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

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Development of a Procedure for Improving the Effectiveness of Personal Identification systems Based on the Analysis of Facial images 17

Development of a Procedure for Improving the Effectiveness o...

引用

17th International Scientific and Technical conference on Dynamics of systems, Mechanisms and Machines, Dynamics 2023

作者： Tatarkanov, A.A. Umyskov, A.A. Kuklin, V. Zh. Institute of Design and Technology Informatics of RAS Moscow Russia

ISBN: (纸本)9798350358315

This research aims to develop a new procedure to improve the performance of the personal identification system. This process will be based on the analysis of digital facial images using special artificial intelligence technologies. Additionally, it is emphasized that the successful development of a completely new neural network algorithm for personality identification and its special robust modification are required to achieve this goal. Furthermore, it is essential that this algorithm demonstrates increased effectiveness in identifying faces. As part of this algorithm, two key components are distinguished, namely, an algorithm aimed at determining the area where a face is located in an image, which involves the use of a standard version of a convolutional neural network. And the presented neural network algorithm is the second component, which is designed for personal identification with a specific robust modification. The studies presented have shown that an algorithm with a proposed architecture, will have around 25 times less weight parameters than the algorithms utilized at present. Also, unlike other algorithms, it was possible to improve the level of accuracy. © 2023 IEEE.

关键词： image enhancement

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YUSEG:Yolo and Unet is all you need for cell instance segmentation 36

YUSEG:Yolo and Unet is all you need for cell instance segmen...

引用

Cell Segmentation Challenge in Multi-Modality High-Resolution Microscopy images - 36th conference on Neural Information processing systems, NeurIPS 2022

作者： Bai, Bizhe Tian, Jie Luo, Sicong Wang, Tao Sisuo, L.Y.U. University of Queensland Australia Southern Medical University Guangdong Guangzhou China Xi’an Jiaotong University Shaanxi Xi’an China Fuzhou University Fujian Fuzhou China Harbin Institute of Technology Shenzhen Guangzhou Shenzhen China

Cell instance segmentation, which identifies each specific cell area within a microscope image, is helpful for cell analysis. Because of the high computational cost brought on by the large number of objects in the scene, mainstream instance segmentation techniques require much time and computational resources. In this paper, we proposed a two-stage method in which the first stage detects the bounding boxes of cells, and the second stage is segmentation in the detected bounding boxes. This method reduces inference time by more than 30% on images that image size is larger than 1024 pixels by 1024 pixels compared to the mainstream instance segmentation method while maintaining reasonable accuracy without using any external data. © 2023 SPIE.

关键词： Cells

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A modified LMS-Newton algorithm for multichannel active noise control

A modified LMS-Newton algorithm for multichannel active nois...

引用

Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Lei Wang Fenghua Tian Ruilei Ma Wei Liu The 705 Research Institute of China State Shipbuilding Corporation Limited Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

This research project is concerned with enhancing the efficacy of active noise control (ANC) systems, with a particular emphasis on the optimisation of the LMS-Newton algorithm. We introduce a novel method based on the unbiased estimation of the autocorrelation matrix and the dynamic adjustment of step parameters, which collectively enable the innovative development of a new form of the LMS-Newton algorithm. The performance of the algorithm was evaluated through simulation tests conducted under different noise conditions. The results demonstrate that, in comparison to the traditional LMS-Newton algorithm, the new algorithm has exhibited a notable enhancement in convergence speed and noise reduction capability. This research outcome paves the way for a new phase of advancement in ANC technology and has the potential to significantly improve the noise control level in related application fields.

关键词： Heuristic algorithms Simulation Noise Noise reduction Signal processing algorithms Estimation Control systems Autocorrelation Optimization Convergence

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Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera 6

Dynamic Scene Representation for Docking in Urban Waters Usi...

