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检索条件"任意字段=Conference on Image Quality and System Performance IX"

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Tomographic Object Detection by Unsupervised Clustering 2

Tomographic Object Detection by Unsupervised Clustering

引用

2nd International conference on Computer Vision and Intelligent Technology

作者： Wang, Fengyuan Wang, Zheng Wu, Yimin Jiang, Yandan Ji, Haifeng Wang, Baoliang Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou Peoples R China

ISBN: (纸本)9798331540050;9798331540043

This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back projection (LBP) algorithm is applied to reconstruct a rough image. Then, clustering is introduced to detect and refine the objects in the image, and a higher-quality image is obtained based on the clustering results. Six unsupervised clustering algorithms belonging to different categories, including K-means, Mini Batch K-means, Agglomerative, density peak clustering (DPC), statistical information grid (STING), gaussian mixture model (GMM), are compared from the aspects of clustering evaluation index, reconstruction quality and reconstruction efficiency. Simulation was carried out based on a capacitively coupled electrical resistance tomography (CCERT) system to collect the projection data under the cases of one to three objects to be detected in the sensing region. The research results show that the quality of object detection and reconstruction can be effectively improved by post-processing the image with clustering. It is found that for the single-object distribution, the grid-based clustering algorithm STING provides images with the highest quality. While for the multi-objects distributions (two or three objects), the Agglomerative algorithm belonging to the hierarchy clustering category shows advantage in achieving good images. Concerning the real-time performance, Agglomerative clustering algorithm is also preferred with less computational time than most of the other clustering algorithms. Therefore, among the combinations investigated in this work, LBP + Agglomerative clustering has the overall best performance in topographic object detection, in regarding of the reconstruction quality and efficiency.

关键词： electrical tomography (ET) image reconstruction clustering object detection image post-processing

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Finger Vein image quality Assessment Based on Stochastic Embedding Robustness 42

Finger Vein Image Quality Assessment Based on Stochastic Emb...

引用

42nd Chinese Control conference, CCC 2023

作者： Yang, Chen Guo, Jian Zhang, Yibo Huang, Yanan Guo, Zijie School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China

ISBN: (纸本)9789887581543

Finger vein image quality assessment is used to evaluate the quality of images, namely to evaluate the applicability of the finger vein images to the recognition system. The work of quality assessment will largely affects the performance of the finger vein recognition system. In previous work, people usually design quality indicators manually, construct quality functions or perform supervised learning based on visual inspection, which has limitations of subjectivity. In order to solve these problems, this paper proposes a finger vein image quality assessment algorithm based on Stochastic Embedding Robustness (SER), which is called SER algorithm in this paper. The algorithm generates random subnetworks from the deployed finger vein recognition system, and indirectly evaluates the image quality by calculating the variation of different subnetworks. The simulation results show that, SER algorithm can effectively filter out low-quality images and improve the performance of the recognition system. Compared with other methods, it is more efficient and stable. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： image quality

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Research on image Signal Enhancement and Denoising Algorithms for Intelligent Communication systems

Research on Image Signal Enhancement and Denoising Algorithm...

引用

2024 International conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Luo, Shenghan Wang, Xinyan Southeast University Nanjing211189 China Xuzhou Pharmaceutical Vocational College Xuzhou221116 China

ISBN: (纸本)9798331542696

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid \mathrm{C}++ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems. © 2024 IEEE.

关键词： C++ (programming language)

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image Classification of Corn Seeds Using Ensemble-Based CNN

Image Classification of Corn Seeds Using Ensemble-Based CNN

引用

2024 International conference on system, Computation, Automation and Networking, ICSCAN 2024

作者： Raja, G. Salini, P. Puducherry Techynological University Department of Computer Science and Enngineering Puducherry India

ISBN: (纸本)9798331510022

As corn has become staple food for people in many countries around the world, that directly increases its production. The versatile nature of maize plant is the reason for its highest cultivation among all other staple foods. As many food industries demand for good quality of corn seeds for usage in their own products. The quality of corn seeds depends on the various factors like type, color, condition, texture, size, etc. The image classification technique using Convolutional Neural Networks shown high accuracy in the selection of good quality of corn seeds. But on the other hand, for image classification of tons of corn seeds, CNN has to extract large number of features. Therefore, this paper proposes the image classification of corn seed using the CNN and ensemble learning technique. The proposed work shown good performance compared to the conventional CNN methods. © 2024 IEEE.

