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Analysis the Bacterial Effect of Leaf Using Colour image processing

Analysis the Bacterial Effect of Leaf Using Colour Image Pro...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Nivethitha, T. Sanjay, P. Sudhir Siddharth, E. Shruthika, S. Sathana, S. Sri Eshwar College of Engineering Department of Ece Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

The bacterial effect of leaf disease are being employed to address the significant crop losses caused by various rice leaf diseases. The proposed solution provides real-time disease diagnosis, giving farmers a cost-effective and efficient tool for monitoring crop health. It will reduce the use of harmful pesticides by detecting disease earlier and improve rice yield and quality. Recent studies highlight that DCNNs are among the most effective tools for detecting and categorizing these diseases. By training the model on datasets containing leaf images, it extracts critical features to distinguish between healthy and infected leaves. This architecture ensures high detection accuracy under diverse environmental conditions and across multiple disease types. The proposed approach offers real-time diagnostics, giving farmers an affordable and practical method to monitor crop health efficiently. Early detection also helps reduce pesticide usage, resulting in better crop quality and increased yields. Additionally, the model's adaptability is enhanced with a feedback loop that integrates new data, allowing continuous improvement and expanding its ability to detect different diseases. With its high accuracy, this DCNN-based system provides a reliable solution for precision agriculture and promotes sustainable farming practices. © 2025 ieee.

关键词： Photointerpretation

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Imperceptible Transfer Attack on Large Vision-Language Models

Imperceptible Transfer Attack on Large Vision-Language Model...

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

作者： Xiaowen Cai Daizong Liu Runwei Guan Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In spite of achieving significant progress in recent years, Large Vision-Language Models (LVLMs) are proven to be vulnerable to adversarial examples. Therefore, there is an urgent need for an effective adversarial attack to identify the deficiencies of LVLMs in security-sensitive applications. However, existing LVLM attackers generally optimize adversarial samples against a specific textual prompt with a certain LVLM model, tending to overfit the target prompt/network and hardly remain malicious once they are transferred to attack a different prompt/model. To this end, in this paper, we propose a novel Imperceptible Transfer Attack (ITA) against LVLMs to generate prompt/model-agnostic adversarial samples to enhance such adversarial transferability while further improving the imperceptibility. Specifically, we learn to apply appropriate visual transformations on image inputs to create diverse input patterns by selecting the optimal combination of operations from a pool of candidates, consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as an adversarial learning problem and employ a gradient approximation strategy with noise budget constraints to effectively generate imperceptible transferable samples. Extensive experiments on three LVLM models and two widely used datasets with three tasks demonstrate the superior performance of our ITA.

关键词： visualization Noise Adversarial machine learning Acoustics Speech processing

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2017 ieee visual communications and image processing, VCIP 2017

2017 IEEE Visual Communications and Image Processing, VCIP 2...

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2017 ieee visual communications and image processing, VCIP 2017

ISBN: (纸本)9781538604625

The proceedings contain 142 papers. The topics discussed include: perceptual optimized adaptive HTTP streaming;TC-CCS: a cooperative caching strategy in mobile transparent computing system;non-invasive imaging based on speckle pattern estimation and deconvolution;object localization in weakly labeled data using regularized attention networks;dictionary learning based high frequency inter-layer prediction for scalable HEVC;CNN oriented fast QTBT partition algorithm for JVET intra coding;endoscopic video deblurring via synthesis;improve object detection via a multi-feature and multi-task CNN model;enhancing pulmonary nodule detection via cross-modal alignment;adaptive difference modelling for background subtraction;geometry-adaptive motion partitioning using improved temporal prediction;cascaded convolutional neural networks for object detection;a new underwater image enhancing method via color correction and illumination adjustment;a novel image compression framework at edges;QoE-based node selection strategy for edge computing enabled internet-of-vehicles (EC-IoV);deep probabilities for age estimation;and deep discriminative network with inception module for person re-identification.