引用

International Maritime and Port Technology and Development conference (MTEC) / 6th International conference on Maritime Autonomous Surface Ships (ICMASS)

作者： Nygard, Trym Anthonsen Brekke, Edmund Forland Mester, Rudolf Stahl, Annette Norwegian Univ Sci & Technol NTNU Dept Engn Cybernet Trondheim Norway Norwegian Univ Sci & Technol NTNU Dept Comp Sci Trondheim Norway

To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird's eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

关键词： Stereo image processing

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Central Perturbation-based Interval Type-2 Fuzzy C-Means Clustering for image Segmentation 4

Central Perturbation-based Interval Type-2 Fuzzy C-Means Clu...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Rong, Lan Na, Qu Tianlong, Hu Feng, Zhao Haiyan, Yu Lu, Zhang University of Posts and Telecommunications School of Communications and Information Engineering Xi'an Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In fuzzy c-means (FCM) clustering algorithm, when assigning membership degree to a data point, the calculation of fuzzy membership degree contains various uncertain factors, such as fuzzifier, distance metric and cluster center, etc. Although the interval type-2 FCM (IT2FCM) clustering algorithm and its improved algorithms consider the uncertainty about the fuzzifier, it suffers from falling into the local optimum because of ignoring the uncertainty of the cluster center. To address the issue, a central perturbation-based interval type-2 fuzzy c-means (CPIT2FCM) clustering algorithm for image segmentation is proposed by representing the uncertainty of the cluster center in this paper. In the proposed algorithm, the traditional crisp center is extended to the interval fuzzy center utilizing central perturbation, and the upper and lower membership functions are constructed by the interval fuzzy center, thus the type-l fuzzy set is extended to the interval type-2 fuzzy set. At the same time, the Mahalanobis distance is introduced into the CPIT2FCM algorithm to improve the clustering performance for the datasets and the color image. Finally, experiments are conducted on synthetic datasets and real images respectively, and the effectiveness of the proposed algorithm was verified by combining visual effects and evaluation indices. © 2022 IEEE.

关键词： image segmentation

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Spectral-and Contrast-Aware Low-Light image Enhancement for Robust Autonomous Driving

Spectral-and Contrast-Aware Low-Light Image Enhancement for ...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Ming Liu Zimo Wang xintong Li Wanting Feng School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China International Business School Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In low-light conditions, autonomous driving perception systems suffer feature loss, which results in deteriorating image quality and leads to the failure of downstream tasks like object detection and semantic segmentation. However, due to the sensor spectral bias and spatial illumination variation, the previous deep learning-based low-light enhancement algorithms result in the problems such as color maladjustment and uneven contrast. To tackle these challenges, we propose a novel enhancement framework which integrates the spectral compensation for color correction (SC-CCM) and region-adaptive contrast enhancement (RA-CE) modules. The SC-CCM module estimates spectral attenuation and corrects for it in RGB channels, thus reducing color distortions introduced by non-uniform sensor responses. In contrast, the RA-CE method dynamically tunes the contrast locally through learned illumination segmentation, enabling a consistent enhancement across each region. The model is based on an adapted Transformer architecture, and our method achieves currently the new-record performance on standard benchmarks as well as robustness on real-world driving conditions for the perceptual quality.

关键词： image quality Adaptation models Visualization image color analysis Lighting Distortion Transformers Robustness Autonomous vehicles image enhancement

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Development of UAV image processing algorithms for Early Detection of Fires in Natural Ecosystems

Development of UAV Image Processing Algorithms for Early Det...

引用

IEEE International conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Volodymyr Hnatushenko Tetiana Bulana Bohdan Molodets Daniil Boldyriev Information Technologies & Computer Engineering Department Dnipro University of Technology Dnipro Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

The article discusses image processing algorithms for creating a full-fledged fire detection system based on UAV collections. The wildfire detection issue is solved using artificial intelligence: YOLOv8 model. The fire classification based on the fuel map obtained using LiDAR data was also implemented. The scheme of the information system that will perform detection and classification is proposed. This information system is useful at the decision-making stage to determine the strategy and tactics of firefighting. With several improvements, the system can be used to process incoming data in real time.

关键词： Wildfires Laser radar image processing Ecosystems Autonomous aerial vehicles Real-time systems Classification algorithms Fuels Monitoring Information systems

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Deep Learning based Underwater Acoustic OFDM Receiver with Joint Channel Estimation and Signal Detection

Deep Learning based Underwater Acoustic OFDM Receiver with J...

