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AI-SIPM 2024: international Workshop on Artificial Intelligence for signal, image processing and Multimedia 24

AI-SIPM 2024: International Workshop on Artificial Intellige...

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4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Ketcham, Mahasak Phodong, Kanyalag Pramkeaw, Patiyuth Yimyam, Worawut Chumuang, Narumol Songmuang, Pokpong Ganokratanaa, thittaporn King Mongkuts Univ Technol Dept Informat Technol Management Bangkok Thailand Phranakhon Si Ayutthaya Rajabhat Univ Dept Comp Sci Phra Nakhon Si Ayutthaya Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Media Technol Program Bangkok Thailand Phetchaburi Rajabhat Univ Dept Business Informat Management Phetchaburi Thailand Muban Chombueng Rajabhat Univ Dept Digital Media Technol Bangkok Thailand Thammasat Univ Dept Comp Sci Bangkok Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Math Bangkok Thailand

ISBN: (纸本)9798400706028

the international Workshop on Artificial Intelligence for signal, image processing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, discuss recent advancements, and explore future directions in the field of artificial intelligence (AI) applied to signal processing, image processing, and multimedia technologies. this workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and image processing, as well as multimedia analysis and understanding. Researchers and practitioners from academia, industry, and government agencies are invited to submit their original research contributions and participate in discussions that foster collaboration and knowledge sharing across different domains. through this workshop, we aim to accelerate advancements in AI-driven technologies for signal processing, image analysis, and multimedia applications, contributing to the advancement of research and innovation in this rapidly evolving field.

关键词： algorithms signal processing image processing computer science artificial intelligence communication systems

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A Multi-Criteria Evaluation Approach for Ensemble Machine Learning algorithms in Precision Brain Cancer Detection 4

A Multi-Criteria Evaluation Approach for Ensemble Machine Le...

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4th international conference on Artificial Intelligence and signal processing

作者： Visalakshi, Gurugubelli Mohan, Laavanya Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

A brain tumour is a lump or collection of abnormal brain cells that has the potential to spread to nearby tissues and a precise diagnosis is necessary for effective treatment planning. the development of an algorithm for multi-criteria ensemble learning to identify brain cancer with decrease the time needed to process the classifier, which will improve accuracy. After using SMOTE and data augmentation, the model pre-processes the images. After that, a convolution neural network is used to analyze the data to extract features with Squared Hinge Loss function. the images that have been optimized are used as input for ensemble learning algorithms that are based on the gradient-boost and Ada-boost decision tree methods. the algorithm was selected with the intention of reducing processing complexity and temporal complexity without sacrificing CNN's accuracy. the system's accuracy for cancer diagnosis is discovered to be 98.45%, which is a superior value than the current approach.

关键词： Classification Ensemble learning SMOTE Brain image Ada-boost

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Leveraging advanced Deep Learning Techniques for Automated Dermatological image Classification 4

Leveraging Advanced Deep Learning Techniques for Automated D...

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4th IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2024

作者： Saha, Akashdip Suddapalli, Rishik Sahoo, Abhaya Kumar Singh, Jagannath School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798331528171

this paper examines the automatic classification of skin photos using the state-of-the-art deep learning methods, with a focus on the categorization of skin lesions. One of the main goals is to improve the accuracy and user-friendliness of dermatology diagnostics;thus, the diagnosis can be made much earlier and more accurately than now, leading to the detection of skin conditions. We use convolutional neural network-based (CNN) algorithms that are highly complex and the huge international Skin Imaging Collaboration (ISIC) library for facial recognition. this model will revolutionize skin diseases diagnosis, enabling clinical decisions to be made quickly and accurately, thereby enhancing patient care and outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Low-Power Design of advanced image processing algorithms under FPGA in Real-Time Applications 4

Low-Power Design of Advanced Image Processing Algorithms und...