关键词： CNN corn kernel corn seed ensemble learning image Classification

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Sliced Maximal Information Coefficient: A Training-Free Approach for image quality Assessment Enhancement

Sliced Maximal Information Coefficient: A Training-Free Appr...

引用

IEEE International conference on Multimedia and Expo (ICME)

作者： Xiao, Kang Wang, Xu He, Yulin Chen, Baoliang Shen, Xuelin Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference. However, existing FR-IQA models including both the classical ones (e.g., PSNR and SSIM) and deep-learning based measures (e.g., LPIPS and DISTS) still exhibit limitations in capturing the full perception characteristics of the human visual system (HVS). In this paper, instead of designing a new FR-IQA measure, we aim to explore a generalized human visual attention estimation strategy to mimic the process of human quality rating and enhance existing IQA models. In particular, we model human attention generation by measuring the statistical dependency between the degraded image and the reference image. The dependency is captured in a training-free manner by our proposed sliced maximal information coefficient and exhibits surprising generalization in different IQA measures. Experimental results verify the performance of existing IQA models can be consistently improved when our attention module is incorporated. The source code is available at https://***/KANGX99/SMIC.

关键词： Full-reference image quality assessment visual attention maximal information coefficient statistical dependency

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Fed-UIQA: Federated Learning for Unsupervised Finger Vein image quality Assessment 20th

Fed-UIQA: Federated Learning for Unsupervised Finger Vein Im...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Liu, Xingli Guo, Jian Mu, Hengyu Gong, Lejun Han, Chong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China

ISBN: (纸本)9789819755936;9789819755943

Finger vein recognition, an emerging biometric technique, has been widely applied across various domains. image quality significantly impacts vein recognition system performance, prompting considerable attention to finger vein image quality assessment. However, most existing methods for assessing finger vein image quality are limited to evaluating image quality levels and lack robustness and practicality. Furthermore, existing finger vein image datasets used for model training not only lack quality labels but also exhibit significant heterogeneity. Moreover, privacy concerns often constrain their creation and usage, further complicating image quality assessment. To address these challenges, this paper introduces a federated learning for unsupervised finger vein image quality assessment (Fed-UIQA). This method computes image spatial distances and relative classifiability to obtain image quality scores, enabling unsupervised quality assessment. Local incremental models are designed on the client-side to address heterogeneous vein datasets to enhance the global model aggregated on the server, thereby improving its adaptability to local data. Finally, extensive experiments conducted on five datasets validate the superiority of the proposed approach.

关键词： Finger Vein image quality Assessment Unsupervised Learning Federated Learning Biometrics

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Preference Contrastive Learning for Personalized Recommendation 6th

Preference Contrastive Learning for Personalized Recommendat...

引用

6th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Bai, Yulong Jian, Meng Li, Shuyi Wu, Lifang Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9789819985456;9789819985463

Recommender systems play a crucial role in providing personalized services but face significant challenges from data sparsity and long-tail bias. Researchers have sought to address these issues using self-supervised contrastive learning. Current contrastive learning primarily relies on self-supervised signals to enhance embedding quality. Despite performance improvement, task-independent contrastive learning contributes limited to the recommendation task. In an effort to adapt contrastive learning to the task, we propose a preference contrastive learning (PCL) model by contrasting preferences of user-items pairs to model users' interests, instead of the self-supervised user-user/item-item discrimination. The supervised contrastive manner works in a single view of the interaction graph and does not require additional data augmentation and multi-view contrasting anymore. performance on public datasets shows that the proposed PCL outperforms the state-of-the-art models, demonstrating that preference contrast betters self-supervised contrast for personalized recommendation.

关键词： Recommender system Contrastive Learning Interest Propagation Graph Convolution

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PERCEPTUAL LEARNED image COMPRESSION VIA END-TO-END JND-BASED OPTIMIZATION 31

PERCEPTUAL LEARNED IMAGE COMPRESSION VIA END-TO-END JND-BASE...

