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

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Sound Field Control in Surround Screen Speaker Array by COSH and SINC algorithms

Sound Field Control in Surround Screen Speaker Array by COSH...

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International conference on image, Video processing, and Artificial Intelligence

作者： Huan, Rui Zhao, xi Commun Univ China 1 Dingfuzhuang East St Beijing 100024 Peoples R China

ISBN: (数字)9781510650282

ISBN: (纸本)9781510650282;9781510650275

This paper proposes a method to solve the problem that the sound reproduction system cannot work when the movie screen is made by a sound-proof material such as LED. It is demonstrated in an array of 192 speakers to surround a screen for sound reproduction, called surround screen speaker array. The speaker array is built in an actual cinema. The sound field control algorithms are implemented by mixers. In order to improve the uneven sound field distribution and sound field aliasing caused by the speaker array, two algorithms COSH and SINC are used in this paper. A new control algorithm is proposed and demonstrated to improve the uniformity of the sound field distribution and reduce the sound field interference.

关键词： Surround Screen Speaker Array Sound Field Control COSH SINC

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Detection of Leaf Diseases Based on Ensemble Convolutional Neural Networks 4

Detection of Leaf Diseases Based on Ensemble Convolutional N...

引用

4th IEEE International conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Chithra, P.L. Pushparani, S. Janes University of Madras Department of Computer Science Tamil Nadu Chennai India Ethiraj College for Women Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350306118

The appearance of infections in plantations is the most common cause of crop losses in a variety of crops. Due to lower product quality and a narrower offering of items, these usually result in reduced income for farmers and higher pricing for consumers. Infection symptoms are mostly evident in plant foliage, hence they are widely used to detect and identify diseases. Combined with computational intelligence or soft computing approaches, image-processing tools can help farmers diagnose diseases more effectively. The disease in plants can be identified using traits gleaned from its symptoms. A strategy using an ensemble approach for providing more precise predictions by merging results through different machine learning algorithms. A group of models cooperating to address a shared issue. Ensemble learning uses the strength of a variety of techniques to overcome the particular weaknesses of each model, as opposed to depending just on one model to provide the optimal answer. The set ought to be less error-prone than a single model. Combining many iterations of a projected model is the process of bagging. Before being used, each model is independently trained using an average model. Bagging's objective is to reduce variance below that of any model. In this work, images of leaves comprising healthy class and diseases obtained from the Plant Village dataset are fed into three Convolutional Neural Network architectures namely LeNet, Inception, and VGG16 net achieve an accuracy of 95%, 92%, and 95% respectively and the ensemble model obtains an improvement of 3.5% matched to the standard method. © 2023 IEEE.

关键词： image enhancement

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Design and Implementation of VGA image Transmission Based on FPGA

Design and Implementation of VGA Image Transmission Based on...

引用

Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Weijun Hao School of Cultural Tourism Chongqing Vocational College of Culture and Arts Chongqing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

With the increase of image data scale, a faster and more efficient image acquisition and processing system is needed. FPGA has good parallel performance, which is very suitable for algorithms dealing with basic parallel features, and can greatly improve the efficiency of image acquisition and processing system. Aiming at the problem of FPGA image acquisition, processing and display, this paper carries out the design of real-time image acquisition system, which has the function of real-time display after processing the acquired image. In this paper, the chip of EP4CE6F17C8 of Cyclone IV series is used, the CMOS camera is OV5640 and the monitor is VAG. After full experimental verification, the system is extensible, the later expansion goal can not be achieved without the cooperation of Bluetooth and other facilities, with the role of recognizing and positioning license plates, obviously this series of application prospects are considerable; With flexibility, can customize the algorithm for optimization; Low cost a series of advantages.

关键词： Bluetooth Robot vision systems Streaming media Cameras Real-time systems Field programmable gate arrays License plate recognition System analysis and design Robots Optimization

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Infrared and Visible image Fusion with Hierarchical Human Perception

Infrared and Visible Image Fusion with Hierarchical Human Pe...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guang Yang Jie Li xin Liu Zhusi Zhong xinbo Gao School of Electronic Engineering Xidian University Xi’an China Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large Vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large Vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement Our code is available at https://***/SSyangguangZHPFusion.

