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Incremental feedback learning mechanism for highly efficient automatic image segmentation with feature coupling

Incremental feedback learning mechanism for highly efficient...

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2021 IEEE international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Bhagwat, Manisha Gupta, Gyanendra Ambhaikar, Asha Kalinga University Dept.of CSE Raipur India

ISBN: (纸本)9781665435215

Segmentation of images is a pre-requisite for all modern high efficiency image processing system. In order to perform this task, various application specific algorithms are designed and deployed by image processing experts. These systems work on a context-specific mode, wherein all segmentation outputs are restricted by context of images for which the system is trained. In order to deploy these systems to other domains, complex tuning and optimization operations are needed. This reduces applicability of these system models for real time use cases, where general purpose segmentation methods are needed. These use cases include but are not limited to, scene classification, satellite image classification, yield prediction, traffic detection, etc. Moreover, general purpose image segmentation models work effectively only under a pre-set types of application scenarios, and need to be constantly trained in order to improve their applicability. Retraining these systems increases computational costs, and requires large training and testing delays. In order to remove these drawbacks, in this text an incremental feedback learning mechanism with feature coupling is proposed. The proposed model uses a wide variety of image segmentation methods that analyze colour, texture shape information;and map it with relevant image features. These features are traced along with segmentation quality metrics like peak signal to noise ratio (PSNR), figure of merit (FOM), minimum mean squared error (MMSE), and probabilistic random index (PRI) in order to evaluate the best segmentation algorithm. These features are classified using an ensemble classification model for selection of the most efficient segmentation method that maximizes PSNR, PRI while minimizing MMSE. Parametric evaluation suggests that the proposed model is able to improve segmentation accuracy by 8%, and reduce false alarm rate by 15% when compared with standard automatic segmentation models. © 2021 IEEE.

关键词： image segmentation

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Research on image enhancement algorithm based on deep neural network algorithm

Research on image enhancement algorithm based on deep neural...

引用

2021 international conference on Artificial Intelligence and Information Technology, ICAIIT 2021

作者： Lin, Qiang wen Li, Liu mei Academic affaires office Guangzhou College of Commerce Guangdong Guangzhou511363 China

image denoising and enhancement technology plays an important role in remote sensor image processing. Due to the low brightness, signal-to-noise ratio and contrast of image processing, the traditional remote sensor is limited in obtaining object information. Based on the principle of biological segmentation algorithm, neural network algorithm technology can carry the advantages of image evaluation algorithms such as PSNR, IFC and SSIM, through the end-to-end training of nonlinear infinite approximation, and carry out the research of image fitting low light level image enhancement and denoising. On this basis, the effectiveness of the research results is evaluated. The results show that compared with the traditional remote sensor imaging technology, the PSNR value of the image constructed by the depth neural network algorithm is significantly improved by about 15.2401, the IFC value is improved by about 0.4523, and the SSIM value is improved by about 0.4500, which improves the low light level imaging quality to a higher extent, and provides excellent technical guidance for the remote sensor to obtain the object information at night. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Deep neural networks

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A Band Selection Method for Hyperspectral image Based on Binary Coded Hybrid Rice Optimization Algorithm 11

A Band Selection Method for Hyperspectral Image Based on Bin...

引用

11th IEEE international conference on Intelligent Data Acquisition and advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Liu, Shiqin Zong, Xinlu Shu, Zhe Xia, Xiaoyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

Hyperspectral image analysis is one of the most important topics in the field of remote sensing. As the band dimension of hyperspectral image increases, it may cause a curse of dimensionality. Band selection could choose a subset of bands that is the most effective for classification recognition to achieve dimensionality reduction. Furthermore, binary coding is more suitable for solving the band selection problem. Therefore, a band selection method for hyperspectral image based on binary coded hybrid rice optimization algorithm is proposed in the paper. The band selection problem is solved as a combinatorial optimization problem by defining an objective function based on the classification accuracy and the number of bands. The proposed method is compared with other nature-inspired algorithms on Indian Pines, Salinas, Kennedy Space Center, and Pavia University hyperspectral datasets. Experimental results demonstrate that the proposed method achieves satisfactory results in terms of performance and execution time for band selection.

关键词： hyperspectral image band selection hybrid rice optimization algorithm image processing

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Analysis and Application of Multispectral image processing Techniques Applied to Soybean Crops from Drones Vision System 18

Analysis and Application of Multispectral Image Processing T...