引用

2024 International conference on image Processing

作者： Pakdaman, Farhad Nami, Sanaz Gabbouj, Moncef Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland

ISBN: (纸本)9798350349405;9798350349399

Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of this approach over traditional codecs is that any optimization criteria can be directly applied to the encoder-decoder networks during training. Perceptual optimization of LC to comply with the Human Visual system (HVS) is among such criteria, which has not been fully explored yet. This paper addresses this gap by proposing a novel framework to integrate Just Noticeable Distortion (JND) principles into LC. Leveraging existing JND datasets, three perceptual optimization methods are proposed to integrate JND into the LC training process: (1) Pixel-Wise JND Loss (PWL) prioritizes pixel-by-pixel fidelity in reproducing JND characteristics, (2) image-Wise JND Loss (IWL) emphasizes on overall imperceptible degradation levels, and (3) Feature-Wise JND Loss (FWL) aligns the reconstructed image features with perceptually significant features. Experimental evaluations demonstrate the effectiveness of JND integration, highlighting improvements in rate-distortion performance and visual quality, compared to baseline methods. The proposed methods add no extra complexity after training.

关键词： Just Noticeable Distortion (JND) Human Visual system ( HVS) learned compression perceptual optimization

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Analysis of Marker Factors Effect on the Detection Process of image-Based Tracking in Web Augmented Reality 16

Analysis of Marker Factors Effect on the Detection Process o...

引用

16th International conference on Information Technology and Electrical Engineering, ICITEE 2024

作者： Febrihartanti, Vallentina Wahyu Hantono, Bimo Sunarfri Nasikun, Ahmad Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350375817

image-based tracking is one of the augmented reality methods used in the marker detection and tracking process. This method uses an image marker to determine the precise location and orientation of the camera for displaying virtual objects. image marker has an important role in the image-based tracking augmented reality working process. Thus, to generate and utilize image markers, factors that functionally affect the performance of image-based tracking augmented reality are required. With the interest to uncover what these factors are and how they affect the performance of the image-based tracking system, this research will examine the factors applied to the image marker and their effect on the performance of image-based tracking web AR in bringing virtual objects to the real environment. In this research, six factors were tested, consisting of the image marker quality, surface area of the covered image marker, camera distance to the image marker, the camera angle towards the image marker, the Light-Marker-Camera (LMC) angle, and the type of device used. The effects of these factors are measured using the latency of image-based tracking augmented reality in displaying virtual objects. Therefore, it is concluded that the LMC angle factor significantly affects the latency of image-based tracking AR. Also, the factors of the surface area of the covered image marker, the distance between the camera and the image marker, and the camera angle towards the image marker show a positive influence on the latency of the system. On the other hand, the factors of image marker quality, and the type of device used prove a negative influence with the latency of image-based tracking augmented reality in displaying virtual objects. © 2024 IEEE.

关键词： Virtual environments

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Confidence-Driven Adaptive Optimal Impedance Control Base on Ultrasound Robot system 11

Confidence-Driven Adaptive Optimal Impedance Control Base on...

引用

11th IEEE International conference on Cybernetics and Intelligent systems (CIS) / 11th IEEE International conference on Robotics, Automation and Mechatronics (RAM)

作者： Lia, Jinkun Zhang, Li Zheng, Wenxu Huang, Haohui Guo, Jing Chen, Shouyan Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Agr Dev Ctr Kaizhou Dist Chongqing Peoples R China

ISBN: (纸本)9798350364200;9798350364194

The ultrasound robotic system (URS) is of tremendous importance for assisting sonographers to diagnose various diseases. Generally, the quality of ultrasound image which is evaluated through confidence map has a high dependency on the experience of sonographers. In order to obtain high-quality ultrasound image under URS, we take confidence map into consideration of control frame and propose confidence-driven adaptive optimal impedance learning scheme. By introducing an appropriate performance function which blends both confidence value and contacting force, an iterative learning strategy is conducted to achieve favorable scanning performance. Finally, we show the experiment results on the human volunteer.

关键词： Ultrasound robot confidence map impedance control

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