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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是461-470 订阅

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Capturing Banding in images: Database Construction and Objective Assessment

Capturing Banding in Images: Database Construction and Objec...

引用

IEEE international conference on Acoustics, Speech and signal processing

作者： Akshay Kapoor Jatin Sapra Zhou Wang University of Waterloo Canada

With the fast technology advancement and the accelerated growth of high-quality image and video production and services, banding or false contour has become a frequently observed artifact in images, creating annoying negative impact on the visual quality-of-experience (QoE) of end users. Nevertheless, thorough investigations on the causes of banding, and effective and efficient methods to detect and reduce banding are largely lacking. This work targets at capturing and quantifying banding artifacts in images. We construct the first of its kind large-scale public database, consisting of 1,250 images with segmented banding regions and 169,501 image patches with class labels. We also develop a deep neural net-work based no-reference deep banding index (DBI), which not only produces an overall banding assessment of a given image, but also creates a banding map that indicates the variation of banding across the image space. Our experiments show that the proposed DBI method achieves accurate banding prediction with low computational cost. The database and the proposed algorithm are made publicly available.

关键词： Visualization image segmentation signal processing algorithms Production signal processing Predictive models Prediction algorithms

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An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation

An automated framework for image classification and segmenta...

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conference on Medical Imaging - image processing

作者： Prieto, Juan C. Shah, Hina Rosenbaum, Alan J. Jiang, Xiaoning Musonda, Patrick Price, Joan T. Stringer, Elizabeth M. Vwalika, Bellington Stamilio, David M. Stringer, Jeffrey S. A. Univ N Carolina Dept Psychiat Chapel Hill NC 27515 USA Univ N Carolina Dept Obstet & Gynecol Chapel Hill NC 27515 USA North Carolina State Univ Dept Mech & Aerosp Engn Raleigh NC USA Univ Zambia Sch Publ Hlth Lusaka Zambia Univ Zambia Sch Med Dept Obstet & Gynaecol Lusaka Zambia Wake Forest Univ Bowman Gray Sch Med Dept Obstet & Gynecol Winston Salem NC USA

ISBN: (纸本)9781510640221

Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. In this report, we leverage a large database of obstetric US images acquired, stored and annotated by expert sonographers to train algorithms to classify, segment, and measure several fetal structures: biparietal diameter (BPD), head circumference (HC), crown rump length (CRL), abdominal circumference (AC), and femur length (FL). We present a technique for generating raw images suitable for model training by removing caliper and text annotation and describe a fully automated pipeline for image classification, segmentation, and structure measurement to estimate the GA. The resulting framework achieves an average accuracy of 93% in classification tasks, a mean Intersection over Union accuracy of 0.91 during segmentation tasks, and a mean measurement error of 1.89 centimeters, finally leading to a 1.4 day mean average error in the predicted GA compared to expert sonographer GA estimate using the Hadlock equation.

关键词： Fetal ultrasound GA estimation Machine learning

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Adaptive Background Normalization Algorithm for Noise Resilient in Time-frequency Map

Adaptive Background Normalization Algorithm for Noise Resili...

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Jiahua Zhu College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Since conventional background normalization approaches are sensitive to the manual setting parameters such as the length of normalization window and normalization threshold, an adaptive algorithm is proposed without any preset parameters. The algorithm is validated by experimental data for the resilience to the noise in the time-frequency map, with acceptable increase on the computational burden.

关键词： Time-frequency analysis image processing conferences Adaptive algorithms Manuals Resilience Noise level

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Decomposing Textures using Exponential Analysis

Decomposing Textures using Exponential Analysis

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IEEE international conference on Acoustics, Speech and signal processing

作者： Yuan Hou Annie Cuyt Wen-shin Lee Deepayan Bhowmik University of Antwerp Antwerp Belgium College of Mathematics and Statistics Shenzhen University Shenzhen Guangdong China Division of Computing Science & Mathematics University of Stirling Stirling UK

Decomposition is integral to most image processing algorithms and often required in texture analysis. We present a new approach using a recent 2-dimensional exponential analysis technique. Exponential analysis offers the advantage of sparsity in the model and continuity in the parameters. This results in a much more compact representation of textures when compared to traditional Fourier or wavelet transform techniques. Our experiments include synthetic as well as real texture images from standard benchmark datasets. The results outperform FFT in representing texture patterns with significantly fewer terms while retaining RMSE values after reconstruction. The underlying periodic complex exponential model works best for texture patterns that are homogeneous. We demonstrate the usefulness of the method in two common vision processing application examples, namely texture classification and defect detection.

关键词： Wavelet transforms Frequency-domain analysis Fitting signal processing algorithms signal processing image representation image decomposition

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Self-Convolution: A Highly-Efficient Operator for Non-Local image Restoration

Self-Convolution: A Highly-Efficient Operator for Non-Local ...