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High-Resolution 3D Reconstruction Based on Prior Knowledge with Binocular Structured Light 8th

High-Resolution 3D Reconstruction Based on Prior Knowledge w...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Luo, Desen Zhou, Wenju Liang, Wei Jin, Maoyu Ren, Xinzhen Chen, Rongfei Han, Xiaofei Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9789819602933

The 3D reconstruction process using binocular structured light goes through two streak image processing: wrapped phase solving and phase unfolding. At least 3 images are required to solve the parcel phase using the phase shift method, and acquiring more than one image will affect the efficiency of the reconstruction. In this paper, a neural network model is designed to solve for the wrapped phase using a single high-resolution phase shift image. Firstly, highly accurate wrapped phase results are solved using 12-step phase images and allowed to be used as theoretical reference values for model training. Secondly, using the optimized U-Net end-to-end generation framework, a single phase-shifted image is input, and a two-channel floating-point image of the calculated wrapped phase numerator and the denominator is output. Thirdly, the output two-channel floating-point image is processed with the arctan function to obtain the wrapped phase of the inference output. In the inference phase, the wrapped phase can be solved using a single image using the a priori knowledge learned from the model. The efficiency of structured light 3D reconstruction can be improved by utilizing the high-resolution wrapped phase generation framework proposed in this paper. Compared with the 12-step PS method, the overall reconstruction time is reduced by 87.85%, and the reconstruction accuracy is close to that of the 12-step PS method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image reconstruction

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Improving the Construction of a Multi-modal Underwater image Recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas 8th

Improving the Construction of a Multi-modal Underwater Image...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Huang, Yu Wang, Kai Gu, Yuntao Zhou, Chaofan Guo, Yuanjun Yang, Zhile China Nuclear Industry Maintenance Co. Ltd. Shenzhen Branch Shenzhen China Taiyuan University of Technology Taiyuan030024 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9789819602315

The cooling facilities of nuclear power plants are crucial components for transferring the accumulated heat from the reactor to the external environment. However, the complex structure of these facilities makes traditional manual anomaly detection methods inefficient, time-consuming, and poses certain risks to human safety. Therefore, this paper proposes a system for anomaly detection in the cooling source areas of nuclear power plants based on improved multi-modal underwater image recognition. The system utilizes deep learning and image processing techniques to denoise, compensate for illumination, and correct water flow in collected images of the cooling source areas. Wavelet-based noise removal techniques are applied to further enhance image clarity, providing accurate inputs for machine vision algorithms. By training an improved convolutional neural network model, real-time automatic identification and classification of underwater anomalies such as foreign objects and pipeline cracks are achieved, significantly improving the monitoring efficiency and safety of the cooling source areas in nuclear power plants. Comparative analysis with actual cases validates the excellent stability and accuracy of the proposed method in handling underwater operation images in the cooling source areas of nuclear power plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nuclear power plants

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Learning Joint Appearance and Shape Co-Representations for Co-Saliency Detection

Learning Joint Appearance and Shape Co-Representations for C...

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

作者： Guanting Guo Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading Co-saliency Detection (CoD) framework aims to segment the co-salient objects by learning the consensus visual representation of the foreground objects. However, despite different categories, some distractors may have similar appearance to the co-salient objects, such as Apples vs. Bananas have similar color and textures. This makes it challenging to distinguish the distractors only through learning the co-salient object appearance representations. To address this issue, we propose a joint appearance and shape co-representation learner for CoD, dubbed as ASCoD. The ASCoD is composed of a Co-Appearance learning Module (CoAM) and a Co-Shape learning Module (CoSM). The CoAM first learns a co-salient object appearance embedding that encodes the global cross-image and spatial context information. Then, this embedding is set as a co-appearance prototype, which guides the model to enhance the features to highlight the co-salient object regions. Afterwards, we design the CoSM that is a cross-attention module, among which the key and the value encode the shape information from a set of salient tokens dynamically selected by a Co-Shape Prototype generation Module (CSPM). Finally, through jointly optimizing the cascaded CoAM and CoSM, the optimal appearance and shape co-representations are achieved, marrying the merits of both appearance and shape co-representations that are not only robust to co-salient objects appearance variations, but also can well discriminate the co-salient objects from the distractors with similar appearance. Extensive evaluations on three challenging benchmarks including CoCA, CoSOD3k and CoSal2015, demonstrate superiority of the ASCoD to a variety of state-of-the-art CoD methods.

