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

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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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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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Design of detection optical system with large relative aperture 12

Design of detection optical system with large relative apert...

引用

conference on Optical Design and Testing xii

作者： Yang, Yingzhe Li, Yuejia Wang, Jia Bai, Jian Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

ISBN: (纸本)9781510656970;9781510656963

With the advancement of aerospace technology, space debris generated by the collision and shedding of failed spacecraft is increasing, which threatens the safe operation of spacecraft in orbit seriously, and arouses people's attention to the detection of space debris. To achieve high-performance detection in dark environments, we propose a large relative aperture space-based detection optical system, which has a field of view of 16 degrees, focal length of 182mm, F number of 1.52, and a working wavelength of 450 similar to 850nm. The system contains total 11 lenses, making the imaging performance more sensitive to temperature, therefore, we carry out thermal analysis for the system and use optical compensation method to achieve athermalized design. Finally, the system can work in the range of -10 degrees C similar to+30 degrees C, and the variation of the radius of the spot diagram with temperature is less than 3 mu m. In addition, we control the influence of stray light in the design process in order to observe bright and dark targets simultaneously. The numerical simulation results demonstrate that the veiling glare index of the system is 2.9%, which meets the requirement of dark object observation. The proposed optical system with large relative aperture and excellent imaging quality could be applied to accurate detection of space debris.

关键词： optical design detection system athermalized stray light analysis

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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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Semantic-Aided image Transmission system with Unequal Error Protection for Next-Generation Communication Networks 25

Semantic-Aided Image Transmission System with Unequal Error ...

引用

IEEE Wireless Communications and Networking conference (IEEE WCNC)

作者： Fayaz, Nargis Shreshtha, Aman Sarangi, Smruti Mallik, Ranjan K. Lall, Brejesh Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India Indian Inst Technol Delhi Dept Comp Sci & Engn New Delhi 110016 India

ISBN: (纸本)9798350303582;9798350303599

Semantic communication (SC) aims to convey the meaning of data instead of focusing on its bit-by-bit reconstruction. SC finds applications in beyond 5G and 6G networks for artificial intelligence-empowered multimedia content delivery. In this paper, we propose a novel semantic-aided autoencoder-based image transmission system that leverages semantic information in the form of the segmentation map of an image. We demonstrate up to 23% and 18% improvement (in terms of mean square error and peak signal-to-noise ratio, respectively) in the quality of the received image with only 2% extra bandwidth over a traditional autoencoder-based image transmission system. The study also explores channel coding strategies for our proposed system. We focus on the intrinsically robust nature of semantic data, as compared to traditional data, to design low-density parity check code, Hamming code, and polar code-based unequal error protection (UEP) schemes. Comparative evaluations between UEP and equal error protection schemes show that while both approaches yield similar performance, UEP schemes are more efficient.

关键词： Autoencoder image segmentation low-density parity check (LDPC) codes multimedia transmission system polar codes semantic communication (SC) unequal error protection (UEP)

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image quality Assessment 8

Image Quality Assessment

引用

8th IEEE International conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sri Haripriya Rai, N. Ranjani, G. Rv College of Engineering Bengaluru India

ISBN: (纸本)9798331505462

In an age where visual intelligence products are omnipresent, images hold a significant position in digital communication. image-based applications are ubiquitous in our daily lives. The implementation in this paper enhances the distorted image with the help of a reference image using the DISTS and ADISTS algorithms, evaluating IQA metrics, and comparing the performance of both models in image enhancement tasks. The results indicate that DISTS is particularly effective at improving traditional pixel-based metrics like PSNR which is 13.15% higher than ADISTS algorithm and aids in lowering the MSE by 17.86% more compared to ADISTS, making it ideal for tasks that demand high pixel-level accuracy. On the other hand, ADISTS shines in enhancing perceptual quality metrics such as SSIM, MS-SSIM, and VIFP, by 2.46%,0.52% and 16.7% respectively compared to DISTS algorithm indicating that it is more effectively captures structural details and enhances visual quality, for the sea anemone image which was executed for 2000 iterations for the both DISTS and ADISTS algorithms. © 2024 IEEE.

关键词： Visual analytics

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On edge FPN reduction in CMOS image Sensor using CNN with Attention Mechanism 19

On edge FPN reduction in CMOS Image Sensor using CNN with At...

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Asia Pacific conference on Circuits and systems (APCCAS)

作者： Kodam, Sandeep Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Indian Inst Technol Dept Elect Engn Jodhpur 342030 RJ India Indian Inst Technol Dept Comp Sci Engn Jodhpur 342030 RJ India

ISBN: (纸本)9798350381191

Obtaining a good quality image from a CMOS image Sensor (CIS) is always a constraint due to the effect of noise present within the image sensor system. One of the dominant source of noise in CIS with column-parallel readout is Fixed Pattern Noise (FPN) which significantly degrade the image quality. This work implements an architecture for the reduction of vertical FPN called Fixed Pattern Noise Reduction Network (FPNrNet), which uses a Convolutional Neural Network (CNN) with an attention mechanism. The denoising performance of the FPNrNet model is quite similar to that of standard denoising models;however, a significant reduction in model size is observed due to a reduction in the number of parameters. An average Peak Signal-to- Noise Ratio (PSNR) improvement of around 11.3 dB with respect to input noisy image and an average Structural Similarity Index Measure (SSIM) of 0.99 is observed for Pascal VOC 2012 dataset. Further, the model is quantized on different bit precision using the Qkeras library and synthesized using the High-Level Synthesis for Machine Learning (hls4ml) platform to make it hardware friendly so that inference can be performed on resource-constrained edge devices.

关键词： CMOS image Sensor Fixed pattern noise CNN Quantization Attention mechanism FPNrNet,hls4ml Qkeras

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