引用

2022 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2022

作者： Zhang, Yuzhi Chang, Jiazheng Liu, Yang Shen, xiaohong Bai, Weigang Xi'an University of Science and Technology School of Communication and Information Engineering Xi'an China Northwestern Polytechnical University School of Marine Science and Technology Xi'an China Xidian University State Key Laboratory of Integrated Service Networks Xi'an China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

This paper presents a deep learning (DL) based underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) receiver, which exploits both DL and expert knowledge of block-based signal processing methods in wireless communication to improve system performance and interpretability. The proposed receiver jointly employs skip connection (SC) convolutional neural network (CNN) for channel estimation and bi-directional long short-term memory (BiLSTM) network for signal detection, abbreviated as SCBNet. Specifically, the channel estimation subnet is designed with SC CNN to utilize the thought of image super-resolution to reconstruct the entire channel frequency response of all subcarriers. The signal detection subnet is designed with BiLSTM to extract the correlations of received sequential data for signal detection. The proposed SCBNet was evaluated by experimental data, and the results have demonstrated that the SCBNet has the lowest BER performance compared with traditional linear algorithms, DL-based black-box receiver, and ComNet receiver. © 2022 IEEE.

关键词： Signal detection

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Automated Pneumonia Detection from Chest X-rays using Convolutional Neural Networks: Advancements, Optimization, and Performance Analysis 2

Automated Pneumonia Detection from Chest X-rays using Convol...

引用

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

作者： Bhuria, Ruchika Gill, Kanwarpartap Singh Upadhyay, Deepak Devliyal, Swati Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura Punjab India Graph Era Hill Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Graph Era Deemed Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India

ISBN: (纸本)9798350391558;9798350379990

Pneumonia, an almost fatal bacterial disease generally affecting one or both lungs and brought on by the bacterium Streptococcus pneumonia, can strike any human. The World Health Organisation (WHO) reports that pneumonia accounts for one in three fatalities in India. Chest X-rays are used to detect pneumonia, require the examination of qualified radiotherapists. Establishing an automated approach to identify pneumonia would therefore be advantageous to treat the illness as soon as feasible, particularly in distant places. Convolutional Neural Networks (CNNs) have attracted a lot of interest for disease diagnosis because of the effectiveness of deep learning algorithms in evaluating medical imagery. Higher accuracy, scalability, and automation opportunities are only a few of the benefits of the suggested CNN-based approach over conventional methods. This study proposes to use deep learning methods to find intricate patterns and characteristics from medical imaging data that are suggestive of pneumonia, hence enhancing diagnosis accuracy. Additionally, picture categorization jobs benefit much from features obtained from big datasets using pre-trained CNN models. This study assesses the capability of these models in this work to diagnose abnormal and normal chest X-rays using pre-trained CNN models as feature extractors and various classifiers subsequently. There is an analytical identification of the optimal CNN model for the task.

关键词： Machine Learning image processing CNN model Classifier Evaluation Artificial Intelligence pneumonia Techniques Diseases Population Resources Medical Health

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis

Research on Traditional Art Image Reconstruction Method Base...

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Artificial Intelligence and Autonomous Robot systems (AIARS), International conference on

作者： Xunmiao Ruan Guangdong University of Science &Technology Dongguan Guangdong China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the information age, image processing technology has become prevalent across various domains. To enhance image correction, computer vision algorithms can be employed. Traditional methods for structural system identification rely on wired or wireless measurement systems for data gathering, which can be cumbersome and time-consuming due to the need for arranging sensors and data collection equipment. The proposed system involves two primary steps: line drawing extraction and rendering. During the extraction phase, an edge detection technique that integrates low and middle-level visual information is utilized. Based on the results of traditional edge detection, the edges are simplified and connected using computer vision principles. This approach treats image processing as the focal point and aims to establish a scientific model for distorted images. image restoration can be facilitated through sample image filtering and noise reduction techniques. Additionally, this article delves into the principles and associated technologies of computer vision, analyzing patterns of visual interaction. Chinese traditional art imagery, as a cultural medium and symbol, fulfills an artistic communication function while preserving the nation's rich cultural heritage. The image processing techniques grounded in computer vision algorithms offer numerous advantages and superior accuracy.

关键词： Computer vision Visualization Art Machine learning algorithms image edge detection Distortion Real-time systems Global communication Cultural differences image reconstruction

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