关键词： visualization Shape Prototypes Color Signal processing Benchmark testing Robustness Acoustics Speech processing

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Modular Prompt Learning Improves Vision-Language Models

Modular Prompt Learning Improves Vision-Language Models

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

作者： Zhenhan Huang Tejaswini Pedapati Pin-Yu Chen Jianxi Gao Rensselaer Polytechnic Institute Troy USA IBM Research Yorktown Heights USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encoders, elicits the potentials of pre-trained models and readily adapts them to new scenarios. Compared to fine-tuning, prompt learning enables the model to achieve comparable or better performance using fewer trainable parameters. Besides, prompt learning freezes the pre-trained model and avoids the catastrophic forgetting issue in the fine-tuning. Continuous prompts inserted into the input of every transformer layer (i.e. deep prompts) can improve the performances of pre-trained models on downstream tasks. For i-th transformer layer, the inserted prompts replace previously inserted prompts in the (i − 1)-th layer. Although the self-attention mechanism contextualizes newly inserted prompts for the current layer and embeddings from the previous layer’s output, removing all inserted prompts from the previous layer inevitably loses information contained in the continuous prompts. In this work, we propose Modular Prompt Learning (MPL) that is designed to promote the preservation of information contained in the inserted prompts. We evaluate the proposed method on base-to-new generalization and cross-dataset tasks. On average of 11 datasets, our method achieves 0.7% performance gain on the base-to-new generalization task compared to the state-of-the-art method. The largest improvement on the individual dataset is 10.7% (EuroSAT dataset). Our code is available at https://***/Zhenhan-Huang/Modular-Prompt-Learning.

关键词： Multiprotocol label switching Adaptation models visualization Codes Semantics Signal processing Performance gain Transformers Acoustics Speech processing

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Clarity Amidst Blur: A Deterministic Method for Synthetic Generation of Water Droplets on Camera Lenses

Clarity Amidst Blur: A Deterministic Method for Synthetic Ge...

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ieee Workshop on Applications of Computer Vision (WACV)

作者： Tim Dieter Eberhardt Tim Brühl Robin Schwager Tin Stribor Sohn Wilhelm Stork Dept. of Highly Automated Driving Systems - Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute for Information Processing Technologies (ITIV) - KIT Karlsruhe Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In computer vision, image clarity is crucial, particularly when challenges like water droplets on camera lenses can significantly impair the accurate analysis of visual data. While existing methods mainly focus on small droplets, the impact of larger droplets has been largely overlooked. This paper introduces a novel approach that models water droplets using randomly generated points and Bézier curves to simulate their shape on the lens. Based on this geometric framework, we developed a classifier to distinguish between two visual scenarios within the droplet. For larger droplets, we use a heuristic method to simulate various lens block-ages. We evaluate this simulation framework using a real stereo dataset from [19] with clear and soiled images. Our method, evaluated using Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM), demonstrates superior performance in MSE, while also achieving com-petitive results in SSIM compared to existing techniques for generating realistic water droplets. Additionally, we applied this technique for data augmentation in object detection tasks using the YOLOv7 [25] model. The results show improved robustness, especially in challenging conditions where large droplets obstruct the lens.

关键词： visualization Computer vision Computational modeling Surveillance Cameras Data augmentation Data models Water pollution Standards Lenses

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Stereo-Correct Digital Magnification of the Physical World Using Video-Pass-Through Augmented Reality

Stereo-Correct Digital Magnification of the Physical World U...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Jerald Thomas Mohammed Safayet Arefin Doug Bowman University of Wisconsin - Milwaukee Colorado State University Virginia Tech

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Video-pass-through (VPT) augmented reality (AR) enables greater field of view and fidelity compared to current optical-see-through AR technologies. However, the computational complexity of VPT AR has traditionally resulted in high latency, low resolution, and/or visual artifacts. Only recently have devices come on the market that successfully mitigate these drawbacks, such as the Varjo XR-3 and XR-4, the Apple Vision Pro, and the Meta Quest 3. We propose that low-latency VPT AR introduces several interesting 3D User Interface (3DUI) capabilities. In this position paper, we discuss one of these 3DUIs, namely stereo-correct digital magnification of the physical environment. Specifically, this paper presents a novel system for stereo-correct digital magnification of the physical environment using the Varjo XR-3 (but that can be generalized to other VPT AR systems). The digital magnification algorithm we propose enables stereoscopic magnification of physical objects in real time by utilizing the front-facing camera feeds of the VPT AR system, implemented entirely through software. In addition, we outline the challenges and limitations associated with the proposed system, discuss our current progress, and suggest potential future directions for the development of our proposed VPT AR digital magnification system.

关键词： Performance evaluation visualization Three-dimensional displays Stereo image processing Software algorithms User interfaces Software Real-time systems Usability Augmented reality

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