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

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Automatic Segmentation of Rectal Tumor on Magnetic Resonance images via a Deep Discriminative Model Consisting of Asymmetric U-Net and Convolutional Conditional Random Field based Post-processing 19

Automatic Segmentation of Rectal Tumor on Magnetic Resonance...

引用

19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Xing Chen, Hao Ge, Sisi Xi'an University of Posts and Telecommunications School of Computer Science Xi'an710121 China Xi'an University of Posts and Telecommunications School of Humanities and Foreign Languages Xi'an710121 China

ISBN: (纸本)9798350304398

The process of manual delineating is frequently time-consuming and can result in low consistency. Our goal was to design a deep discriminative model (DDM) to mitigate these issues of magnetic resonance imaging (MRI) for rectal tumor lesion boundary delineation, and we proposed an asymmetric U-Net rectal tumor MR images segmentation algorithm based on multi-scale dilated convolutional inputs and convolutional conditional random fields (ConvCRFs). A multiscale convolutional input module was designed as a preprocessing step to enrich the extraction and input of global contextual semantic information;then, an asymmetric U-Net network combined with ConvCRFs was used to discriminatively fine-tune the segmentation results so as to enhance segmentation accuracy of tumors. To verify the feasibility of the algorithm, the experiments were conducted on a private dataset provided by a medical institution, and the experimental Dice coefficient reached 0.876 on T2WI images, 0.851 on DWI images, respectively, indicating the existence of an important clinical guidance value for rectal tumor image segmentation algorithms. © 2023 IEEE.

关键词： Tumors

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Robust Calibration for Enhanced Imaging in Near-Field D-Band MIMO Radar systems

Robust Calibration for Enhanced Imaging in Near-Field D-Band...

引用

European conference on Antennas and Propagation, EuCAP

作者： Lorena Perez-Eijo Marcos Arias Borja Gonzalez-Valdes José Vázquez-Cabo Ignacio Sardinero–Meirás Jesus Grajal AtlanTTic Research Center Universidade de Vigo Vigo Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work introduces a calibration algorithm designed to improve high-precision imaging in real-time MIMO radar systems. The algorithm applies the Inverse Fourier Transform (IFFT) and advanced signal processing techniques to efficiently extract and process key data from raw measurements, focusing on critical Regions Of Interest (ROI) while filtering out unnecessary information. A robust phase correction mechanism ensures precise calibration and alignment, preventing phase errors that could otherwise lower image quality. The algorithm provides an effective balance between computational efficiency and the high accuracy required by modern MIMO radars, paving the way for further improvements in real-time imaging.

关键词： MIMO radar Signal processing algorithms Coherence Radar imaging Signal processing Real-time systems Hardware Calibration Spatial resolution image reconstruction

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Research on Efficient image Feature Extraction based on Low-Rank Representation for Intelligent Reading systems

Research on Efficient Image Feature Extraction based on Low-...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Yulan Tan Heilongjiang University of Technology Jixi China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Efficient and accurate extraction of image features is essential for ensuring high-performance information transmission in intelligent reading systems. This paper introduces a computer-assisted method for intelligent image feature extraction, specifically focusing on the Low-Rank Representation (LRR) approach. The study addresses challenges presented by noisy and diverse character or image data encountered in real-world scenarios. By employing LRR, the accuracy of image extraction, which includes object recognition and scene understanding, is significantly enhanced. Subsequently, the proposed techniques for intelligent image feature extraction undergo comprehensive evaluation, assessing both effectiveness and versatility through extensive experiment verifications. The experimental results validate the efficacy of the proposed method and demonstrate its superior image content extraction accuracy compared to existing methods. This proposed method provides crucial assistance to high-performance intelligent reading systems, thereby enhancing its applicability for potential consumers.

关键词： YOLO Visualization Accuracy Process control Feature extraction Rendering (computer graphics) Classification algorithms Data mining Proposals Principal component analysis

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A Cooperative Gene Regulatory Network Target Entrapping in GNSS-denied Environments 12

A Cooperative Gene Regulatory Network Target Entrapping in G...

