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Automated Detection of Poultry Diseases using Implicit pattern Mining over Explicit image Analysis

Automated Detection of Poultry Diseases using Implicit Patte...

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2024 IEEE international conference.on Signal processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Donthi, Khushi Kiran, Kuval Srinivasa, Gowri PES University PES Center for Pattern Recognition Dept. of CSE Bangalore India

ISBN: (纸本)9798350376135

Early detection of poultry diseases is crucial for poultry farmers, but traditional identification methods are often slow and tedious due to limited facilities. We present a comprehensive evaluation of implicit pattern mining techniques using lightweight, CNN-based deep learning models such as, MobileNetV2, InceptionV3, and EfficientNetB5 that are amenable for implementation on edge devices. We contrast them with traditional machine learning approaches applied to analyse chicken fecal image data to classify images of healthy samples from unhealthy ones. We use model interpretation to gain insight to the patterns in the input image that the deep learning models use to make predictions. This facilitates corroborating domain understanding with model performance. This study demonstrates the viability of deploying deep learning based methods for poultry disease detection on handheld devices, highlighting its potential for deployment in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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CCFusion: An infrared and visible image fusion method based on cycle consistency and covariance compression disentanglement module

CCFusion: An infrared and visible image fusion method based ...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Song, Wenfeng Luo, Xiaoqing Zhang, Zhancheng School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China College of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9781510680425

Thanks to the feature disentanglement capability of disentangled representation methods, deep learning-based deentanglement methods have achieved strong performance in image fusion tasks. However, it should be noted that training a powerful performance of a disentangled representation method is a challenging problem due to the domain differences between image fusion tasks and mainstream disentanglement tasks. To overcome this problem, an infrared and visible image fusion method based on cycle consistency and the covariance compression disentanglement module proposed in this manuscript. Firstly, the proposed covariance compression disentanglement module statistically counts the global information by global covariance pool and adaptively enhances the desired features and suppresses others by the covariance compression module. Secondly, it makes the disentanglement module cycle-consistent by disentanglement the fusion image, and strengthens the disentanglement by the proposed consistency loss function and feature similarity loss function constraints. Specifically, in this paper, multi-source images are input into the disentanglement module, modal features and common features are disentangled, and fusion results are generated by fusing the modal features and common features of multi-source images. After generating the fusion result, the fusion result is input into the disentanglement module again, and the re-decomposed features are used to calculate the consistency loss with the multi-source image features. On the publicly available TNO dataset, subjective and objective evaluations show that the method proposed in this manuscript has better fusion performance than other state-of-the-art methods. © 2024 SPIE.

关键词： image fusion

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An overview of digital image analog noise removal based on traditional filtering

An overview of digital image analog noise removal based on t...

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Ma, Yiqun Zhang, Tianshuo Lv, Xin School of advanced technlolgy Jiaotong-liverpool University Suzhou China School of Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9781510666351

Due to interference in the transmission of external equipment, images can suffer from varying noise concentrations. As an immediate and practical step to reduce the impact of noise on an image and to improve its quality and visual presentation, filtering is the best method. The paper examines how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference. Also included in this paper is an analysis of the advantages and challenges of traditional noise filtering, as well as suggestions for a filtering method for a variety of noise levels. Lastly, the effects of filtered noise pictures are evaluated through extensive experimental simulations, providing substantial assistance for related research. © 2023 SPIE.

关键词： Salt and pepper noise

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Stable tracking for low contrast extended target based on edge feature point matching

Stable tracking for low contrast extended target based on ed...

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zheng, Mengting Qi, Xiaoping Zhang, Jianlin Wei, Yuxing Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510666351

To solve the tracking drift caused by rotation, uneven illumination, and beyond the field of view in the moving process of the target with low contrast extension, a method of edge feature points matching is proposed in this paper. Specifically, feature points are extracted from image edges to improve the stability of feature points matching. In addition, combined Non-Local algorithm and improved Contrast Limited Adaptive Histogram Equalization algorithms are used to enhance the image. Experimental results show that this method has satisfactory performance, has good anti-rotation characteristics, can be stably tracked when the target reappears in the field of view and basically meets the real-time performance. © 2023 SPIE.

关键词： image enhancement

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CAM: Consistency adversarial model for image generation with high-frequency image details

CAM: Consistency adversarial model for image generation with...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Qin, Zihan Sun, Mingzhen Liu, Jing Institute of Automation Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510680425

As an extension of diffusion models, consistency models reduce the necessary sampling steps to a single iteration when synthesizing an image sample, thereby significantly enhances the efficiency of image generation. In addition, they also allow multi-step generation, providing flexibility for trade-offs between sample quality and computational efficiency. Despite these advantages, traditional consistency models are troubled by loss of high-frequency image details. This issue is attributed to the inherent regression-to-mean property of the L2 training loss, impeding overall improvement of model performance on the image generation task. In this paper, we propose a novel consistency adversarial model to address the loss of high-frequency image details through adversarial generation. In particular, we train a consistency model in an adversarial manner by treating it as an image generator. Then, an additional image discriminator is introduced and optimized along with the consistency model in an adversarial manner. The target of the image discriminator is to punish the image generator when it synthesizes images lacking high-frequency image details. In this way, image samples with high-frequency image details can be obtained and the performance of consistency models can be improved. Extensive experiments demonstrate the effectiveness of our proposed method. Our CAM outperforms the traditional consistency model on two challenging benchmarks: imageNet and LSUN. © 2024 SPIE.

