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

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Bengali Handwritten Character Transformation: Basic to Compound and Compound to Basic Using Convolutional Neural Network

Bengali Handwritten Character Transformation: Basic to Compo...

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Soarov Chakraborty Shourav Paul Khulna University of Engineering &Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Bengali handwritten character recognization technique faces numerous complexities because of the unusual shape and stroke of the letters. In Bengali scripts, two or more basic characters construct a compound character. So it is challenging for the children to understand how these compound characters have been structured. Here, we recommend a system that can turn these basic characters into a compound character. Also, transfer a compound character into the basic characters by combining deep learning and image processing principles. The identification is begun by preprocessing the image using several image processing techniques, and then a convolutional neural network model is used for the transformation of the characters. Furthermore, the MapReduce technique is introduced for finding the appropriate character. This experiment was done on a dataset of more than 300,000 images and received an 89.20% accuracy rate.

关键词： Handwriting recognition image processing Software algorithms Software Compounds Convolutional neural networks Character recognition

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Modular Commands Recognition Based on Semantic Understanding

Modular Commands Recognition Based on Semantic Understanding

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IEEE international conference on signal and image processing (ICSIP)

作者： Xuyi Zhuang Mingjiang Wang Yukun Qian Zehua Zhang Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)9781665446006

Recent commands recognition methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes. Most commands recognition models can only recognize the command in a fixed format, which is not convenient. In this research, we propose a modular model that separates speech recognition model which can make full use of advanced end-to-end ASR and semantic recognition model which is designed for semantic understanding. We use the tokens recognized by speech recognition model as the input of the semantic recognition model. We tested the performance of the model by using both fixed form commands and commands in the daily pragmatic environment. For this reason, we specially build a command dataset in the daily pragmatic environment. Experimental results show that the model proposed in this paper not only performs well in the task of fixed-format commands recognition but can also realize efficient and accurate command recognition in daily pragmatic environment.

关键词： Training Deep learning image recognition conferences Semantics Neural networks Speech recognition

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Learning Mixed Membership from Adjacency Graph Via Systematic Edge Query: Identifiability and Algorithm

Learning Mixed Membership from Adjacency Graph Via Systemati...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Shahana Ibrahim Xiao Fu School of Electrical Engineering & Computer Science Oregon State University Corvallis OR United States

Graph clustering is a core technique for network analysis problems, e.g., community detection. This work puts forth a node clustering approach for largely incomplete adjacency graphs. Under the considered scenario, instead of having access to the complete graph, only a small amount of queries about the graph edges can be made for node clustering. This task is well-motivated in many large-scale network analysis problems, where complete graph acquisition is prohibitively costly. Prior work tackles this problem under the setting that the nodes only admit single membership and the clusters are disjoint, yet multiple membership nodes and overlapping clusters often arise in practice. Existing approaches also rely on random edge query patterns and convex optimization-based formulations, which give rise to a number of implementation and scalability challenges. This work offers a framework that provably learns the mixed membership of nodes from overlapping clusters using limited edge information. Our method is equipped with a systematic edge query pattern, which is arguably easier to implement relative to the random counterparts in certain applications, e.g., field survey based graph analysis. A lightweight scalable algorithm is proposed, and its performance characterizations are presented. Numerical experiments are used to showcase the effectiveness of our method.

关键词： Systematics Scalability image edge detection conferences signal processing algorithms Clustering algorithms signal processing

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An advanced JPEG Steganalysis Method with Balanced Depth and Width Based on Fractal Residual Network

An Advanced JPEG Steganalysis Method with Balanced Depth and...

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Symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Rui Zhan Yi Ma Shanshan Yang Yufei Man Yu Yang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665426565

Digital image steganalysis technology based on deep learning has made rapid development. The latest technology, SFNet based on fractal technology, exceeds the cutting-edge technology SRNet in performance. The disadvantage is that SFNet is not suitable for JPEG steganalysis. Due to the interference of compression noise in JPEG images, it is difficult for SFNet to extract weak stego signal by self-similarity extended network. In this paper, a JPEG steganalysis method based on fractal residual network, called FRNet, is proposed. This paper introduces the residual unit with a shortcut connection into the fractal structure, which helps the network effectively suppress the image content and generate the residual image with stego noise. Then, referring to the bottleneck block of ResNet, the deep feature extraction module is constructed to downsample the feature map and superimpose the weak stego signal between different channels of the convolution layer. Finally, the fractal residual module and depth feature extraction module are used to control the width and depth of the network to maximize the detection performance. Two adaptive steganography algorithms of J-UNIWARD and UERD are chosen to evaluate the performance. Experimental results show that the detection error of FRNet is 11.52% lower than J-XuNet, 10.12% lower than WangNet, and 2.54% lower than SRNet.

关键词： Steganography image coding Convolution Transform coding Interference Feature extraction Fractals

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Adversarial Reconstruction for Outdoors Insulator Anomaly Detection and Recognition in High-Speed Railway Traction Substation

Adversarial Reconstruction for Outdoors Insulator Anomaly De...

