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

共 7080 条记录，以下是61-70 订阅

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Validating a Simulation Tool for Use with Patient Data for Evaluation of Advanced CT Reconstruction

Validating a Simulation Tool for Use with Patient Data for E...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Shin, Daniel W. Hein, Ilmar Akino, Naruomi Boedeker, Kirsten L. Canon Med Syst Corp Otawara Japan Canon Med Res USA Inc Vernon Hills IL USA

ISBN: (纸本)9781510671553;9781510671546

As Deep Learning Reconstruction (DLR) begins to dominate computed tomography (CT) reconstruction, performance evaluation via conventional phantoms with uniform backgrounds and specific sizes may benefit from augmentation with simulated controlled test objects inserted into anatomical backgrounds. The purpose of this study is to validate a simulation tool with physics-based image quality metrics both in phantom and in patient data. An analytic forward projection tool, based on detector and source geometry with beam spectra was designed to match the specifications of Canon Medical's Aquilion ONE Prism. The Catphan (TM) 500 and two water phantoms, 24cm and 32cm in diameter, were scanned with Aquilion ONE Prism at various mA levels and reconstructed with FBP. Corresponding simulated images were generated. The CT number, noise power spectrum (NPS) and modulation transfer function (MTF) were evaluated and compared between the simulated images and actual images. Simulated projection data of a CatphanTM sensitometry cylinder was also combined with a patient sinogram and reconstructed with a variety of kernels. The MTF of three different contrast rods were measured and compared with the MTF measured from Catphan (TM). The CT numbers were equivalent between the simulated data and the image acquired from the actual CT system. The MTF measured from the simulated data of both phantom and patient image matched with the MTF from Catphan (TM). The noise properties of the simulated data also aligned with the NPS of the 24cm and 32cm water phantom image. The simulation tool was able to generate images with image quality equivalent to the images scanned and reconstructed from the actual CT system. With this validation study, the simulation will be utilized to further evaluate the performance of deep learning reconstructions (DLRs).

关键词： Simulation Study Validation Study Reconstruction Deep Learning Reconstruction (DLR) image quality Computed Tomography

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Face image quality Vector Assessment for Biometrics Applications

Face Image Quality Vector Assessment for Biometrics Applicat...

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Najafzadeh, Nima Kashiani, Hossein Saadabadi, Mohammad Saeed Ebrahimi Talemi, Niloufar Alipour Malakshan, Sahar Rahimi Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9798350320565

In this paper, we propose a multi-task convolutional neural network which produces an image quality vector for an input face image. This image quality vector contains the face quality score and the information about the nuisance factors (i.e., pose, illumination, blurriness and expression) that caused the predicted image quality score. Our multi-task network utilizes a pretrained ResNet-50 as its stem. Employing different data augmentation techniques, we create a huge and diverse dataset. We ground truth this dataset and use it to fine-tune our multi-task neural network. Our Multi-task learning framework enables us to learn a shared and beneficial feature representation among the relevant tasks to achieve a better performance. Moreover, the proposed multi-task neural network provides useful information about the nuisance factors. Nuisance factors information is useful for applications like face image quality assessment during the automatic enrollment process where user's photo should comply with a standardized criteria. In this case, if the user upload a low-quality image which does not comply with a predefined standard, the system provides feedback about the nuisance factors associated with the captured image. Therefore, the user can resolve the problem, and upload a new image that can remedy the issue. Although an extensive research has been done on face image quality assessment, non of them have addressed face image quality vector effectively. To the best of our knowledge, our method is the first method that uses deep learning approach to generate a face image quality vector. The evaluation of results demonstrates that our method gets higher or comparable accuracy for face image quality assessment in comparison to the state-of-the-art methods and analyzes the input image to provide detailed information about the nuisance factors.

关键词： image quality Face recognition Neural networks Lighting Multitasking Convolutional neural networks Task analysis

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Fingerprint image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation 4

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

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4th International conference on Sustainable Expert systems, ICSES 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Choudhary, Suresh School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India Aryabhattt College of Engieering and research Centre India Mds University Ajmer India

ISBN: (纸本)9798331540364

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness. © 2024 IEEE.

