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Advanced remote sensing techniques for underwater Fin whale detection in the indian Ocean

Advanced remote sensing techniques for underwater Fin whale ...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Vasavi Sanikommu Akshaya Sura Pranavi Chimirala Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

the accurate detection and monitoring of whales in the indian Ocean are vital for marine conservation but face significant challenges due to the complexities of underwater environments. Advanced remote sensing technologies offer potential solutions to overcome these limitations. this study integrates hyperspectral imaging, satellite imagery, and thermal imaging to develop a comprehensive whale detection system. Hyperspectral imaging captures the spectral reflectance of whale skin, enabling detection under varying water conditions. Satellite imagery aids in identifying subsurface features, while thermal imaging detects whales' thermal signatures. Machine learning algorithms process the combined data to enhance detection accuracy and reliability. the integrated system demonstrated effective detection of whales at various depths and environmental conditions in the indian Ocean. Hyperspectral imaging proved sensitive to water turbidity and target reflectance, while the coastal blue band successfully identified subsurface whale features. thermal imaging further enhanced detection capabilities in low-light conditions. the combined use of these technologies outperformed traditional methods, offering an accuracy of 0.96 and reliability for real-time whale monitoring.

关键词： Reflectivity Accuracy indian Ocean Whales Imaging Satellite images Reliability Remote sensing Monitoring Hyperspectral imaging

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Development of a multi-modality based approach for Plastic Waste Segregation

Development of a multi-modality based approach for Plastic W...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Wejdene Smari Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Plastic waste is becoming nowadays a huge environmental problem. Circular economy seems to be the best choice for preserving the environment, by reinserting plastic waste into the industrial process and use. A key step for that is the plastic waste segregation from other waste. this paper proposes a new plastic waste segregation approach based on two modalities: spectroscopy and optical images. the research handles an optical camera for waste image acquisition, while the ground truth for plastic type identification, with respect to the seven primary plastic types: PET, HDPE, LDPE, PP, PS, PVC, and Other, is established through spectroscopic analysis. Various deep learning models were trained and evaluated on a dataset of publicly available and custom-collected images, aiming to achieve human-level accuracy in distinguishing between plastic types. the proposed methodology is based on a set of steps, where the model is first built implicitly on a segregation of different wastes, and then applied via transfer learning for separating the seven plastic types. the system performances demonstrate the feasibility and effectiveness of this combined approach for accurate and efficient plastic waste sorting. the proposed system has been implemented on Raspberry Pi 4 Model B+ as the central processing unit, integrating computer vision and deep learning techniques for efficient and accurate sorting.

关键词： Deep learning Waste management Visualization computer vision Accuracy Embedded systems Biological system modeling Optical imaging Plastic waste Sorting

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Segment Anything for Dendrites from Electron Microscopy

Segment Anything for Dendrites from Electron Microscopy

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International image processing, Applications and Systems conference (IPAS)

作者： Zewen Zhuo Ilya Belevich Ville Leinonen Eija Jokitalo Tarja Malm Alejandra Sierra Jussi Tohka A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland Electron Microscopy Unit Institute of Biotechnology University of Helsinki Helsinki Finland Department of Medicine Faculty of Health Sciences University of Eastern Finland Kuopio Finland

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Segmentation of cellular structures in electron microscopy (EM) images is fundamental to analyzing the morphology of neurons and glial cells in the healthy and diseased brain tissue. Current neuronal segmentation applications are based on convolutional neural networks (CNNs) and do not effectively capture global relationships within images. Here, we present DendriteSAM, a vision foundation model based on Segment Anything, for interactive and automatic segmentation of dendrites in EM images. the model is trained on high-resolution EM data from healthy rat hippocampus and is tested on diseased rat and human data. Our evaluation results demonstrate better mask quality compared to the original and other fine-tuned models, leveraging the features learned during training. this study introduces the first implementation of vision foundation models in dendrite segmentation, paving the path for computer-assisted diagnosis of neuronal anomalies.

关键词： Training image segmentation Foundation models Morphology Object segmentation Reproducibility of results Dendrites (neurons) Electron microscopy Electrons Diseases

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Revolutionary MRI Imaging for Alzheimer's: Cutting-Edge GANs and vision Transformer Solutions

Revolutionary MRI Imaging for Alzheimer's: Cutting-Edge GANs...

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International image processing, Applications and Systems conference (IPAS)

作者： Houmem Slimi Imen Cherif Sabeur Abid Mounir Sayadi Dept. Electrical Engineering University Of Tunis Tunis Tunisia Dept. Electronics University Of Tunis Tunis Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

this study introduces a new approach to diagnosing Alzheimer’s disease by combining Generative Adversarial Networks (GANs) with vision Transformers (ViTs) to tackle the common issue of limited medical imaging data. GANs are used to generate synthetic MRI images of Alzheimer’s patients, which are combined with real images to create a richer and more varied dataset. In this approach, the GAN model— comprising a generator and discriminator—learns to produce high-quality synthetic MRI images that, together with real data, significantly expand the training set. these images are then fed into a ViT model, which, thanks to its self-attention mechanisms, excels at identifying and classifying the stages of Alzheimer’s disease. Our evaluation shows impressive results, with metrics including accuracy, F1-score, AUC, precision, and recall reaching 98.8%, 98.43%, 99.5%, 98%, and 98%, respectively— showing the effectiveness of the expanded dataset in boosting classification performance. this integrated GAN-ViT approach not only enhances diagnostic accuracy for Alzheimer’s but also sets the stage for applying similar techniques across other medical imaging domains, providing better tools for understanding disease progression and improving patient outcomes.