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4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Wang, Yuyao Lamar University Department of Electrical Engineering BeaumontTX United States

ISBN: (纸本)9798350359589

Due to the real-time measurement required for image processing in practice, image processing technology is increasingly supplemented by hardware, such as using DSP (Digital signal processing) chips (image processing specific chips). However, when storing and processing large amounts of data, using DSP for image processing also has problems such as slow processing speed and poor real-time performance of the system. this article combines the embedded cache module in the IP core of FPGA (Field Programmable Gate Array) with the read/write ping-pong structure to store and retrieve image data, in order to save memory space on the chip. the relatively simple algorithm functions and parallel computing of FPGA, combined with a pipeline structure, shorten the image preprocessing time and solve the problem of poor real-time image processing. the experimental results show that due to the increase in workload, the static power consumption of FPGA algorithm design only increased from lW to 1.242W, while the static power consumption of traditional design increased from 1.009W to 1.578W;the dynamic power consumption of FPGA algorithm design has increased from 1.005W to 1.489W. © 2024 IEEE.

关键词： Field Programmable Gate Array image processing Low-power Design Synchronous Dynamic Random Access Memory

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Unified Dictionary Training Approach for High-Resolution image Enhancement 4

Unified Dictionary Training Approach for High-Resolution Ima...

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4th international conference on Artificial Intelligence and signal processing

作者： Srinadh, Kannuru David, Valaparla Lakshmi, Goruputi Ramakrishna, Karlapudi NIT Rourkela Dept ECE Rourkela Odisha India Anurag Engn Coll A Dept ECE Kodada Telangana India NIT Rourkela Dept Math Rourkela Odisha India

ISBN: (纸本)9798350350661;9798350350654

this research presents a new unified dictionary training super-resolution (UDTSR) approach to single and multi-image super-resolutions that uses a bilevel optimization framework and patchwise sparse recovery. By employing interconnected dictionaries to bridge the two spaces of image patches, we ensure that a sparse representation of a low-resolution (LR) image patch can accurately reconstruct its corresponding high-resolution (HR) patch. To facilitate efficient stochastic gradient descent, implicit differentiation calculates the gradient. Furthermore, by using a neural network model for rapid sparse inference and selective processing of visually essential areas, we can almost tenfold improve the performance of real-world applications. Also, by discovering the fundamental relationships between different data modalities, our approach overcomes the difficulty of dealing with panchromatic and multispectral images. For example, using shared and individual sparse representations, we describe a data model that can detect similarities and differences in multimodal signals. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) are advanced separately, with minimal research on their ideal combination. We propose a novel UDTSR analysis using an iterative shrinkage and thresholding algorithm. Our simulations of many combinations of SISR and MFSR, such as x2, x3, and x4, confirm our theory quantitatively and qualitatively.

关键词： Dictionary learning single image multi-frame image super-resolution sparse representations

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Research and Analysis on image Reconstruction Algorithm Based on Compressed Sensing 4

Research and Analysis on Image Reconstruction Algorithm Base...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Gao, Wenhuan Ren, Lifeng Jilin Agricultural Science and Technology University School of Electrical and Information Engineering Jilin China

ISBN: (纸本)9798331534622

Compressed Sensing (CS) is an efficient signal processing technique that can accurately acquire and reconstruct signals at lower sampling rates exploiting the sparsity of the signals. In the field of image processing, compressed sensing image reconstruction algorithms utilize the sparse representation of images to recover the original image from small number of observations. the core of this algorithm lies in the sparse representation of the signal, the construction of the observation matrix, and the selection of the reconstruction. First, it is assumed that the original image can be sparsely represented in a certain sparse basis, such as wavelet transform or sparse dictionary. then the sparse coefficients are randomly sampled to obtain the observations. Finally, the image is reconstructed using a compressed sensing algorithm based on the sampling data and the observation matrix. performance of the reconstruction algorithm is affected by factors such as the sparsity of the signal, the design of the observation matrix, and the selection of the reconstruction algorithm To improve the reconstruction performance, various improved algorithms have been proposed in recent years, such as the iterative hard thresholding algorithm with variable step size and the deep learning image compressed sensing algorithm. © 2024 IEEE.

关键词： image reconstruction

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Camouflaged Human Detection: comparative analysis using YoloV5s, Yolov5l, Yolov5m, Yolov5n, Yolov5x Model 4

Camouflaged Human Detection: comparative analysis using Yolo...

