISBN: (纸本)9783031756047

This paper presents an overview and analysis of numerous research projects on image fusion methods, with a particular emphasis on deep learning-based methods. The research analyses the inadequacies of current fusion models and suggests novel methods for feature extraction, visual sensor networks, remote sensing applications, medical imaging, and multi-resolution image fusion. The study demonstrates the advantages of deep learning techniques for image fusion tasks, including Convolutional Neural Networks (CNNs), Stacked Auto encoders, and Convolutional Sparse Representation (CSR). These methods provide better fusion quality, fast processing, and better visual perception. Numerous studies provide novel methods, such as the rapid Integer Lifting Wavelet Transform (ILWT), Dual-Tree Complex Contourlet Transform (DT-CCT), and hybrid fusion methods that combine Discrete Cosine Transform (DCT) and Integer Lifting Wavelet Transform (ILWT) techniques. The review literature survey shows how these techniques can improve fusion outcomes while introducing little information distortion. In general, this paper brings together improvements in feature extraction and image fusion techniques, demonstrating the strength and potential of deep learning-based approaches. It is a useful tool for academics and professionals working in various fields who want to comprehend and advance these fields of study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image fusion

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Analysis of Relationship between Point Cloud Just Noticeable Difference and Attribute Quantization Parameters

Analysis of Relationship between Point Cloud Just Noticeable...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhang Chen Luqian Bai Mengting Yu Shuai Wan Hejie Yang School of Electronics and Information Northwestern Polytechnical University Xi’an China School of Telecommunication Engineering Xidian University Xi’an China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Utilizing just noticeable difference(JND) thresholds to describe distortion visibility enables efficient transmission and storage of point clouds while maintaining high quality. Therefore, describing the relationship between point cloud JND and attribute quantization parameters (QP) is essential. This relationship is ,however, not clear regarding point cloud compression so far. We investigate the correlation between JND and attribute QP using Geometry-based Point Cloud Compression(G-PCC), which is the latest standard for point cloud compression. Moreover, we provide the corresponding datasets in this work. Using the K-means clustering algorithm, we partition the collected raw data into intervals and identify the peaks closest to the means in each sub-interval as the desired JND points. For human-content point clouds, although the number of JND varies, the QP values corresponding to different levels of JND show a regular distribution. The study focuses on the QP corresponding to the starting JND point(JND S ) and the ending JND point (JND E ).The findings indicate that the critical points for human-content point clouds are relatively consistent, with JND S mainly concentrated at QP = 27 and JND E at QP = 45. Conversely, perception in object-content point clouds is more influenced by their content, and the distribution of JND is not as uniform as in human-content point clouds. The experimental datasets and research results are accessible at the following link: https://***/ZhangChen2022/JND-attribute-QP-on-G-PCC.

关键词： Point cloud compression Quantization (signal) Correlation visual communication image processing Clustering algorithms Distortion Partitioning algorithms Standards

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Acoustic and visual Information Fusion of Unmanned Surface Vehicles

Acoustic and Visual Information Fusion of Unmanned Surface V...

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IEEE International conference on Signal processing, communications and Computing (ICSPCC)

作者： Ruoxi Hou Xingyue Zhou Wutao Yin School of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi China Key Laboratory of Ocean Acoustics Ocean Institute Northwestern Polytechnical University Suzhou Jiangsu China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

In many scenarios, sensors installed in USV scan only detect either the above-water or underwater part of ob-stacles, which leads to errors in obstacle avoidance. In this paper, the obstacle information detected by the sonar and visual camera is used to perform cross-domain data fusion. In low-speed operation mode, the prior binding information (surface and underwater) such as coordinate and direction will be used to match the collected image information (surface and underwater) until the desired matching level is achieved, directly determining the target orientation. When the coordinate matching level falls below the threshold or when the target ID information is missing, data processing module including cluster analysis, confidence filtering, and information entropy assessment will be employed to allocate weights to heterogeneous information, such as the distance and direction of the obstacle. In the fusion model, the Monte Carlo algorithm is applied to calculate the nonsynchronous error between the obstacles detected by the sonar and the visual sensor. Corresponding angle calculations are performed, and the information such as the distance and direction is converted into latitude and longitude coordinates. Compared to the traditional approach of simply merging surface or underwater obstacle information, we have integrated the information of the same obstacle from both above and below the water surface and enhance the reliability and accuracy of decision-making for unmanned surface vehicles.

关键词： visualization Accuracy Monte Carlo methods Signal processing algorithms Clustering algorithms Sensor fusion Sensors Surface treatment Autonomous vehicles Sonar detection

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MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible image Fusion

MAITFuse:Multi-Dimension Adaptive Interaction Transform Netw...

引用

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

作者： Yabin Sun Wentai Lei Ziyi Zhang Jiongchang Liu Chenxu Li Tao Zhang Artificial Intelligence Jiangnan University Wu Xi China Communications Engineering Central South University Chang Sha China School of Electronic Information Central South University Chang Sha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.

关键词： Adaptive systems Transforms Signal processing Feature extraction Transformers Acoustics Data mining Speech processing image fusion visual perception

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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Vision Transformer Hashing for image Retrieval

Vision Transformer Hashing for Image Retrieval

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Dubey, Shiv Ram Singh, Satish Kumar Chu, Wei-Ta Indian Institute of Information Technology Computer Vision and Biometrics Lab Allahabad India National Cheng Kung University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, Transformer has emerged as a new architecture in deep learning by utilizing self-attention without convolution. Transformer is also extended to Vision Transformer (ViT) for the visual recognition with a promising performance on imageNet. In this paper, we propose a Vision Transformer Hashing (VTS) for image retrieval. We utilize the pre-trained ViT on imageNet as the backbone network and add the hashing head. The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks with their objective functions. We perform the extensive experiments on CIFAR10, imageNet, NUS-Wide, and COCO datasets. The proposed VTS based image retrieval outperforms the recent state-of-the-art hashing techniques with a significant margin. We also find the proposed VTS model as the backbone network is better than the existing networks, such as AlexNet and ResNet. The code is released at https://***/shivram1987/VisionTransformerHashing. © 2022 IEEE.

关键词： image retrieval

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