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18th international conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Gonzalez, Evelio Nunez, Cristhian Salinas, Jose Rodas, Jorge Rodas, Mariela Paiva, Enrique Kali, Yassine Saad, Maarouf Lesme, Fernando Lesme, Jose Gonzalez, Luis Maldonado, Belen Rodriguez-Pineiro, Jose Univ Catolica Nuestra Senor Asuncion Unidad Pedag Caacupe Caacupe Paraguay TECHA Caacupe Paraguay Univ Nacl Asuncion Fac Ingn Lab Power & Control Syst Luque Paraguay Inst Paraguayo Tecnol Agr Caacupe Paraguay Ecole Technol Super GREPCI Lab Montreal PQ Canada Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9789897585227

Drones are important in precision agriculture applications since they represent a new tool that can increase crop production. In this context, the digital processing of the images obtained from multispectral cameras integrated into the drones makes it possible to analyze the stress state of the crops, their vigor, a burned area, among others. The latter are usually obtained through proprietary applications with very high subscription costs. For this reason, this article presents the step-by-step implementation process of the different methods or algorithms to be applied to multispectral images using the open-source Python programming language. We use a soybean crop as an example of the application, and the results obtained from applying the digital image processing algorithms are presented.

关键词： Drones Multispectral Imaging Digital signal processing Precision Agriculture

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A Multiscale Dual-Attention based Convolutional Neural Network for Ship Classification in SAR image

A Multiscale Dual-Attention based Convolutional Neural Netwo...

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2021 IEEE international conference on signal processing, Communications and Computing, ICSPCC 2021

作者： Zhou, Gaoyu Zhang, Gong Fang, Zheng Dai, Qijun Ministry of Education Nanjing University of Aeronautics and Astronautics Key Lab of Radar Imaging and Microwave Photonics Nanjing China

ISBN: (纸本)9781665429184

Classical ship target classification algorithms in synthetic aperture radar (SAR) images mainly rely on manual extraction of simple physical features to distinguish different ship types. A novel multiscale dual-Attention (MDA) based convolutional neural network (CNN) is proposed in this paper to highlight the deep distinguishable features extracted from SAR images for classification. First, we apply the data enhancement method to expand the limited labeled sample set to alleviate the defect of unbalanced dataset. Second, the squeeze-And-excitation(SE) block is adopted to learn the initial weight of the feature maps to mitigate the negative effects of large similarity between different ship classes. Third, the weighted feature maps are sent into two parallel self-Attention blocks, and the semantic dependence on spatial dimension and channel dimension is modeled respectively. Feature fusion is carried out to further improve feature discrimination ability. The results of ablation experiments and comparative experiments on the open SAR ship dataset show that this method can mine deep discriminant features effectively, and further improve the classification accuracy of ships in SAR images compared with baseline method and other latest methods. © 2021 IEEE.

关键词： Ships

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image Segmentation Techniques and Optimization algorithms for Lung Cancer Detection 1

引用

1st international conference on Advances in signal processing and Communication Engineering (ICASPACE)

作者： Sucharitha, B. Devi, Damisetti Savitri Sushmitha, Jakku MJCET ECED Hyderabad Telangana India MJCET ECE Hyderabad Telangana India

ISBN: (数字)9789811955501

ISBN: (纸本)9789811955525;9789811955501;9789811955495

The most frequent malignancy is lung cancer and it has the greatest fatality rate among all types of cancer. Early discovery and treatment of the condition can minimize the mortality rate and probability. Other imaging methods are not as good as CT. CT scan images are visually interpreted and it can be a time-consuming and errorprone endeavor, delaying lung cancer identification Lung cancer was diagnosed using chest X-ray films but using these films, the cancer is not classified into various and accuracy obtained is not acceptable. The main goal is to develop an automated method for detecting lung cancer in scans. image preprocessing methods such as median filtering are proposed, followed by segmentation of the lung region of interest using mathematical morphological operations and optimization algorithms such as Particle Swarm Optimization, k-means clustering and Inertia Weighted Particle Swarm Optimization. Out of all, the algorithms k-means clustering out performed with the high (Brindha et al. in J Chem Pharm Sci, 2016) gave a highest accuracy of 90% than the other methods which has only 89.05% accuracy.

关键词： Lung cancer Malignant Particle swarm optimization

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A Compendious Analysis of Advances in HE Methods for Contrast Enhancement 2nd

A Compendious Analysis of Advances in HE Methods for Contras...

