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检索条件"任意字段=Image Processing: Machine Vision Applications"

共 6530 条记录，以下是1141-1150 订阅

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A vision-Based Solution for Water Level Monitoring in Flash Floods Scenario

A Vision-Based Solution for Water Level Monitoring in Flash ...

引用

IEEE Sensors applications Symposium, SAS

作者： Bruno Andò Salvatore Castorina Salvatore Graziani Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI) University of Catania Catania Italy

ISBN: (数字)9798350369250

ISBN: (纸本)9798350369267

Flash floods represent a serious problem, especially in urban areas, due to the consequences of sediment transport and other phenomena affecting security and life quality of citizens. The availability of low-cost solutions for the real-time monitoring of such phenomena, with particular regards to the water level in urban areas, is of main interest for the realization of early warning systems aimed to predict and handle hazardous events. In this paper a water level monitoring system, based on a low-cost vision system and a dedicated signal processing implemented on an embedded hardware platform, is presented. Main advantage of the proposed approach resides in the adopted sensing methodology, which besides being low-cost, allows for a robust estimation of the water level, without the need for active (powered) ground devices. A proof-of-concept aimed prototype has been realized and its performances have been assessed by means of a dedicated experimental survey. Obtained results highlight high sensitivity and specificity in the water level recognition, with an accuracy close to 100% over the whole explored detection range.

关键词： image recognition Target recognition machine vision Urban areas Signal processing algorithms Prototypes Cameras

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Computer vision for Supporting image Search

Computer Vision for Supporting Image Search

引用

7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Smeaton, Alan F. Insight Centre for Data Analytics Dublin City University 9 Glasnevin Dublin Ireland

ISBN: (纸本)9783030902346

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it. © 2021, Springer Nature Switzerland AG.

关键词： Computer vision

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Enhanced Efficient image Dehazing for Onboard Satellite imagery processing

Enhanced Efficient Image Dehazing for Onboard Satellite Imag...

引用

machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： D. Sai Sreekar Vishesh Vatsal Adithya Kothandhapani Ramakrishnan Rajagopalan Computer Vision Team Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Technology Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Products Hyspace Technologies Pvt. Ltd. Bangalore India Former Group Director at Space Applications Center ISRO

Satellite imagery is often composed of diverse terrains like forest, desert, snow and exhibits haze, fog, thin clouds which require dehazing in order to make them analysisready. Onboard processing of satellite imagery requires the algorithm's parameters to be fine-tuned depending on the type of terrain encountered. From the atmospheric light scattering model, the estimation of atmospheric light and transmission map is performed in single image dehazing method. This paper focuses on tuning an existing method the “Efficient image Dehazing with Boundary Constraints and Contextual Regularization method for satellite imagery”. A new image quality assessment method is introduced to enable fine-tuning the exponent of the algorithm. With the onset of onboard processing requirements, parallel implementation and faster image processing methods are explored for small run-times.

关键词： image quality Satellites Atmospheric modeling Snow image processing Light scattering Forestry

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Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings 1

引用

21st International Conference on image Analysis and processing (ICIAP)

作者： Aslan, Sinem Castellano, Giovanna Digeno, Vincenzo Migailo, Giuseppe Scaringi, Raffaele Vessio, Gennaro Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Venice Italy Univ Bari Aldo Moro Dept Comp Sci Bari Italy

ISBN: (数字)9783031133213

ISBN: (纸本)9783031133213;9783031133206

Recognizing the emotion an image evokes in the observer has long attracted the interest of the community for its many potential applications. However, it is a challenging task mainly due to the inherent complexity and subjectivity of human feelings. Such a difficulty is exacerbated in the domain of visual arts, mainly because of their abstract nature. In this work, we propose a new version of the artistic knowledge graph we were working on, namely Artgraph, obtained by integrating the emotion labels provided by the ArtEmis dataset. The proposed graph enables emotion-based information retrieval and knowledge discovery even without training a learning model. In addition, we propose an artwork emotion classification system that jointly exploits visual features and knowledge graph-embeddings. Experimental evaluation revealed that while improvements in emotion classification depend mainly on the use of visual features, the prediction of style, genre and emotion can benefit from the simultaneous exploitation of visual and contextual features and can assist each other in a synergistic way.

关键词： Visual arts Digital humanities Computer vision Deep learning Knowledge graphs Emotion classification

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Robust Face Detection and Recognition using image processing and OpenCV 6

Robust Face Detection and Recognition using Image Processing...

引用

6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Bhat, Aruna Jha, Rahul Kumar Kedia, Vansh Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9781665410281

An enormous progress in the fields of Artificial Intelligence, machine Learning, and Computer vision is witnessed over the last decade, owing to a doubling in processing capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. The approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code. © 2022 IEEE.

关键词： Face recognition

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Hyperspectral image Dimensionality Reduction Using Inter-Band Cross-Correlation and K-Means Clustering Algorithm

Hyperspectral Image Dimensionality Reduction Using Inter-Ban...

引用

Workshops on image processing Theory, Tools and applications, IPTA

作者： Henry Ndu Akbar Sheikh-Akbari Koushlendra K. Singh School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur India

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Hyperspectral imaging has become crucial in various domains, especially for the accurate detection of human veins in medical diagnostics, though managing the extensive data from hyperspectral (HS) images remains a challenge. To improve data handling during analysis, dimensionality reduction methods are frequently utilized. This paper presents a dimensionality reduction method for HS images using HS image inter-band cross-correlation and the K-means clustering algorithm. The proposed method computes inter-band correlations across all bands of the input HS image, which form a 2D correlation matrix. Eigen-decomposition is applied to the resulting matrix, extracting its eigenvectors and eigenvalues. The k-mean clustering algorithm is then applied to a selection of eigenvectors representing the largest eigenvalues, splitting the eigenvectors into several clusters. The reduced HS image is generated by averaging each cluster's image bands. The proposed dimensionality reduction method together with the Support Vector machine (SVM) classifier was then used for vein detection in HS images. The HyperVein image dataset was used to generate experimental results. Experimental results were generated for the proposed method and Principal Component Analysis (PCA) and Folded PCA (FPCA). Results show the proposed method outperforms PCA and FPCA in most performance metrics.