关键词： Gabor filters

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Deep Equilibrium Diffusion Restoration with Parallel Sampling

Deep Equilibrium Diffusion Restoration with Parallel Samplin...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cao, Jiezhang Shi, Yue Zhang, Kai Zhang, Yulun Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Shanghai Jiao Tong Univ Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350353013;9798350353006

Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Due to the inherent property of diffusion models, most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs. Moreover, such long sampling chains hinder understanding the relationship between inputs and restoration results since it is hard to compute the gradients in the whole chains. In this work, we aim to rethink the diffusion model-based IR models through a different perspective, i.e., a deep equilibrium (DEQ) fixed point system, called DeqIR. Specifically, we derive an analytical solution by modeling the entire sampling chain in these IR models as a joint multivariate fixed point system. Based on the analytical solution, we can conduct parallel sampling and restore HQ images without training. Furthermore, we compute fast gradients via DEQ inversion and found that initialization optimization can boost image quality and control the generation direction. Extensive experiments on benchmarks demonstrate the effectiveness of our method on typical IR tasks and real-world settings.

关键词： Deep Equilibrium Models Diffusion Model image Restoration Parallel Sampling

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Brand Logo image Classification Based on Deep Convolutional Generative Adversarial Network 3

Brand Logo Image Classification Based on Deep Convolutional ...

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3rd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Wang, Yang Guangzhou Institute Of Science Technology Guangzhou China

ISBN: (纸本)9798350318609

Brand logo image examines how a critical dimension of logo design, namely the naturalness of the logo color, influences brand design induced by the logo. However, high complexity is observed, requiring higher quality data and susceptibility to overfitting when there is insufficient training data. The implementation of the proposed system involved the utilization of a Deep Convolutional Generative Adversarial Network (DCGAN) reduce overfitting and efficiently handle noise and distortion in the logo image, which is essential in brand logo image processing. The brand logo image dataset forwarded to the preprocessing stage included pixel values, which may lead to the loss of contrast, shadow, sharpness, noise removal, and structure in some relevant images. The classification involved DCGAN, aiming to achieve high-quality logo image prediction, noise reduction, and handling of overfitting by incorporating synthetic logo images. The proposed DCGAN method achieves promising performance, including a mean Average Precision (mAP) of 52.25, Accuracy of 97.09%, Precision of 97.01 %, Recall of 98.10%, and F1-score of 97.11 %. This performance is compared to existing methods such as YOLOv3 and Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Generative adversarial networks

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Design of English Teaching Resource system Based on Deep Learning 2

Design of English Teaching Resource System Based on Deep Lea...

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2nd IEEE International conference on image Processing and Computer Applications, ICIPCA 2024

作者： Jiang, Xiaojun School of Translation Studies Xi'an Fanyi University Shaanxi Xi'an710105 China

ISBN: (纸本)9798350360240

This paper seeks to design an English deep learning-based online teaching assistant system in this paper. The basic structure of the system is described in a web environment, which is composed of a system login module and a homework function module. It is then designed that the examinations and examination information management module reinforce its role as an online English teaching instrument. Training in deep learning networks is on the software side of system to improve quality of English online teaching resources. The results of the experiment show that training efficiency and login success rate both are higher than that in traditional systems. OK, then this system's application performance is good. © 2024 IEEE.

关键词： Deep reinforcement learning

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Exploring image Similarity-Based Splitting Techniques in Automotive Perception systems 17th

Exploring Image Similarity-Based Splitting Techniques in Aut...

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17th International conference on the quality of Information and Communications Technology (QUATIC)

作者： Babu, Md Abu Ahammed Pandey, Sushant Kumar Durisic, Darko Koppisetty, Ashok Chaitanya Staron, Miroslaw Volvo Car Corp Res & Dev Gothenburg Sweden Chalmers Univ Gothenburg Gothenburg Sweden

ISBN: (纸本)9783031702440;9783031702457

Training object detection models for automotive perception systems is often a challenging task due to the variation in the image data used to train and test the model. The images might come from different geographic locations as well as different weather and lighting conditions. image similarity-based split could be a safe option to keep the train and test sets as identical as possible. However, based on which similarity measure is chosen to split the data, the train and test set may not contain enough representative images of all the different situations, including seasonal and lighting variations, and hence often degrades overall performance. This study considered four different image similarity measures based on visual features and intrinsic/semantic information that come with the image data. The semantic similarity helps to avoid gathering images in the same (train/test) set that visually look similar to each other. The evaluation results show that the semantic similarity-based splits resulted in 12-47% higher performance of the object detection model in terms of mean average precision (mAP) and F1-score. Among the four similarity measures, All Class similarity consists of the highest intrinsic information available with the image data, which also results in the highest performance of the model when used for data splitting.