引用

2nd international conference on VLSI, Communication and signal processing, VCAS 2019

作者： Vijayalakshmi, D. Nath, Malaya Kumar National Institute of Technology Puducherry Karaikal India

ISBN: (纸本)9789811568398

image enhancement is a crucial pre-processing technique for image processing applications such as satellite images, medical images, and aerial surveillance systems. The image enhancement aims to produce visual content of the image in more pleasing and more suitable for machine vision applications. Enhancement of image can be improved by either increasing the contrast of the image with a low dynamic range or by highlighting the prominent details of the image. In this paper, a comprehensive analysis of bi-histogram and two-dimensional histogram equalization-based contrast enhancement techniques is performed. The performance of the various algorithms has been validated through three different databases and various performance measures. From the compendious analysis, it can be interpreted that modeling an algorithm with lower absolute mean brightness error (AMBE) and higher contrast values can render a better-enhanced image. From the qualitative and quantitative analysis, edge enhancing bi-histogram equalization using guided image filter outperforms the other contrast enhancement techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Parametric Analysis of CT-image-Preprocessing for Improved Performance of Post-processing Operation 10

Parametric Analysis of CT-image-Preprocessing for Improved P...

引用

10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Shivwanshi, Resham Raj Nirala, Neelamshobha National Institute of Technology Raipur Department of Biomedical Engineering India

ISBN: (纸本)9781665439701

advanced technological tools in medical image analysis for disease detection and diagnosis are progressively coming into the utility of doctors and academicians due to various methodological evolution. Since the last three decades, various studies have been performed to achieve the state of the art predictive ability through early warning disease detection systems. After going through existing research work, it has been found that there is a lack of credibility in CT (computed tomography) image disease detection algorithms, which can be overcome by applying certain image processing and statistical analysis techniques. This article is made to describe a disparate approach in order to attain eminence in terms of lung disease diagnosis and detection. There are a huge amount of databases available online, but most of them encounter the issues of image noise and quality deterioration that further becomes the cause of irregularity and erroneous outcomes. The notion of this paper is to delineate an approach to pre-process input images and measure the quality of the given technique in order to choose better image operations and improve their visual information before analyzing them through a meticulous algorithm. An amalgamation of appropriate filters and image enhancement operations are also utilized to make clear insights of abnormality present inside of lung parenchyma. Furthermore, This study shows that the application of a high pass filter in the spatial domain improves the input image quality that is clearly identified by performing statistical analysis of output parameters. It is also observed that the otsu filtered image is more suitable to prepare the image for an efficient segmentation procedure. At last, it has been discussed that the overall approach in the form of pre-processing and its parameter estimation would not only help to assure quality enhancement of input image but also assist to run disease detection precisely in order to obtain reliable outcomes. ©

关键词： image enhancement

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image processing and Control of Tracking Intelligent Vehicle Based on Grayscale Camera 5

Image Processing and Control of Tracking Intelligent Vehicle...

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5th IEEE international conference on image processing Applications and Systems (IPAS)

作者： Zhang, Jian Liu, Yufan Li, Ao Zeng, Jinshan Xie, Hongtu Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665462198

In order to realize the rapid and stable recognition and automatic tracking of various complex roads by the intelligent vehicles, this paper proposes image processing and cascade Proportion Integration Differentiation (PID) steering and speed control algorithms based on CMOS grayscale cameras in the context of the national college student intelligent vehicle competition. First, the grayscale image of the track is acquired by the grayscale camera. Then, the Otsu method is used to binarize the image, and the information of black boundary guide line is extracted. In order to improve the speed of the race, various track elements in the image are identified and classified, and the deviation between the actual centerline position and the ideal centerline position of the intelligent vehicle is calculated. Third, the discrete incremental cascade PID control algorithm is used to calculate the pulse width modulation (PWM) signal corresponding to the deviation. And the PWM signal is acted on the steering motor through the driving circuit, driving the intelligent vehicle to always drive along the middle road, so as to achieve the purpose of automatic tracking guidance. Experiments prove that the intelligent vehicle of this design can identify complex roads quickly and in a stable way, accurately complete automatic tracking, and obtain higher speed performance.

关键词： tracking intelligent vehicle proportional integral derivative control CMOS grayscale camera binary image image processing

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Deformation Field Component Attention Fusion for Coarse-to-Fine Medical image Registration Network 9

Deformation Field Component Attention Fusion for Coarse-to-F...

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9th international conference on Intelligent Computing and signal processing, ICSP 2024

作者： Zhang, Chi Zhao, Haifeng Liu, Deyin School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350376548

In recent years, deep learning-based medical image registration algorithms have demonstrated notable achievements in improving registration accuracy and speed. However, these algorithms have not explored the potential benefits of utilizing different networks to generate distinct deformation fields, which can then be integrated to produce a final deformation field. In this paper, we propose deformation field component attention fusion for coarse-to-fine medical image registration. Specifically, the registration process is divided into multiple stages, which generate different deformation fields that focus on capturing deformations of varying degrees. To ensure the effective integration of multiple deformation fields, we propose the Deformation Field Component Attention Fusion Module. This module separates the deformation field into three axial displacement components and performs independent attention fusion for the different axial displacement components. Through comparative experiments and ablation studies, we demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： image registration

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