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4th international conference on Artificial Intelligence and signal processing

作者： Mulla, Sufiya Mandavkar, Rajlaxmi Jamadar, Simran Magdum, Sneha Gurav, Uma KITs Coll Engn Autonomous Dept CSE Data Sci Kolhapur Maharashtra India

ISBN: (纸本)9798350350661;9798350350654

this research paper discusses about, the Camouflaged human or object Detection using different variations of YOLOV5. Our project deep dives into the analysis of YOLO(You Only Look Once) algorithm and it's different types for performance analysis. the proposed model, trained on the dataset with different regions and angles of images, utilizes YOLOv5 for efficient camouflaged human detection, with manual annotation of images used for training the dataset and calculated different outputs for performance analysis. there can be lot of applications of the Camouflaged object detection project. Even if it is Biodiversity conservation in Nature like detecting the animals or plants which gets camouflaged in the nature and are difficult to detect or detecting enemy military invasion. this algorithm can be combined with one or more other image processing algorithms also for better accuracy and easy implementation purpose. this research work is carried out on different variants of YOLOv5 algorithm. We have used the different variants like Yolov5l, Yolov5m, Yolov5n, Yolov5x enhancing accuracy and robustness. the performance analysis is carried out in terms of P curve (Precision), R curve(recall), PR curve(precision and recall) and F1 curve of each training set and also the confusion matrix for all the algorithms is calculated and it is observed that Yolov5l works better as compared to other YOLO variants.

关键词： YOLOv5 image processing Camouflaged object detection

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Mathematical morphology operators for reading radioactivity DNA array images

Mathematical morphology operators for reading radioactivity ...

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4th IASTED international conference on Visualization, Imaging, and image processing

作者： Angulo, J Ecole Mines Paris Ctr Morphol Math F-77305 Fontainebleau France

ISBN: (纸本)0889864543

We have recently proposed an automatic non-supervised set of algorithms for an accurate spot data extraction from Cy3/Cy5 cDNA microarrays using morphological operators. On the basis of an image signal intensity model, we explore in this study the application of advanced morphological filters for the spot determination and the spot signal quantification on radio-labelled cDNA arrays. the idea behind the approach here developed is to use the connected operators like area and contrast opening, adaptively applied according to the image features (size of spots, back.-round estimate, etc.), for defining a zone of the positive spots where computing effectively the spot intensity without the overlapping and back-round contributions. A gridding algorithm is only necessary to index the detected spots to be able to identify the corresponding biological probe.

关键词： DNA array image spot quantification spot aridding mathematical morphology granulometry opening

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Comparative Evaluation of Various Wavelet Techniques for image Compression 4

Comparative Evaluation of Various Wavelet Techniques for Ima...

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4th IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2024

作者： Nisa, Hidayat Un Mehraj, Samrah Parah, Shabir A. University of Kashmir Dept. of Electronics and Instrumentation Technology Srinagar India

ISBN: (纸本)9798331528171

image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. this paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. the timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. this study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems. © 2024 IEEE.

关键词： Information management

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Large disparity image stitching technique based on improved RANSAC and NISwGSP algorithms 4

Large disparity image stitching technique based on improved ...

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4th international conference on signal image processing and Communication, ICSIPC 2024

作者： Li, Qin Huang, Bizhong Huang, Guanrong Li, Yimiao Chen, Hao Liang, Guang Chen, Xuetai Zhong, Qianhui Ke, Mingdong Xie, Dingbing Guangdong Power Grid Corp Zhanjiang Power Supply Bureau Guangdong Zhanjiang524000 China

ISBN: (纸本)9781510682467

Based on traditional image stitching technique, improved RANSAC and NISwGSP algorithms are proposed, which both boosted the number of matched points and significantly improved the algorithm efficiency. the operations include using secondary matching based on grid partitioning for feature points, optimizing the algorithm through multi-threaded concurrency and optimizing parameters to reduce computational complexity. Finally after incorporating the seam estimation strategy, the ghosting phenomenon in large-disparity image stitching has been alleviated. © 2024 SPIE.

关键词： image registration

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