关键词： Visualization Codes Semantics Visual systems Signal processing Acoustics Speech processing image fusion Optimization Standards

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Low-Dose CT image Reconstruction Based on a Residual Network

Low-Dose CT Image Reconstruction Based on a Residual Network

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IEEE Information Technology, Networking, Electronic and Automation Control conference

作者： Jing Li xiaoyan Huang School of Electronic Engineering Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Computed Tomography (CT) scanning is a common medical imaging examination method, but high doses of X-ray radiation may cause radiation damage to patients. Low-dose CT technology aims to reduce the radiation dose received by patients, reducing patient risk while maintaining image quality. However, the quality of low-dose CT images is often poor, with potential issues such as noise and artifacts. Using residual networks for image reconstruction is an effective deep learning method. By learning the residual information of images, residual networks can efficiently generate high-quality reconstructed images. This article describes the application of residual networks in the field of low-dose CT image reconstruction and summarizes their advantages and challenges. Research indicates that residual networks can reduce information loss, preserve the details of the original images, and have good universality and adaptability. Future research can further explore deeper network structures, integrate attention mechanisms, to promote the application of residual networks in low-dose CT image reconstruction.

关键词： Learning systems Attention mechanisms Computed tomography System performance Noise Reconstruction algorithms image reconstruction X-ray imaging Residual neural networks Biomedical imaging

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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Data-driven Optimization of Complex systems (DOCS), International conference on

作者： Zhijie Zhang Huang Bai Ljubiša Stanković Junmei Sun xiumei Li School of Information Science and Technology Hangzhou Normal University Hangzhou China Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression pro-cedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iter-ative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission- augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through exper-iments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Redundancy Signal processing algorithms Signal processing Reconstruction algorithms Propagation losses Sensors Reliability image reconstruction Compressed sensing Optimization

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DDM-YOLOv8s for Small Object Detection in Remote Sensing images

DDM-YOLOv8s for Small Object Detection in Remote Sensing Ima...

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Machine Learning and Natural Language processing (MLNLP), International conference on

作者： Yangli xi Dan Qu Liming Du Dept. School of Cyberspace Security Zhengzhou University Zhengzhou Henan China Dept. School of Information Systems Engineering Information Engineering University Zhengzhou Henan China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In order to solve the problem of low accuracy of remote sensing target detection caused by insufficient feature representation, complex background and diversified scales in remote sensing image target detection task, a remote sensing image target detection algorithm based on DDM-YOLOv8s (DySample DASI-MODSConvYOLOv8s) model was proposed. First, a super lightweight and efficient dynamic upper sampler DySample is used to improve the image resolution of the algorithm without adding additional burden. Secondly, the new C2f_DASI module is constructed by using the dimensional awareness selective integration module DASI and C2f, and the adaptive channel selection and fusion are realized in the feature fusion module, which effectively solves the complex background problem in remote sensing images; Finally, the head part is redesigned by Maintaining the Original information - Depth Separable Convolution MODSConv, which maintains as much feature layer information as possible before prediction, so that the extracted features contain more correlation information, greatly improving the detection accuracy and speed. The experimental results on RSOD datasets show that the test mAP of the improved YOLOv8s algorithm is 94.5%, which proves that this method can effectively improve the detection accuracy of small targets in remote sensing images.

关键词： Accuracy image resolution Correlation Convolution Heuristic algorithms Object detection Feature extraction Complexity theory Data mining Remote sensing

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Deep Learning Approach to Automated Tomato Plant Leaf Disease Diagnosis 2

Deep Learning Approach to Automated Tomato Plant Leaf Diseas...