关键词： image similarity object detection computer vision deep learning automotive perception system

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image classification algorithm performance based on Fλ/d 35

Image classification algorithm performance based on Fλ/d

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conference on Infrared Imaging systems - Design, Analysis, Modeling, and Testing XXXV

作者： Hixson, Jonathan G. Teaney, Brian Finch, Michael Nehmetallah, Georges Driggers, Ronald US Army Combat Capabil Dev Command DEVCOM C5ISR C Ft Belvoir VA 22060 USA Catholic Univ Amer EECS Dept 620 Michigan Ave NE Washington DC 20064 USA Univ Arizona Wyant Coll Opt Sci Infrared Syst Grp Tucson AZ USA

ISBN: (纸本)9781510674097;9781510674080

This paper will take an initial look at the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of Yolo_v3 (You Only Look Once) algorithm for object classification. The Yolo_v3 algorithm will initially be trained using static imagery provided in the commonly available Advanced Driver Assist system (ADAS) dataset. image processing techniques will then be used to degrade image quality of the test data set, simulating detector-limited to optics-limited performance of the imagery. The degraded test set will then be used to evaluate the performance of Yolo_v3 for object classification. Results of Yolo_v3 will be presented for the varying levels of image degradation. An initial summary of the results will be discussed along with recommendations for evaluating an algorithm's performance using a sensors FLD metric value.

关键词： infrared simulation YOLO ADAS FLD NV IPM computer vision image identification and classification convolutional neural network deep learning

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I3FDM: IRIS INPAINTING VIA INVERSE FUSION OF DIFFUSION MODELS 49

I3FDM: IRIS INPAINTING VIA INVERSE FUSION OF DIFFUSION MODEL...

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49th IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Chenyang Zhang, Zhili Li, Peipei He, Zhaofeng Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Iris images captured in real-world scenarios are often occluded, which leads to a significant degradation for the iris recognition system. Therefore, it is necessary to propose an effective iris inpainting method. While generative adversarial network (GAN)-based image inpainting methods have shown promise, they often suffer from issues such as mode collapse and training instability. Recently, denoising diffusion probabilistic model (DDPM) has surpassed GAN in terms of image quality while maintaining stable training. Combining DDPM and the characteristics of iris image, I3FDM (Iris Inpainting via Inverse Fusion of Diffusion Models), a method that iteratively modifies intermediate variables in the generation process based on a given occluded image. Since these modifications introduce semantic differences, we introduce an inverse fusion module to enhance the performance of iris inpainting. I3FDM enables the processing of various types of occluded images using an unconditional DDPM without the need for additional learning. Extensive experimental results qualitatively and quantitatively demonstrate that our method produces iris images with richer texture information and improves the performance of iris recognition.

关键词： Iris images Iris Inpainting Denoising Diffusion Probabilistic Model Mask image

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quality Assessment of Screen Content Videos 6

Quality Assessment of Screen Content Videos

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6th International conference on Pattern Recognition and image Analysis (IPRIA)

作者： Motamednia, Hossein Cheraaqee, Pooryaa Mansouri, Azadeh Mahmoudi-Aznaveh, Ahmad Kharazmi Univ Fac Engn Dept Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci Sch Comp Sci High Performance Comp Lab Tehran Iran Shahid Beheshti Univ Cyber Res Inst Tehran Iran

ISBN: (纸本)9798350314557

Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human visual system. With the diversity in the contents of multimedia signals, the conventional methods for traditional media seems no longer satisfying. One of these emerging media, is the screen content images/videos (SCI/Vs). Containing texts and computer generated graphics, SCVs cannot be sufficiently expressed with features designed for natural sceneries. Therefore, new researches tried to devise objective quality assessment metrics, specificly for screen contents. Recently, a dataset was proposed for quality assessment of screen content videos. Since screen contents are full of structures that spread in cardinal directions, we were motivated to employ the horizontal and vertical subbands of the wavelet transform to characterize these types of visual contents. The features were incorporated in a full-reference method that showed promising results on the publicly available dataset for SCV quality assessment. The method can bo accessed via: https://***/motamedNia/QASCV.

关键词： video quality assessment full reference human visual system wavelet screen content

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