关键词： Training computer vision Accuracy Magnetic resonance imaging Generative adversarial networks Transformers Data augmentation Computational efficiency Alzheimer's disease Biomedical imaging

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Integrating Object Detection in Bird-View Systems for Enhancing Safety in Construction Machinery

Integrating Object Detection in Bird-View Systems for Enhanc...

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International image processing, Applications and Systems conference (IPAS)

作者： Julian Hund Eric Schöneberg Daniel Görges Hasan Hashib Electrical and Computer Engineering Institute of Electromobility Kaiserslautern Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Working with construction machinery necessitates that workers remain attentive to effectively survey their surroundings. the complex environment, blind spots, and limited visibility while simultaneously tackling complex tasks create hazardous conditions. Camera systems providing a bird view help monitoring the machine’s surroundings, but still require active monitoring while working. In this paper, we propose extending bird-view systems with object detection. We investigate and evaluate two different approaches: Detecting people in four fisheye camera images and transforming the obtained bounding boxes into a bird view, and directly detecting people in bird view. Our models, based on the YOLOv5 and YOLOv8 architectures, were trained and validated on custom datasets with 3,302 fisheye images and 1,343 bird-view images. the models achieved mAP@.5-.95 scores of 0.514 (fisheye) and 0.546 (bird view) and run at 25 frames per second on an NVIDIA Jetson Nano edge GPU. Our trained models for the detection utilize the YOLOv5 and YOLOv8 architecture and run inference with 25 frames per second on an NVIDIA Jetson Nano edge GPU. the first approach also addresses objects disappearing in stitching areas - characteristic of bird-view projection errors - by displaying bounding boxes even if they are invisible in bird view.

关键词： YOLO Surveys image edge detection image processing graphics processing units Birds Cameras Safety Machinery Monitoring

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HPDNET: Hyper Prior Dependent Demosaic Neural Network

HPDNET: Hyper Prior Dependent Demosaic Neural Network

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International image processing, Applications and Systems conference (IPAS)

作者： Youngil Seo Sungho Jun Dongpan Lim Seongwook Song System LSI Division Samsung Electronics Yongin Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. this presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation.

关键词： image quality Deep learning image sensors Artificial neural networks Pressing Feature extraction Rendering (computer graphics) Information filters Computational efficiency image restoration

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Segmentation of Ship Propeller Cavitation Area Using image-Based Anomaly Detection Network

Segmentation of Ship Propeller Cavitation Area Using Image-B...

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International image processing, Applications and Systems conference (IPAS)

作者： Wonje Jeong Yong-Jin Shin Soon-Yong Park School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea Samsung Heavy Industries Co. Ltd. Daejeon Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. the pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer Networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. the proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision.

关键词： Vibrations Deep learning Pressure sensors image segmentation Propellers Annotations Artificial neural networks Transformers Marine vehicles Anomaly detection

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6th International conference on computer vision and image processing, CVIP 2021

6th International Conference on Computer Vision and Image Pr...

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6th International conference on computer vision and image processing, CVIP 2021

ISBN: (纸本)9783031113451

the proceedings contain 97 papers. the special focus in this conference is on computer vision and image processing. the topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;vision HELPER: CNN Based Real Time Navigator for the Visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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6th International conference on computer vision and image processing, CVIP 2021

6th International Conference on Computer Vision and Image Pr...

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6th International conference on computer vision and image processing, CVIP 2021

ISBN: (纸本)9783031113482

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Microaneurysm Detection for Early DR Diagnosis Using Advanced image processing and a Lightweight Feature Pyramid Network

Microaneurysm Detection for Early DR Diagnosis Using Advance...

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International conference on Advancements in Computational Sciences (ICACS)

作者： Muhammad Zeeshan Tahir Muhammad Nasir Tufail Muhammad Sanyuan Zhang Muhammad Ahsan Qureshi Dept. of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China School of Biomedical Engineering University of Science and Technology of China Hefei Anhui China Department of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Diabetic Retinopathy (DR) is a diabetes-related condition that can lead to vision loss and even permanent blindness. the growing number of diabetic patients combined with a shortage of ophthalmologists underscores the need for automated screening tools to enable early detection. Microaneurysms (MAs), the earliest signs of DR, yet identifying them in fundus images is challenging because of their tint size and subtle features. Furthermore, low contrast, noise, and lighting variations in fundus images, including glare and shadows, add complexity to the detection process. To address these challenges, we applied image enhancement techniques such as green channel extraction, gamma correction, and median filtering to improve image quality. Additionally, to boost the performance of the MA detection model, we integrated a lightweight Feature Pyramid Network (FPN) with a pre-trained ResNet34 backbone, allowing for multiscale feature capture. Our method was evaluated on the E-ophtha dataset, achieving a sensitivity of 0.601, precision of 0.697 and F1 score of 0.651. the experimental findings indicate that the proposed method surpasses current approaches.

关键词： image quality Diabetic retinopathy Sensitivity Filtering Noise Lighting Blindness Feature extraction Complexity theory image enhancement

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