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Lightweight TransUNet Informal Residential Area segmentation 2

Lightweight TransUNet Informal Residential Area segmentation

引用

2nd International conference on Machine Vision, image Processing and Imaging Technology, MVIPIT 2024

作者： Zhang, Shuo Jia, Yiru Liu, Zhaoying Yang, Fei School of Computer science Beijing University of Technology Beijing China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331543037

Remote sensing image informal residential area segmentation plays an important role in urban planning, environmental detection, and disaster assessment. With the development of deep learning, the performance of informal residential area extraction has been improved. However, trying to accurately extract the boundaries between different buildings in the same complex is still a challenging task, and at the same time, segmentation model training takes a lot of time. To address the above problems, we propose a deep learning-based segmentation method for remote sensing images of informal residential area instances, aiming to further improve the accuracy of informal residential area segmentation. This paper has the following three main contributions. First, this paper designs and implements a remote sensing image informal residential area instance segmentation method called TransUNet-IRA, which is a model based on TransUNet, and reduces the training file weights by reducing the number of encoder layers and changing the format of the dataset, which increases the computing speed and improves the performance of the model. Second, we combine DiceCE Loss with IoU Loss to form DiceCE-IoU Loss to further improve the model’s performance in informal residential area segmentation. Finally, we collect a large amount of meter-level remote sensing image data from Guilin City, Guangxi Province, and Chenzhou City, Hunan Province, and construct a remote sensing image informal residential area instance segmentation dataset for these two regions based on these remote sensing images, which is annotated with three segmentations of the background, informal residential area, and single building in the formal residential area. After experimental validation, our method demonstrates performance advantages on the constructed dataset, not only outperforming classical models such as UNet, ResUNet, AttUNet, and SwinUNet but also improving on the original TransUNet. ©2024 IEEE.

关键词： image segmentation

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Salt Deposits Identification Using Enhanced U-net-based Mixed Attention Loss Function for Semantic Segmentation 2

Salt Deposits Identification Using Enhanced U-net-based Mixe...

引用

2nd International conference on data Science and Information System, ICDSIS 2024

作者： Anitha, D. Al-Attabi, Kassem Priyanka, M.S. Soujanya, Thota Bhuvaneshwarri, I. Srm Institute Of Science And Technology School Of Computing Department Of Computational Intelligence Tamilnadu Kattankulathur India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of AI-ML Bengaluru India Sr University School Of Agricultural Sciences Telangana Warangal India Government College Of Engineering Department Of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350381665

Numerous areas of Earth's surface with huge accumulations of oil and gas have high salt deposits under surface. Determining such deposits assists several countries in enhancing the storage capacity of petroleum reserves and exploring new ones. However, determining such a deposit is challenging because expert seismic images need human interpretation of salt bodies, which causes biased and greatly variable translations. This research proposes the Enhanced U-net with Mixed Attention Loss Function (EU-net-based MALF) to automatically and accurately identify whether or not the seismic images have salt deposit regions. Initially, the images are gathered from Tomlinson Geophysical Services (TGS) dataset to evaluate the model performance. Then, the nearest neighbor interpolation method and min-max normalization are employed for image resizing and normalizing. At last, the EU-net-MALF is performed for semantic segmentation. The EU-net-MALF achieves a better accuracy of 98.26% compared to existing methods like fully convolutional DenseNet, U-Net and deep supervision, and Interactive Salt Segmentation based on Convolutional Neural Network and GraphCut (SaltISCG). © 2024 IEEE.

关键词： image enhancement

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Enhanced Melanoma Detection using a Fine-Tuned EfficientNetV2-L Model on Dermoscopic images 2

Enhanced Melanoma Detection using a Fine-Tuned EfficientNetV...

引用

2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Nasra, Parul Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Melanoma, a severe type of skin cancer, spreads rapidly and makes detection and treatment rather tricky. Improving patient outcomes requires precise and prompt detection. Deep learning methods have shown potential recently in automated melanoma classification using dermoscopic images. This work uses the Melanoma Cancer image dataset to examine the utilization of a pre-trained EfficientNetV2-L model for melanoma classification. Renowned for its scalable and efficient design, EfficientNetV2-L offers modern performance for a variety of image categorization purposes. Through an extensive, annotated dataset of dermoscopic images, the EfficientNetV2-L model was refined via transfer learning to differentiate benign lesions from malignant melanoma. The training strategy included preprocessing, augmentation, and data optimization, aiming to increase generalization and model accuracy. Models were evaluated with respect to accuracy, Precision, Recall, and F1-score. Our results reveal that the revised EfficientNetV2-L model considerably raised the accuracy of melanoma classification compared to other deep learning models and traditional methods. According to the study, the model was accurate, generally at 96% overall. EfficientNetV2-L helps dermatologists in early and accurate melanoma detection, making better clinical decisions and enhancing patient outcomes. The results highlight the necessity of ongoing research in this domain and assist the increasing amount of data validating the utilization of advanced deep learning models in medical image processing.

