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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

共 12978 条记录，以下是151-160 订阅

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Dual-band image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： image fusion

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Alzheimer's Disease Classification and Severity Level Identification in image processing using Deep Learning algorithms 2

Alzheimer's Disease Classification and Severity Level Identi...

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Devi, T. Sangeetha Raghavendran, V Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci Sch Comp Sci Chennai 600117 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCI). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with InceptionV3 model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.

关键词： Alzheimer disease detection severity level analysis deep learning model InceptionV3 Random Forest

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PiVisionSort: Integrating image processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

引用

2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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Conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

引用

2nd International conference on Information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial Intelligence image processing Smart Firearms Wildlife Conservation Hunting Tourism

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image Recognition System Based on Distributed Intelligence Technology to Optimize User Experience 2

Image Recognition System Based on Distributed Intelligence T...

引用

2nd IEEE International conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Huang, Yihang Hanyang University Graduate School of Design Visual Design Major Ansan Korea Republic of

ISBN: (纸本)9798350372892

With the continuous progress of computer technology, distributed intelligent systems become more and more popular. Based on this background, this paper discusses the theory of image recognition based on distributed intelligent network model and aims to implement the user location function through this theory. In this paper, an automatic detection algorithm based on distributed intelligence technology is proposed for implementing the function of user location. This algorithm is based on the theory of image recognition based on distributed intelligent network model and processes the image data through a specific algorithmic logic in order to locate the user's position. The applicability of the algorithm in real projects is verified by writing experimental programmes. The Distributed Intelligence technique was compared with the other three algorithms and the results showed that the Distributed Intelligence technique performed best in terms of adaptability and stability. The comprehensive data exceeds 85.5%, indicating that the algorithm has high accuracy and efficiency in recognising and processing images, solves the problem of matching the target with the environmental scene, and provides an important reference and comparison basis for image acquisition and processing. The concluding observation of this paper is that the Distributed Intelligence Technology has the best comprehensive performance in different environments, not only superior to other algorithms in terms of adaptability and stability, but also its comprehensive data index exceeds 85.5%. © 2024 IEEE.

关键词： distributed intelligence technology image recognition optimization research user experience

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Low-illuminance image Enhancement Algorithm for Indoor Wine Cellar Scenes Based on Retinex-net

Low-illuminance Image Enhancement Algorithm for Indoor Wine ...

引用

International conference on Electronic Engineering and Information systems (EEISS)

作者： Han, Yunjie Chen, Guanyu Li, Chang Liu, Songsong Wan, Chaoye Guizhou Zhongke Repchain Cloud Technol Co Ltd Guiyang Peoples R China Guiyang Informat Technol Res Inst Guiyang Peoples R China

ISBN: (纸本)9798350351040;9798350351033

Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental light, and the imaging quality is poor and there are many noise points in low light environments. The influence of ambient light poses significant challenges to computer vision. To address the issues of poor image visibility and high interference in low illuminance environments, this article proposes an improved low-illuminance image enhancement method based on Retinex-Net network. Convert the input image from RGB domain to IISV color space for processing, introduce denoising convolutional neural network Deam-Net network into the reflection image of V component for denoising, enhance the color of the illumination image of V component through spatial attention module and channel attention module, and perform bilateral filtering and contrast stretching on the S component. Finally, fuse all components and convert to RGB for obtain the enhanced image. Prove through verification have shown that the low -illumination images enhanced by the algorithm proposed in this article have improved brightness, prominent details, minimal image distortion, and are realistic and natural. They are superior to other algorithms from subjective feelings and objective evaluation indicators.

关键词： component Retinex Net image decomposition Attention module Low light image enhancement

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HPDNET: Hyper Prior Dependent Demosaic Neural Network 6

HPDNET: Hyper Prior Dependent Demosaic Neural Network

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

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

ISBN: (纸本)9798331506520

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. © 2025 IEEE.

关键词： image quality

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Siamese-Driven Optimization for Low-Resolution image Latent Embedding in image Captioning 27

Siamese-Driven Optimization for Low-Resolution Image Latent ...

引用

27th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Tan, Jing Jie Mokraoui, Anissa Kwan, Ban-Hoe Ng, Danny Wee-Kiat Hum, Yan-Chai Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Department of Mechatronics and Biomedical Engineering Malaysia Université Sorbonne Paris Nord Laboratoire de traitement et transport de l'information France

ISBN: (纸本)9788362065486

image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-computer interaction. However, a recent challenge in this domain is dealing with low-resolution image (LRI). While performance can be improved by using larger models like transformers for encoding, these models are typically heavyweight, demanding significant computational resources and memory, leading to challenges in retraining. To address this, the proposed SOLI (Siamese-Driven Optimization for Low-Resolution image Latent Embedding in image Captioning) approach presents a solution specifically designed for lightweight, low-resolution images captioning. It employs a Siamese network architecture to optimize latent embeddings, enhancing the efficiency and accuracy of the image-to-text translation process. By focusing on a dual-pathway neural network structure, SOLI minimizes computational overhead without sacrificing performance, making it an ideal choice for training on resource-constrained scenarios. © 2024 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Signal encoding

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Compact colorful compressive spectral imager based on deep learning reconstruction

Compact colorful compressive spectral imager based on deep l...

引用

2024 International conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Li, Jinshan Ma, Xu Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510680425

Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressive *** CCSI systems enhance the modulation freedom through the combination of colorful coding mask (CCM) and dispersive element, but this kind of system has complex structure that limits the miniaturization of ***, the reconstruction quality of CCSI systems can be further improved by using deep learning *** paper proposes a compact CCSI method based on deep learning reconstruction, which tries to reduce the volume of system by attaching the CCM to the *** combination of CCM and RGB detector enhances the modulation ***, a Transformer-based deep learning algorithm is used to obtain promising reconstruction results of the target spectral *** of both simulations and experiments demonstrate the effectiveness of the proposed compact CSSI method. © 2024 SPIE.

关键词： image compression

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Joint Dehazing and Low-light image Enhancement with Complete Atmospheric Model 16

Joint Dehazing and Low-light Image Enhancement with Complete...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Wu, Yong Xia, Kexiang Ge, Liyong Ye, Jiongkai Zhejiang Institute of Communications Hangzhou311112 China The School of Automation Hangzhou Dianzi University Hangzhou310000 China

ISBN: (纸本)9781510689251

Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model that simultaneously considers haze and low-light conditions. This model incorporates a global darkness factor as a disturbance variable alongside the atmospheric scattering model, enabling adaptive luminance balancing without the need to estimate the global atmospheric light or the global darkness factor. Building on the principle of object color rendition, This paper justifies the use of the dark channel prior for haze removal and extends the bright channel prior for low-light image enhancement. Furthermore, we explore the mapping relationship between normal and inverted images based on the complete atmospheric model. By solving for the transmission rates of the global atmospheric light and global darkness factor, we propose an image enhancement algorithm capable of concurrently processing haze and low-light images. The proposed algorithm effectively mitigates issues of contrast reduction and color distortion. Experimental results demonstrate the efficacy of our approach, showing that the visual outcomes of simultaneous haze removal and low-light enhancement surpass those achieved through single-task haze removal. © 2025 SPIE.

关键词： Demulsification

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