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12th International conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Shi, Ze Fan, Zhun Ma, Peili Hong, Juncao Li, Wenji Zhu, Guijie Huang, Huaxing Cai, Yuwei Dong, Zhaohui Zhu, Xiaomin Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou515063 China College of Systems Engineering National University of Defense Technology China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In recent years, the use of gene regulatory network (GRN) to guide swarm robots for target-entrapping tasks has received increasing attention. Even though many GRN-based algorithms for swarm aggregation morphogenesis generation are designed and verified in two-dimensional space, they are not suit-able for target trapping in three-dimensional space with GNSS-denied environments. Therefore, we propose a cooperative GRN (Co-GRN), which can guide swarm robots to complete target-entrapping tasks in GNSS-denied three-dimensional environments. The average distance and enveloping strength are analysed in several simulated scenes, and the effectiveness and flexibility of the proposed method are verified. © 2022 IEEE.

关键词： Genes

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Laplacian of Gaussian Based on Color Constancy Algorithm for Surrounding image Stitching Application

Laplacian of Gaussian Based on Color Constancy Algorithm for...

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IEEE International conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Lee, Tsung-Han Chou, He-Sheng Chen, Tsung-Yi Lo, Wen-Shen Zhang, Jun-Ting Chen, Chiung-An Lin, Ting-Lan Chen, Shih-Lun Chung Yuan Christian Univ Dept Elect Engn Taoyuan Taiwan Ming Chi Univ Technol Dept Elect Engn New Taipei Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper presents a new algorithm for Laplacian of Gaussian (LoG) image processing systems. In order to improve nowadays digital capture system and digital display calibration. Color constancy is added to improve the measurement of object color based on independent light sources. To satisfy the human eye's recognition of colors, the angular error must be corrected to make the light source reflect naturally in the image. In addition, the hardware design of classic white balance algorithms such as Gray-World, Max-RGB, and Gray-Edge with the Minkowski norms is implemented in this study. The proposed algorithm is simulating with a Gaussian filter and an additional Laplacian of Gaussian (LoG) to generate real-time masks that automatically brighten images. Compared with several previous studies, the proposed algorithm shows lower angular errors while maintaining the quality of the graphics. The motivation is to make better quality watches on display facilities and better capture systems.

关键词： White Balance Color Constancy Gaussian filter Laplacian of Gaussian(LoG) Gray-World Max-RGB Gray-Edge Minkowski norm Angular error

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Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Enhanced K-means Color Clustering Based on SLIC Superpixels ...

引用

IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Haleema Sadia Parvez Alam School of Engineering The University of Edinburgh Edinburgh Scotland UK

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Superpixel-based segmentation is an important pre-processing step for the simplification of image processing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either underor over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.

关键词： image segmentation image color analysis Insects Merging Clustering algorithms Software Hybrid power systems

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image Quality Assessment for Computer Vision Based Perception algorithms Using Edge and Structural Features 38

Image Quality Assessment for Computer Vision Based Perceptio...

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38th IEEE Region 10 conference, TENCON 2023

作者： Akshaya, V.G. Rithika, S. Lanciya Angel, A. Amirtha Pravin, K.S.M. Balasubramanian, Karthi Srinivasan, Suganthi Rajegowda, Rakesh Bhattacherjee, Payal Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India R & D-Computer Vision Systems Valeo India Pvt.ltd Comfort Driving Assistance Group Chennai India

ISBN: (纸本)9798350302196

image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc

关键词： image quality

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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Glaucoma Stage Classification Using image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus images 4th

Glaucoma Stage Classification Using Image Empirical Mode Dec...

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4th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Shankar, D. Harsha, I. Sri Madhuri, P. Shyamala Naidu, J. N. S. S. Janardhana Madhuri, P. Krishna Cherukuvada, Srikanth Department of Computer Science and Engineering Vishnu Institute of Technology Kovvada India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a Computer-Aided Diagnosis (CAD) method is used called image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computer aided diagnosis

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Research on Graph Neural Network Algorithm and image Recognition Application 20

Research on Graph Neural Network Algorithm and Image Recogni...

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20th International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Xu, Jia-Hao Li, Jian-Ping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

The emergence of graph neural networks makes up for the limitations of traditional neural networks in dealing with complex graph data, and they are widely used in the fields of social networks and recommender systems, but at the same time, there are some defects in themselves. This paper introduces the basics of graph neural networks, and also summarizes the advantages and disadvantages of GSN, GraphSAGE, DeepWalk and Node2Vec algorithms. Finally, it also discusses the application of graph neural networks in the field of image recognition, summarizes its deficiencies in practical applications, and puts forward the prospect of the future development of graph neural networks. © 2023 IEEE.

关键词： image recognition

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