关键词： Dimensionality reduction Support vector machines Correlation Accuracy Clustering algorithms Eigenvalues and eigenfunctions Medical diagnosis Principal component analysis Hyperspectral imaging Morphological operations

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning

Vision-Based Autonomous Car Brake and Steering Assistance Us...

引用

AIML-applications for Engineering & Technology (ICAET), International Conference on

作者： Anees H Pooja Agrawal School of Robotics Defence institute of advanced technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies.

关键词： YOLO Instance segmentation Deep learning Visualization Accuracy Simulation Real-time systems Road safety Automobiles Autonomous vehicles

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Evolving processing Pipelines for Industrial Imaging with Cartesian Genetic Programming

Evolving Processing Pipelines for Industrial Imaging with Ca...

引用

Autonomic Computing and Self-Organizing Systems (ACSOS),International Conference on

作者： Andreas Margraf Henning Cui Anthony Stein Jörg Hähner Digital Production and AI Fraunhofer IGCV Augsburg Germany Organic Computing Group University of Augsburg Augsburg Germany AI in Agricultural Engineering University of Hohenheim Stuttgart Germany

The reconfiguration of machine vision systems heavily depends on the collection and availability of large datasets, rendering them inflexible and vulnerable to even minor changes in the data. This paper proposes a refinement of Miller’s Cartesian Genetic Programming methodology, aimed at generating filter pipelines for image processing tasks. The approach is based on CGP-IP, but specifically adapted for image processing in industrial monitoring applications. The suggested method allows for retraining of filter pipelines using small datasets; this concept of self-adaptivity renders high-precision machine vision more resilient to faulty machine settings or changes in the environment and provides compact programs. A dependency graph is introduced to rule out invalid pipeline solutions. Furthermore, we suggest to not only generate pipelines from scratch, but store and reapply previous solutions and re-adjust filter parameters. Our modifications are designed to increase the likelihood of early convergence and improvement in the fitness indicators. This form of self-adaptivity allows for a more resource-efficient configuration of image filter pipelines with small datasets.

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Development of an image Restoration Algorithm Utilizing Generative Adversarial Networks (GAN's) for Enhanced Performance in Engineering applications: A Comprehensive Approach to Improving image Quality and Clarity Through Advanced machine Learning Techniques

Development of an Image Restoration Algorithm Utilizing Gene...

引用

Innovation and Novelty in Engineering and Technology (INNOVA), International Conference on

作者： T.C. Manjunath G. Pavithra Gunjal G.H. Samyama Swapnil S. Ninawe Dept. of Electronics & Communication Engineering Dean Research (R & D) Rajrajeswari College of Engineering Bangalore Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore Karnataka

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

image restoration, a critical task in computer vision and image processing, focuses on recovering degraded or damaged images to their original, high-quality state. This paper introduces an innovative approach to image restoration using Generative Adversarial Networks (GANs). GANs, a prominent deep learning framework, consist of two neural networks-a generator and a discriminator-that compete to produce and evaluate realistic images. The generator creates images, while the discriminator distinguishes between real and generated ones, refining the generator's capability through adversarial training. Leveraging GANs' ability to learn complex image features, the proposed algorithm restores degraded images affected by noise, blur, and low resolution, producing high-quality, realistic results. Simulation outcomes demonstrate significant advancements in image restoration, showcasing GANs as a powerful tool for addressing challenges in this domain. The study underscores the potential of GANs in generating visually appealing restorations and advancing the state-of-the-art in image processing and restoration tasks.

关键词： Training image quality Technological innovation machine learning algorithms image resolution Refining Noise Generative adversarial networks Generators image restoration

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Alps: An Adaptive Load Partitioning Scaling Solution for Stream processing System on Skewed Stream 33rd

Alps: An Adaptive Load Partitioning Scaling Solution for Str...

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33rd International Conference on Database and Expert Systems applications (DEXA)

作者： Zou, Beiji Zhang, Tao Zhu, Chengzhang Xiao, Ling Zeng, Meng Chen, Zhi Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Coll Literature & Journalism Changsha 410083 Peoples R China Mobile Hlth Minist Educ China Mobile Joint Lab Changsha 410008 Peoples R China

ISBN: (纸本)9783031124266;9783031124259

The distributed stream processing system suffers from the rate variation and skewed distribution of input stream. The scaling policy is used to reduce the impact of rate variation, but cannot maintain high performance with a low overhead when input stream is skewed. To solve this issue, we propose Alps, an Adaptive Load Partitioning Scaling system. Alps exploits adaptive partitioning scaling algorithm based on the willingness function to determine whether to use a partitioning policy. To our knowledge, this is the first approach integrates scaling policy and partitioning policy in an adaptive manner. In addition, Alps achieves the outstanding performance of distributed stream processing system with the least overhead. Compared with state-of-the-art scaling approach DS2, Alps reduces the end-to-end latency by 2 orders of magnitude on high-speed skewed stream and avoids the waste of resources on low-speed or balanced stream.

关键词： Data streams Stream processing system Adaptive scaling policy

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