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

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Automatic conversion system of digital music score image based on image recognition algorithm 3

Automatic conversion system of digital music score image bas...

引用

3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Yi Xi'an Conservatory of Music Shaanxi Xi'an710000 China

ISBN: (纸本)9781510661240

The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method. © 2022 SPIE.

关键词： image recognition

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Application Of MLOps Practices For Biomedical image Classification 5

Application Of MLOps Practices For Biomedical Image Classifi...

引用

5th International conference on Informatics and Data-Driven Medicine (IDDM)

作者： Berezsky, Oleh Pitsun, Oleh Melnyk, Grygory Batko, Yuriy Derysh, Bohdan Liashchynskyi, Petro West Ukrainian Natl Univ 11 Lvivska St UA-46001 Ternopol Ukraine

With active hardware development, the number of software machine learning-based systems has increased dramatically in all areas of human activity, in particular, in medicine. The use of machine learning elements in software systems requires the organization of a pipeline process of software development, testing, and support. The application of MLOps technologies will improve the quality and speed of system development, as well as simplify the process of adjusting the algorithm parameters to improve the system operation quality. The purpose of this work is to develop an MLOps pipeline that will consider all the necessary stages of software development based on machine learning algorithms for biomedical image processing.

关键词： Machine learning MLOps biomedical images programming

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Retinal image Segmentation U sing Clustering Methods: Performance Analysis 19

Retinal Image Segmentation U sing Clustering Methods: Perfor...

引用

19th IEEE International Multi-conference on systems, Signals and Devices, SSD 2022

作者： Mehidi, Imane Jabri, Dalel Belkhiat, Djamel Eddine Chouaib Ferhat Abbas University Dac Hr Laboratory Setif Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Clustering is a popular method for seg-menting retinal images due to its effectiveness in performance. This paper investigates the ability of multiple clustering algorithms to segment the retinal image to isolate blood vessels. The flowchart of this analyzes two phases. First, the pre-processing phase aims to increase the contrast of the image by applying a series of operations: Contrast Limited Adaptive Histogram Equalization (CLAHE), median filtering, and bottom-hat filtering. Furthermore, the second phase deals with the segmentation of the pre-processed image using one of the clustering algorithms selected for this study: k-means (KM), k-medoids (KD), Gaussian Mixture Model (GMM), and fuzzy c-means (FCM). The experimental part is carried out on the DRIVE and STARE databases, and the performance measure of each algorithm is accomplished using sensitivity, specificity, and accu-racy performance metrics. In addition, the running times of different algorithms are compared. Finally, the obtained results are extensively discussed, and some suggestions are proposed at the end of this paper. © 2022 IEEE.

关键词： Blood vessels

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Development of an Urban Vegetation Monitoring System using image processing and Deep Learning 10

Development of an Urban Vegetation Monitoring System using I...

引用

10th IEEE International conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Alighaleh, Pejman Ooi, Melanie Kaung, Ye Chow University of Waikato School of Engineering Hamilton New Zealand

ISBN: (纸本)9798350365283

Automated monitoring of urban vegetation, particularly trees, facilitates large-scale urban planning and environmental surveillance. However, such systems can be cost-prohibitive to deploy, especially in smaller cities. This paper describes a cost-efficient standalone RGB-imaging platform with adaptable mounting for service vehicles, implemented in Hamilton East, Hamilton City, New Zealand. First, the hardware design and imaging platform are described. Then, to evaluate the quality of the tree imagery captured by this system, two deep-learning models, YOLOv5n and YOLOv8n, were trained using the collected images. The collected dataset consists of approximately 12,000 images of various tree species typical to the region. Of these, 450 images (4%) were used for training and validation, and the remaining images were used for testing. The models were then tested on images collected in 4 different test image collection sessions. The results of the models in the validation phase showed that the YOLOv5n model demonstrated a precision of 0.87 and a recall of 0.85, compared to YOLOv8n's precision of 0.86 and recall of 0.82. During the test phase, both models performed comparably, indicating that the imaging system can capture high-quality images before direct use with deep-learning algorithms for tree detection. Furthermore, the imaging system is capable of capturing at least 11 images per tree on an urban route, showing the potential to be deployed for urban vegetation monitoring. © 2024 IEEE.

关键词： Imaging systems

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Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation 48

Secure and Decentralized Collaboration in Oncology: A Blockc...

