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

共 13626 条记录，以下是121-130 订阅

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Deep Optics: Learning Cameras and Optical Computing systems 4

Deep Optics: Learning Cameras and Optical Computing Systems

引用

AI and Optical Data Sciences iv 2023

作者： Wetzstein, Gordon Electrical Engineering Department Stanford University StanfordCA94305 United States

ISBN: (纸本)9781510659810

Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we explore hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can learn application-domain-specific cameras using modern artificial intelligence techniques or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems. © 2023 SPIE.

关键词： Optical data processing

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Synergizing Support Vector Machines and image processing for Superior Skin Cancer Detection : A Crammer-Singer Approach 9

Synergizing Support Vector Machines and Image Processing for...

引用

9th International conference on Communication and Electronics systems, ICCES 2024

作者： Divya, S. Thilagavathi, S. Sri Krishna Adithya College of Arts and Science Department of Computer Science Coimbatore India

ISBN: (纸本)9798350377972

Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with image processing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines image processing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve

关键词： Median filters

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Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems 4

Research on traffic sign recognition algorithms for autonomo...

引用

4th International conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wang, Chuanfei School of Government Beijing Normal University Beijing100875 China

ISBN: (纸本)9781510682528

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. This study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. The images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. The research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.

关键词： Object detection Detection and tracking algorithms Unmanned vehicles Performance modeling Feature extraction image processing Data modeling image segmentation Mathematical optimization

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Comparative Study of Pre-processing Data on Scoliosis Type Classification on X-Ray image Using CNN Algorithm 6th

Comparative Study of Pre-processing Data on Scoliosis Type C...

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6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Fitrianah, Devi Muhammad, Agit Nurhaida, Ida Bina Nusantara Univ Sch Comp Sci Dept Comp Sci Jakarta 11530 Indonesia Univ Mercu Buana Fac Comp Sci Jakarta 11650 Indonesia Univ Pembangunan Jaya Dept Informat Tangerang Selatan Indonesia Univ Pembangunan Jaya Ctr Urban Studies Tangerang Selatan Indonesia

ISBN: (纸本)9783031214370;9783031214387

Deep Learning is a technology developed with GPU Acceleration that has a good ability to process image computations. One of the deep learning methods widely used in classifying two-dimensional objects is the Convolutional Neural Network algorithm. Just like other image processing algorithms, the classification process is very dependent on the quality of the image used. Therefore, it is concerned that pre-processing is done. This study aimed to find a scenario for image data pre-processing by comparing the threshold types used. By using two scenarios, the first scenario using Simple Threshold and the second scenario using threshold Canny. The first scenario begins with collecting data from an X-ray image after the established dataset is advanced to pre-process the data set. In this pre-processing data, several things were done to increase the level of data accuracy by changing and equalizing the pixel size in the dataset, changing the color of the image on the dataset to grayscale, distributing the histogram or commonly known as histogram equalization, and finally applying a simple threshold. Unlike the second scenario, which does not use a simple threshold but uses a threshold canny. After completion of the pre-processing stage and then the continued training phase. At this stage, the dataset will be trained using CNN. After the dataset is trained, it enters the testing stage. The testing stage shows the results that the data is classified properly. The validation obtained from the two scenarios shows that the simple threshold gives better results than the canny threshold, with a value that shows a simple threshold of 97% and a canny threshold of 89%. This result shows that the dataset's treatment differences greatly affect the results' accuracy.

关键词： Threshold Canny X-ray CNN Scoliosis Histogram equalisation

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

引用

2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Jain, Anamika Langhe, Anagha Choudhary, Harsh Mishra, Ashutosh School of Computer Engineering and Technology Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798350372229

In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their spread and minimize any damage. However, this task often requires a high amount of labor and experience. This project suggests using image processing techniques to extract characteristics from images of plant leaves and then utilizing machine learning algorithms to classify these leaves as either healthy or diseased. The evaluation of machine learning algorithms, such as Convolutional Neural Networks, was conducted to measure their performance, using their accuracy, precision, and recall. The proposed system displays a level of accuracy in detecting and classifying plant diseases. It focuses on categorizing images of types of plants into disease types as well as healthy ones. These findings highlight the potential benefits of employing machine learning techniques in the detection of plant diseases offering farmers a tool for managing and safeguarding their crops. © 2024 IEEE.

关键词： agriculture classification crop management feature extraction image processing machine learning

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Application of Computer Vision for Efficient Maintenance of Cleanliness and Order in Commercial Areas: An Invention Approach for Automated Management in Commercial Zones 8

Application of Computer Vision for Efficient Maintenance of ...

引用

8th International conference on Communication and Information systems, ICCIS 2024

作者： Llacta, Jesus David Villon Simeon, Wiler Yosef Hurtado Rosales, Jorge Martin Vega Universidad Continental Facultad de Ingeniería Huancayo Peru Universidad Continental Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798331531171

This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing commercial spaces, this paper proposes a technological model that utilizes strategically placed cameras and image processing algorithms to detect disorganized or dirty tables, sending real-time alerts to cleaning staff via a mobile application. The goal is to optimize resource use, reduce response times, and enhance customer experience. To validate the model, an experiment was conducted with a sample of 30 customers, measuring their satisfaction levels before and after the system's implementation. The results, analyzed through statistical tests, show a significant improvement in customer satisfaction. Additionally, the study discusses its limitations and proposes future research directions, including an evaluation of the costs associated with the automated system compared to traditional methods. © 2024 IEEE.

关键词： automation cleaning management commercial areas Computer vision customer experience detection algorithms image processing operational efficiency

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression 2

Fundus Image Classification for Glaucoma using U-Net Archite...

引用

2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Bajaj, Vanshika Kotambkar Shelke, Deepali M. Shri Ramdeobaba College of Engineering and Management Dept. of Information Technology Nagpur India

ISBN: (纸本)9798350310719

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas. © 2023 IEEE.

关键词： Computer aided diagnosis

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Breaking Through Color Casts: Enhancing image Fidelity with Machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

引用

2024 International conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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Robust image watermarking scheme based on orientation of local image texture 16

Robust image watermarking scheme based on orientation of loc...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Li, Qiutong Zhang, Qiyuan Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

With the rapid development of big data and Internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust watermarking technology can effectively resist various attacks and protect image copyright. Most of the existing robust watermarking schemes generally adopt the global embedding method and often ignore the texture orientation of the cover image itself leading to poor imperceptibility and robustness. In this paper, we propose an innovative digital image robust watermarking scheme. Firstly, we conduct the Oriented FAST and Rotated BRIEF (ORB) and Speeded Up Robust Features (SURF) feature point detection algorithms, combined with the texture complexity of the cover image to select the most complex texture region as the local embedding location of the watermark. Secondly, to further improve the robustness of the watermarked image, we conduct the Discrete Cosine Transform (DCT) adaptive inter-block correlation algorithm. We embed the watermark into the cover image by adjusting the difference between the image embedding block and the adjacent block. We have tested a dataset containing multiple images, in which various common attacks are simulated and multiple cover images are tested. By comparing the experimental results, our scheme performs better regarding robustness and imperceptibility. © 2025 SPIE.

关键词： Cosine transforms

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processing and Classification algorithms of Medical images 6

Processing and Classification Algorithms of Medical Images

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

作者： Seitaj, Oltiana Mechanics University of Roma Tre Department of Industrial Engineering Electronics Rome Italy

ISBN: (纸本)9798331506520

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional Neural Networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation. © 2025 IEEE.

关键词： Convolutional neural networks

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