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

作者： Yuchen Peng Xuemin Lu Wei Quan Ning Zhou Dong Zou Jim X. Chen School of Electrical Engineering Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China George Mason University Fairfax Virginia USA

As an indispensable part of the high-speed railway traction substation,the outdoors insulator ensures the normal operation of transmission network and defends the stability of transmission line. In order to avoid the serious transmission fault and decrease the economic loss, finding a effective way to find the anomaly regions of insulator is a meaningful issue. Therefore, a method is presented for outdoors insulator anomaly detection and recognition in high-speed railway traction substation based on adversarial reconstruction in this paper. First, the R3Det is employed to fix the position of insulator from the input image of traction substation. At the second stage, the insulator image which located from the first stage is entered into our designed generative adversarial networks to reconstruct input image. In the end, the structural similarity algorithm is adopted to compare the difference between the input image and the generated image to find the anomalous region, then template matching is used for recognition of the anomalous area. Test results on Heishan traction substation show the effective performance of our designed method.

关键词： Visualization Substations image recognition signal processing algorithms Insulators Generative adversarial networks Rail transportation

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Quality Assessment оf Defocused image Recovery algorithms 2020

Quality Assessment оf Defocused Image Recovery Algorithms

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2020 3rd international conference on Sensors, signal and image processing

作者： Magdalena Garvanova Vladimir Ivanov University of Library Studies and Information Technologies Bulgaria Institute of Information and Communication Technologies-BAS Bulgaria

ISBN: (纸本)9781450388283

Camera shake during video recording is a major source of image defocus. Restoring these defocuses is one of the most important tasks in image processing. This article examines and evaluates the most commonly used algorithms in practice, namely: Wiener filtration, Richardson-Lucy algorithm, Tikhonov regularization, and Blind deconvolution. The comparisons were made using structural similarity and root mean square error.

关键词： camera shake image processing defocuses

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Lossy Data Compression using K-Means Clustering on Retinal images using RStudio

Lossy Data Compression using K-Means Clustering on Retinal I...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： S. Sivaarunagirinathan B. Ajith Bala Shaik Fairooz G. Sasi Har Narayan Upadhyay V. Elamaran School of EEE SASTRA Deemed University Thanjavur India Malla Reddy Engineering College Secunderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (纸本)9781665438124

The massive volume of data limits the use of data on portable devices like mobile phones, as well as memory-limited devices like video game consoles. As a result, the demand for data compression methods in the field of signal and image processing is skyrocketing. Data compression required advanced ways to serve better, with a greater compression ratio, due to the rising demands, which expected more sophisticated algorithms. This article primarily focuses on image compression (retinal images) utilizing the k-means clustering technique, which is a popular unsupervised classification strategy in machine learning. The compression challenge in this study uses two retinal pictures. The most useful open-source statistical calculation tool is R programming, which is employed here with RStudio, an Integrated Development Environment (IDE). The use of R (RStudio) has had an impact across all domains of research and engineering. The test retinal images had a compression ratio of 33.3 percent, according to simulation data.

关键词： image coding Machine learning algorithms Data compression Clustering algorithms Process control Programming Retina

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SV-Loop:Semantic-Visual Loop Closure Detection with Panoptic Segmentation

SV-Loop:Semantic-Visual Loop Closure Detection with Panoptic...

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Zhian Yuan Ke Xu Bin Deng Xiaoyu Zhou Peibo Chen Yanxin Ma College of Electronic Science National University of Defense Technology Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China Hunan Key Laboratory for Marine Detection Technology Changsha China

ISBN: (纸本)9781665446006

Loop closure detection plays an important role in simultaneous localization and mapping (SLAM) system. Traditional visual-based methods of loop closure detection are hard to relieve the issue of dynamic objects and change of light in complex environments. To improve robustness of loop closure detection, a loop closure detection algorithm named SV-Loop combines semantic and visual information is proposed in this paper. This work leverages the latest panoptic segmentation network to extract semantic information. Firstly, similarity of details is calculated through visual module in micro perspective. Then similarity of objects is computed by semantic module from a macro perspective. Finally, visual and semantic loop candidates are integrated to determine loop closure. Experiments have been conducted on open source datasets and practical scene. Experimental results have shown higher accuracy and better robustness of proposed method in complex environmental changes.

关键词： Location awareness Visualization image segmentation Simultaneous localization and mapping Heuristic algorithms Semantics Robustness

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Self-Adaptive Visual Background Extraction with Ghost Regions Elimination

Self-Adaptive Visual Background Extraction with Ghost Region...

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IEEE international conference on signal and image processing (ICSIP)

作者： Ling Liu Shuai Liu Zhong Qu Dongyang Zhou School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665446006

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.

关键词： Visualization Adaptation models Heuristic algorithms image processing conferences Lighting Reliability

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Structure-Enhanced Attentive Learning For Spine Segmentation From Ultrasound Volume Projection images

Structure-Enhanced Attentive Learning For Spine Segmentation...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Rui Zhao Zixun Huang Tianshan Liu Frank H. F. Leung Sai Ho Ling De Yang Timothy Tin-Yan Lee Daniel P.K. Lun Yong-Ping Zheng Kin-Man Lam The Hong Kong Polytechnic University Hong Kong China School of Biomedical Engineering University of Technology Sydney NSW Australia

ISBN: (纸本)9781728176055;9781728176062

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis.

关键词： image segmentation Ultrasonic imaging Shape conferences Semantics Imaging signal processing algorithms

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