引用

48th Annual IEEE International Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Ranjbarzadeh, Ramin Keles, Ayse Crane, Martin Anari, Shokofeh Bendechache, Malika Dublin City Univ Fac Engn & Comp Sch Comp Dublin Ireland Ankara Medipol Univ Fac Engn & Nat Sci Dept Artificial Intelligence Engn Ankara Turkiye Islamic Azad Univ South Tehran Branch Dept Accounting Econ & Financial Sci Tehran Iran Univ Galway ADAPT Res Ctr Sch Comp Sci Galway Ireland

ISBN: (纸本)9798350376975;9798350376968

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

关键词： Blockchain Technology Decentralized Healthcare systems Collaborative Annotation Medical image processing Data Security and Privacy

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Human Detection using ROI of an image

Human Detection using ROI of an Image

引用

2024 International conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Kartik, R. Megadarshini, V. Kavisri, S. Manimegalai, R. PSG Institute of Technology and Applied Research Department of EEE Tamilnadu India

ISBN: (纸本)9798350378177

Efficient human presence detection within specified Regions of Interest (ROIs) is essential to many real-world applications, including as resource allocation, security surveillance, and crowd monitoring. In this paper, we present a robust human pres-ence detection system leveraging advanced techniques, notably YOLOv8, for accurate and real-time object detection. Our system addresses the challenges associated with traditional methods by focusing on multi-scale detection windows and efficient merging of nearby detections to refine results. We introduce a streamlined approach for ROI definition and utilize transfer learning to adapt YOLOv8 to specific human presence detection tasks. Extensive experiments on diverse datasets demonstrate the effectiveness and robustness of our system, showcasing superior performance in terms of accuracy, real-time processing, and adaptability to chal-lenging scenarios. Additionally, we implemented four algorithms, including Histogram of Oriented Gradients (HOG) [1], Roboflow 3.0 object detection algorithm, YOLOv5, and YOLOv8. After thorough examination, we determined that YOLOv8 showed the most favorable outcomes in terms of both precision and speed. Hence, we opted for YOLOv8 as the primary algorithm for our system aimed at detecting human presence. This decision was informed by its superior performance across diverse datasets and its capability to address the challenges associated with traditional methods. © 2024 IEEE.

关键词： Object detection

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Synthetic Aperture Radar Signal processing Algorithm Implementation in Python 5

Synthetic Aperture Radar Signal Processing Algorithm Impleme...

引用

5th IEEE Asia-Pacific conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Setiadi, Bambang Arisal, Andria Firmansyah, Iman Subekti, Agus Chasanah, Nurul Hamonangan, Jefri Abner Kurniawan, Farohaji Research Center for Data and Information Sciences National Research and Innovation Agendy Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Research Center for Aeronautics Technology National Research and Innovation Agency Bogor Indonesia

ISBN: (纸本)9798350398212

The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks. © 2022 IEEE.

关键词： Python

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Quilt-1M: One Million image-Text Pairs for Histopathology 37

Quilt-1M: One Million Image-Text Pairs for Histopathology

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Ikezogwo, Wisdom O. Seyfioglu, Mehmet S. Ghezloo, Fatemeh Geva, Dylan Mohammed, Fatwir S. Anand, Pavan K. Krishna, Ranjay Shapiro, Linda G. University of Washington United States

ISBN: (纸本)9781713899921

Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has slowed comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1, 087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of 802, 144 image and text pairs. QUILT was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: QUILT-1M, with 1M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal retrieval tasks. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Large datasets

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Utilizing Sensitive Features for image Tampering Detection 5

Utilizing Sensitive Features for Image Tampering Detection

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5th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2022

作者： Deng, Yuchao School of Software Northwestern Poly Technical University Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

image tampering has brought a great negative impact on society. People who do not know the truth are easy to be misled and used by people with intentions. Its impact on society has attracted extensive attention of Chinese and foreign researchers. With the development of image editing technology, image tampering detection has made remarkable achievements from the initial naked eye recognition to machine learning recognition of tampered images, and then to the excellent characteristics of deep learning algorithms in processing images in complex image tampering detection in recent years. In view of the outstanding performance of Faster RCNN network in the field of target detection technology, this paper studies an image algorithm and detection method based on Faster R-CNN network, and adds position sensitive candidate area pooling on this basis. In each rectangular cell, the pixel values of a fixed number of sampling points at a fixed position are calculated for average pooling. The input tampered image is feature extracted through the feature extraction network. After the extracted feature map passes through the RPN layer, the tampered area candidate frame is automatically generated for subsequent classification and regression. Experimental results show that this method greatly improves the effect of image tampering detection. © 2022 IEEE.

关键词： Learning algorithms

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Convolution Neural Networks: Intersection of Deep Learning and image processing in Computational Art 10

Convolution Neural Networks: Intersection of Deep Learning a...

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10th International conference on Digital and Interactive Arts (ARTECH)

作者： Shabbar, Aya Tishreen Univ Mechatron Engn Latakia Syria

ISBN: (纸本)9781450384209

Deep Learning and image processing is a key concept in today's world of computational art, where artists employed AI algorithms to generate visuals. This paper explores AI-generated images, using Convolutional Neural Networks software as a paradigm of symbolic AI creative systems, and contextualizes the use of modern image processing technologies to create visual artworks. It discusses the methodologies and strategies used to make art using AI algorithms, manipulating them with processing software tool. The discussion focuses on CNN (Convolutional Neural Network) and processing software (Java) as the main technologies used in distinct fields to generate images. My conception of technical images provides a conceptual framework for examining the qualities and attributes of AI-generated images.

关键词： Deep learning image processing computational art convolutional neural networks

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