关键词： Diffusion Education and training Content addressable memory Statistical modeling Performance modeling Adversarial training Curium image quality Data modeling image enhancement

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2024 international conference.on advances in Computing Research on Science Engineering and Technology, ACROSET 2024

2024 International Conference on Advances in Computing Resea...

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2024 international conference.on advances in Computing Research on Science Engineering and Technology, ACROSET 2024

ISBN: (纸本)9798350388800

The proceedings contain 197 papers. The topics discussed include: analyzing the human element in cybersecurity breaches with a focus on social engineering tactics and the risks posed by insider threats;design and development of real-time image based fire detection using OpenCV and HSV;the role of low-dimensional neural networks in solving complex inverse problems;transformative potential IoT sensor frameworks for real-time Alzheimer's detection and monitoring in elderly populations;decoding the Devanagari - handwritten Hindi recognition using deep learning methodology;countering the rise of AI-generated content with innovative detection strategies and large language models;a method to reduce the incidence of wheat leaf diseases;and an extensive analysis of pattern identification and categorization.

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Analysis of spectral characteristics of typical construction waste

Analysis of spectral characteristics of typical construction...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zhang, Jiayuan College of Geomatics and Geoinformation Guilin University of Technology Guilin China

ISBN: (纸本)9781510680425

With the acceleration of urban construction and renovation, the volume of construction waste has significantly increased, accounting for 30% to 40% of the total urban solid waste emissions. Therefore, analyzing the spectral characteristics of typical construction waste is crucial for remote sensing identification and monitoring, providing essential ground verification information. This study conducted spectral data collection of construction waste in areas such as Daxing District of Beijing and Pingdingshan City, Henan. Various spectral analysis methods were applied to examine its characteristics. The findings revealed that: (1) the wavelength range is between 1913nm and 1932nm, the spectral curves of all kinds of construction wastes have obvious reflection troughs, In the range of 1660-1690nm, organic building materials exhibit absorption valleys. (2) In the wavelength intervals of 600~1100nm and 1860~1930nm, spectral curves exhibit consistent trends, rendering them unsuitable for discrimination. (3) In the range of 1900~2200nm, samples exhibit reduced susceptibility to the spectral characteristics of the dust-proof net, closely mirroring the spectral features of uncovered samples, thereby rendering it suitable as a basis for classification. © 2024 SPIE.

关键词： Reflectivity Absorption Reflection Remote sensing Visible radiation Data acquisition Smoothing Spectrometers image classification Solids

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HSDFormer: an improved transformer for remote sensing image semantic segmentation

HSDFormer: an improved transformer for remote sensing image ...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Bai, Yang Gao, Suixiang University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Zhongguancun Laboratory Beijing100049 China

ISBN: (纸本)9781510680425

The extraction of building locations is crucial in the field of remote sensing, commonly applied in tasks such as emergency response, urban planning, and environmental monitoring. Existing methods often employ convolutional models such as ResNet and U-Net. however, these models struggle to capture long-range features of geographical objects, limiting their practical effectiveness. Meanwhile, Transformer based models face challenges due to their quadratic computational complexity. Additionally, the characteristics of remote sensing images, including dense arrangement, small scale, and class imbalance, pose difficulties for traditional patch-merging modules in downsampling, leading to the loss of significant building information. To address such limitations, we present the HSDFormer model in this paper. The model achieves a reduction in the computational complexity of self-attention calculation through a straightforward sequential reduction process. Additionally, our model has optimized the downsampling process specifically for remote sensing semantic segmentation tasks, effectively minimizing the loss of crucial information during layer-wise feature extraction. We evaluate the HSDFormer on the WHU Building Dataset and Satellite Dataset II (East Asia). The results demonstrate the model's effectiveness in accurately segmenting building locations, surpassing performance compared to other baseline models. © 2024 SPIE.

关键词： Semantic Segmentation

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Research on maize disease detection based on attention mechanism

Research on maize disease detection based on attention mecha...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Liang, Xinwei Qi, Long Ma, Ruijun College of Engineering South China Agricultural University Guangdong 510642 China

ISBN: (纸本)9781510680425

Maize disease detection refers to the detection of maize growth images, and ultimately the detection of diseases and their sites carried by maize. At the early and middle stages of maize diseases, they are often characterized by inconspicuous pathological features and small areas of pathological sites, which constitute a great challenge for accurate maize disease detection. The attention detection mechanism determines the feature focus of the target detection model on the detection target, and the feature extraction ability of the model can be improved by feature focusing on the target, thus solving the problem of inconspicuous pathological features and small area of pathological sites to a certain extent. CBAM is a module based on the channel attention mechanism and the spatial attention mechanism, which can be effectively enhanced compared with other attention mechanisms. Therefore, in this paper, CBAM is used as an attention mechanism for maize disease detection, which focuses on three factors such as pathology color, pathology area and pathology shape of maize diseases. In this paper, this mechanism is integrated into YOLOv5, and validated on the publicly available maize pathology detection dataset, and its detection accuracy reaches 92.8%, and mAP0.5 is improved by 4% compared with the original YOLOv5 detection algorithm, which achieves good experimental results. © 2024 SPIE.

关键词： Diseases and disorders Feature extraction Object detection Data modeling Machine learning Pathology Target detection image processing Matrices Neurological disorders

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6th international conference.on Recent Trends in image processing and pattern recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th international conference.on Recent Trends in image processing and pattern recognition, RTIP2R 2023

ISBN: (纸本)9783031530845

The proceedings contain 31 papers. The special focus in this conference.is on Recent Trends in image processing and pattern recognition. The topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and Their Countermeasures;Enhancing CT image Visualization and Analysis Through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient Data Integrity: Decentralised Storage and Retrieval in Modern Healthcare Systems;a Residual Learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and Their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and Thermography Based image processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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