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

共 12901 条记录，以下是111-120 订阅

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An Efficient Signal and Noise Simulator for Imaging Sonar

An Efficient Signal and Noise Simulator for Imaging Sonar

引用

OCEANS conference

作者： Jose, Tessin K. Subhalakshmy, A. B. Hareesh, G. Def Res & Dev Org Naval Phys & Oceanog Lab Kochi Kerala India

ISBN: (纸本)9798350362077

High-frequency imaging sonar systems are a critical technology for underwater sensing. Design and experimental validation of signal processing algorithms for image reconstruction is very challenging due to difficulties in deployment and accurate positioning. Modelling and simulation are powerful tools to evaluate the system before prototyping. Unfortunately, there are only a few simulation tools available, and the computation complexity for accurate simulation is very high. Most of the simulators directly convert the estimated target range, receive beam direction and reflected intensity level in to synthetic sonar image. These simulators are not accounting for the spatial response of the array with beamformer and temporal response of the waveform, while generating the image. To evaluate the effectiveness of different signal processing techniques for image reconstruction, a received signal model for the sensor array considering the array and waveform properties is essential. This paper presents an efficient signal and noise simulator, to generate the sonar receiver array signal time series for practical imaging scenarios, with five times lesser computation time compared to the state-of-the-art simulator reported in the literature. The simulator also contains a basic sonar processing module which performs near-field beamforming, matched filtering and image processing on the simulated signal, to generate the final sonar image. Optimisation in the sonar receiver and signal processing can be conveniently evaluated through this simulation scheme. The simulation results are compared with experimental results to verify the simulation model. Being generic in nature this simulator can simulate sector scan sonar, single beam and multi-beam side scan sonars simultaneously for the same simulation scenario. This enables verification of co-registration of side scan and gap filler sonar images.

关键词： Imaging sonar simulation Sonar signal noise simulation Synthetic sonar image generation

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Exploration on Intelligent Virtual Reality Technology and algorithms for VR Platforms under Intelligent Control and Computer Vision 2

Exploration on Intelligent Virtual Reality Technology and Al...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Wei, Yuhua Chen, Yunxing Guangzhou Huali Science and Technology Vocation College Guangzhou China

ISBN: (纸本)9798350317558

Intelligent control and computer vision algorithms can achieve clearer, more delicate, and realistic image effects through extremely high image quality and target recognition technology, as well as complete image processing tasks faster, thereby improving user experience and interaction effects. This study is based on intelligent control and computer vision technology and algorithms, aiming to design a highly integrated VR (virtual reality) platform. This article collected and established evaluation data and related parameters for the intelligent VR platform. Intelligent control and computer vision algorithms can evaluate and analyze intelligent VR platforms, and improve their user experience and interaction effects by optimizing structural parameters and performance indicators. After experimental testing, the subjective image quality rating of the algorithm platform in this article was between 8.41 and 9.32, and the target recognition rate was between 6.72 and 9.87. The design based on intelligent control and computer vision algorithms can improve the image quality and target recognition rate of the VR platform, achieving precise detection and recognition of target objects. This study provided an effective method and technology for the design of intelligent VR platforms, and provided strong support for improving the image quality and target recognition rate of VR platforms. © 2024 IEEE.

关键词： computer vision image quality intelligent control intelligent VR platform target recognition rate

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A Novel Extraction Algorithm for Pixel Recognition in AI images with Feature Point 2

A Novel Extraction Algorithm for Pixel Recognition in AI Ima...

引用

2nd IEEE International conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Fan, Jiajia Yuan, Bo Jin, Zhuang College of Computer Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798331504960

In the era of big data, image processing still faces significant bottlenecks compared to other fields of computer science. In this paper we studied the feature point extraction algorithm based on gray points and the stereo matching technique, both of them play important roles in image processing. The feature point extraction algorithm based on gray points includes Moravec algorithm and Susan Algorithm. The results indicate that it can discover that simplified inverse filter tracking (SIFT) algorithm is the best algorithm among all the feature point extraction algorithms, which has have strong stability, fast calculating speed and high accuracy. In stereo image matching technique, we found that dense matching algorithm plays an important role in target detection and tracking in the three-dimensional reconstruction operations. By comparing the matching process between traditional nearest neighbor matching and this paper's matching algorithm, we concluded that the matching relationship in this algorithm is entirely correct. © 2024 IEEE.

关键词： image matching

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Enhancing Consistency-Based image Generation via Adversarially-Trained Classification and Energy-Based Discrimination 38

Enhancing Consistency-Based Image Generation via Adversarial...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Golan, Shelly Ganz, Roy Elad, Michael Technion Israel

The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowness of diffusion models by directly mapping noise to data, while maintaining a (relatively) simpler training. Consistency models enable a fast one- or few-step generation, but they typically fall somewhat short in sample quality when compared to their diffusion origins. In this work we propose a novel and highly effective technique for post-processing Consistency-based generated images, enhancing their perceptual quality. Our approach utilizes a joint classifier-discriminator model, in which both portions are trained adversarially. While the classifier aims to grade an image based on its assignment to a designated class, the discriminator portion of the very same network leverages the softmax values to assess the proximity of the input image to the targeted data manifold, thereby serving as an Energy-based Model. By employing example-specific projected gradient iterations under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the imageNet 64x64 dataset for both Consistency-Training and Consistency-Distillation techniques. © 2024 Neural information processing systems foundation. All rights reserved.

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A Study on multi-scale landscape character extraction combining GIS and graphic processing algorithms 3

A Study on multi-scale landscape character extraction combin...

引用

3rd International conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Wang, Shuwei Liu, Yang Li, Guangqi Su, Xueying Wang, Sen Bai, Xuyong Center for Geophysical Survey China Geological Survey Langfang China Technology Innovation Center for Earth Near Surface Detection China Geological Survey China

ISBN: (纸本)9781510690219

This research presents an innovative approach to accurately extract multi-scale landscape characters by combining geographic information systems (GIS) and graphic processing algorithms. It is essential to recognize and analyze landscape characters at different scales. This study uses GIS advantages in spatial analysis and the K-Means clustering algorithm in image enhancement, feature extraction, image segmentation, and color quantification. Through image interpretation and deep learning, the distribution characteristics of buildings in Langfang City are obtained to provide guidance and suggestions for the future construction and development of the city. The proposed method covers data preparation, algorithm design, and system implementation. In addition, the future research direction of improving the method is also discussed, and the possibility of its application in other fields is explored. © 2025 SPIE.

关键词： Photointerpretation

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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...

引用

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 ...

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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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