引用

IEEE international conference on Acoustics, Speech and signal processing

作者： Lanqing Guo Zhiyuan Zha Saiprasad Ravishankar Bihan Wen School of Electrical & Electronic Engineering Nanyang Technological University Singapore Science and Engineering and Biomedical Engineering Michigan State University East Lansing MI USA

Constructing effective image priors is critical to solving ill-posed inverse problems, such as image restoration. Recent works proposed to exploit image non-local similarity for inverse problems by grouping similar patches, and demonstrated state-of-the-art results in many applications. However, comparing to classic local methods based on filtering or sparsity, most of the non-local algorithms are time-consuming, mainly due to the highly inefficient and redundant block matching step, where the distance between each pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step, and produce the equivalent results with much cheaper computation. Based on Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results also demonstrate that Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching. The codes will be released on GitHub.

关键词： Inverse problems Filtering Fast Fourier transforms Computational modeling conferences signal processing algorithms signal processing

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image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： image recognition Convolution Target recognition Fuses Neural networks Stacking signal processing algorithms

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Pixelation Algorithm for Computing the Intersection of Connected Graphs in Intelligent Damage Recognition

Pixelation Algorithm for Computing the Intersection of Conne...

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international conference on Vision, image and signal processing (ICVISP)

作者： Hang Li Di Xiong Yan Wang Xin Ma Qianqian Zhu Automotive Data of China Co. Ltd. China Automotive Technology and Research Center Co.Ltd. Tianjin China

ISBN: (纸本)9781665408356

Intelligent damage determination refers to the identification of the damage and parts of the vehicle in the picture and video through the image recognition algorithm, and then automatically output the damage of the vehicle. How to accurately judge the size of the damage on the vehicle components is an important step to get the damage of the vehicle. This problem is actually a solution to the intersection of two connected graphs. However, the existing algorithms can only solve some simple and special graphics. In practical applications, the shapes of damage and parts are changeable, and these algorithms can no longer meet the actual needs. This paper presents a universal algorithm for solving the intersection of connected graphs by pixelating the graph and according to the change law of the relative position relationship between the points on the plane and the graph. The algorithm has a wide range of applications and simple input requirements for graphics. The application results of simulation tests and actual scenes show that this algorithm can accurately calculate the intersection of concave polygons, holed polygons and other connected graphics, which has high practical application value.

关键词： image quality image recognition Shape Face recognition Computational modeling signal processing algorithms signal processing

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Time-Frequency Parameter Estimation Method of Frequency Hopping signal Based on Morphology Method under Low SNR

Time-Frequency Parameter Estimation Method of Frequency Hopp...

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Jialu Wu Fucheng Guo College of Electronic Sciences and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Time frequency detection under low signal-to-noise ratio(SNR) is a challenging problem in electromagnetic spectrum management. In this paper, we propose a method for estimating time-frequency parameters of frequency hopping(FH) signal embedded in noise. Based on mathematical morphology method, the effects of different structure elements(SEs) on time-frequency image are compared in detail. Furthermore, Otsu binarization algorithm is used to improve the accuracy of time-frequency parameters by better threshold of gray image binarization. Simulation results show that the proposed method has smaller measurement error and better estimation performance for time-frequency parameters.

关键词： Time-frequency analysis Measurement errors Simulation image processing signal processing algorithms Morphology Estimation

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image Denoising and Colorization Based on Plug and Play Framework

Image Denoising and Colorization Based on Plug and Play Fram...

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IEEE international conference on signal and image processing (ICSIP)

作者： Weicheng Guo Fei Chen Hang Cheng College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665446006

A digital image has become an essential carrier of information transmission, but it will inevitably be destroyed during the transmission process. For image denoising or image colorization, there are already many excellent algorithms, and there have been promising developments in the field of deep learning. However, in these classic methods, few algorithms can accomplish image denoising and colorization simultaneously. This paper proposes a plug and play framework to solve this problem, which combines the advantages of generative adversarial networks and plug and play framework. Moreover, we design a lightweight conditional GAN to achieve the purpose of training with fewer computing resources. Experiments on CFP and Landscape datasets show that this method can effectively complete image denoising and image colorization tasks.

关键词： Training Deep learning Digital images Noise reduction Information processing Generative adversarial networks Noise measurement

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FPGA Hardware Design for Plenoptic 3D image processing Algorithm Targeting a Mobile Application

FPGA Hardware Design for Plenoptic 3D Image Processing Algor...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Faraz Bhatti Thomas Greiner Pforzheim University Pforzheim Germany

ISBN: (纸本)9781728176055;9781728176062

Over the past years, widespread use of applications based on 3D image processing has increased rapidly. It is being employed in various fields, such as research, medicine and automation. Plenoptic camera system is used to capture light-field that can be exploited to estimate the 3D depth of the scene. The respective algorithms consist of a large number of computation-intensive instructions. It eventually leads to the problem of large execution time of the algorithm. Moreover, they require substantial amount of memory cells for the storage of intermediate and final results. Desktop GPU based solutions are power intensive and therefore cannot be used in the mobile applications with low energy requirements. The idea presented in this paper is to use the FPGA based hardware design to improve the performance of a 3D depth estimation algorithm by utilizing the advantage of concurrent execution. The algorithm is implemented, evaluated and the results show that FPGA design reduces the respective execution time significantly.

关键词： Three-dimensional displays image processing signal processing algorithms Graphics processing units Estimation Cameras Hardware

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