引用

2nd International conference on Electronics and Renewable systems, ICEARS 2023

作者： Karthika, I. Megha, M. Roshni, M. M. Kumarasamy College of Engineering Dept. of Computer Science Engineering Tamilnadu Karur India

ISBN: (纸本)9798350346640

India's agriculture is primary occupation and it experiences a yearly loss of 35% in crop productivity due to plant-related issues. Advances in technology, such as precision agriculture and the use of Deep learning algorithms, are helping to make this possible. A number of practical and technological challenges, including dataset collecting and labelling, variability in plant leaf images, inter-class similarity, imbalanced datasets, generalization, and real-time detection, are prevalent in the challenging task of detecting plant leaf disease. The main goal of utilizing a convolutional neural network (CNN) for plant leaf disease detection is to precisely categorize images of plant leaves into categories of healthy or diseased. Depending on the particular application scenario, there could be additional goals that researchers try to accomplish with their CNN models in addition to this main one. Tomato is a widely cultivated vegetable and is an important food crop worldwide. Employing machine learning and image processing methods for detecting diseases in tomato plant leaves can be highly efficient. These techniques can be used to analyze images of tomato leaves taken in the field, and an accurate algorithm can be used to identify the presence of disease symptoms. One such approach is using Convolutional Neural Networks (CNNs) algorithm which have been trained on a dataset of images of tomato leaves with and without disease symptoms. This can be done by gathering the images of the healthy tomato leaves and the infected ones and then training a CNN model to detect the difference. The trained model can categorize new images of tomato leaves into healthy or infected, offering a quick and precise method to identify diseases in tomato plants' leaves. © 2023 IEEE.

关键词： image segmentation

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Normalized Multichannel Frequency-Domain LMS Filter With Nearest Kronecker Product Decomposition for Blind Identification of Low-Rank Acoustic systems

Normalized Multichannel Frequency-Domain LMS Filter With Nea...

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European Signal processing conference (EUSIPCO)

作者： Zhimin Qiu Hongsen He Jingdong Chen Jacob Benesty Yi Yu School of Information Engineering Southwest University of Science and Technology Mianyang China CIAIC and Shaanxi Provincial Key Laboratory of Artificial Intelligence Northwestern Polytechnical University Xi'an China INRS-EMT University of Quebec Montreal Canada

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper proposes a multichannel frequency-domain adaptive filtering algorithm to blindly identify low-rank acoustic systems. The model filters of the multichannel acoustic impulse responses are decomposed into two sets of short sub-filters through the nearest Kronecker product (NKP). An extended multichannel frequency-domain signal model and its associated cost function are established by using these short sub-filters. The normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm based on NKP is subsequently derived according to the Newton's iteration criterion. Simulations show that the proposed algorithm is computationally more efficient and has a better convergence behavior for blindly identifying multichannel acoustic systems than the conventional NMCFLMS adaptive algorithm, regardless of whether the excitation is a white sequence or a speech signal.

关键词： Adaptation models Adaptive systems Filters Frequency-domain analysis Signal processing algorithms Adaptive filters Filtering algorithms Acoustics Numerical models Speech processing

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Yaw and Pitch Steering Using a Nonlinear Least Square Approach for Spaceborne SAR systems

Yaw and Pitch Steering Using a Nonlinear Least Square Approa...

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International conference on Signal processing Proceedings (ICSP)

作者： Yongfei Ding Dinghai Xu Yu Li Zheng Fang Tao Chen Hengyang Zhang Key Laboratory on Avionics Integration Aviation Industry of China (AVIC) China Institute of Navigation and Information Airforce Engineering University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

In this paper, we investigate the attitude control algorithms for antenna steering of spaceborne Synthetic Aperture Radars (SAR). They are based on the method of Nonlinear Least Squares (NLS), which has the ability to fast calculate the attitude angles (yaw and pitch) of the satellite platform. Doppler centroid and Doppler slope versus range may both be set to zeros by satellite attitude control. Since the Least Mean Squares (LMS) type algorithm needs many iterations to converge, Newton’s method may be chosen to accelerate its performance. Computer simulations demonstrate the numerical precision and computational efficiency of the proposed algorithms. As a consequence, we may apply the NLS method to the SAR yaw steering issue with good results.

关键词： Satellites Attitude control Spaceborne radar Computer simulation Signal processing algorithms Signal processing Millimeter wave radar Doppler effect Synthetic aperture radar System analysis and design

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