关键词： Automated diagnosis Skin cancer Dermoscopic images Melanoma EfficientNetV2-L Deep learning and Medical image analysis

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A Hyper-Graph Embedded Bandlet-Based Facial Emotion Monitoring System for Enhanced Urban Health 2

A Hyper-Graph Embedded Bandlet-Based Facial Emotion Monitori...

引用

2nd International conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Swarup Kumar, J.N.V.R. Vignesh, Modepalli Manoj, Pera Siva Rao, I.S. Babu, Mahesh Mutyala, Ramu Gitam Deemed to Be University Gitam School of Technology Dept. of Cse Visakhapatnam India Gudlavalleru India

ISBN: (纸本)9798350348002

The state of health of a person can affect their facial expressions. As a result, a system that recognizes facial expressions can be beneficial for healthcare services. In this study, a Facial-Expression Recognition system has been developed to improve healthcare in smart cities by extracting features from a face image through a bandlet transform and Center-Symmetric Local Binary Pattern (CS-LBP). The most prominent features are selected using a Feature-Selection algorithm and then provided to two classifiers, Gaussian mixture model and support vector machine, to determine the facial expression with a confidence score that is calculated from the combined ratings of the classifiers. The proposed system has been tested with large data sets and found to have an accuracy of 99.5% in identifying facial expressions. © 2023 IEEE.

关键词： image enhancement

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Depth image Restoration for High-Level Vision Tasks 2

Depth Image Restoration for High-Level Vision Tasks

引用

2nd International conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Gao, Ying Song, Shaojing Ding, Xie Wang, Yu Sun, Xiang Zheng, Shaoqiu School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Wonders Information Co. Ltd Data Intelligence Product Department Shanghai China School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350360363

High-level computer vision tasks often necessitate large-scale, high-quality depth images. However, real-world depth images are unsuitable for direct application in experimental studies due to their inherent noise and presence of holes. Consequently, image restoration becomes imperative. Large-scale hole repair often proves ineffective in the face of realistic and complex degraded scenes. To address this issue, our research introduces the fast Fourier convolution and a Multi-Scale Attention mechanism into an unsupervised learning network. This integration expands the network's sensory field and enhances perception of spatial feature details within the depth image. Its objective is to improve the restoration performance of depth images that possess substantial regions of missing depth values. Our experimental results demonstrate significant enhancements achieved by our restoration algorithm. Specifically, relative to the original depth images, the algorithm improves PSNR and SSIM by 25% and 60%. On average, improvements exceed the baseline by 9% and 15%, respectively. Moreover, our algorithm outperforms the original depth images in target detection, with the average precision values for human pose recognition tasks increasing by 24% and 25%, respectively. Compared to the baseline, these AP values experience additional enhancements of 11% and 18%, respectively. The restoration algorithm presented in this paper not only achieves visually pleasing results in terms of image quality but also significantly enhances the performance of downstream advanced vision tasks. © 2023 IEEE.

关键词： image reconstruction

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Optimization of Vehicle Blind Zone Monitoring (BSD) Evaluation Scheme Based on image Processing and Radar Ranging Algorithm 2

Optimization of Vehicle Blind Zone Monitoring (BSD) Evaluati...

引用

2nd IEEE International conference on Electrical Engineering, Big data and Algorithms, EEBDA 2023

作者： Boxu, Zhang Cheng, Zhang Libin, Zhu Meixia, Lin Co. Ltd. Changzhou China

ISBN: (纸本)9781665462532

In recent years, the country has gradually paid attention to the intelligent and connected automobile industry, from emergency driving to the current auxiliary driving, the blind spot detection system is also gradually upgraded. For the early warning BSD system, how to have a better alarm signal definition and a higher accuracy is the basis of upgrade direction. In the actual driving process, the change in road conditions and the driver's negligence make the BSD system has the upgrade space. First of all, according to the BSD test of GB / T 39265-2020, evaluates its rationality and propose an improvement plan. The evaluation scheme of key functions that can be applied to blind spot monitoring in L2 driving assistance is designed to effectively guarantee the further standardization and upgrading of the blind spot detection system. © 2023 IEEE.

关键词： Automotive industry

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Highly Secure Lsb-based image Steganography with Four-factor Security 2nd

Highly Secure Lsb-based Image Steganography with Four-factor...

引用

2nd International conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Sreenidhi, A. Shruti, B. Divya, Ambati Subhashini, N. Muthulakshmi, S. School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819754113

In view of rapidly evolving technology, there comes a fair share of vulnerabilities, and a greater need for protection of confidential data. Although encryption techniques are popular to make sensitive data indecipherable, the same can be extracted if the appropriate key is obtained. Steganography, however, aims to keep the very existence of the message hidden from prying eyes. It is the art of secretly writing sensitive text inside an image or an audio file in such a way, that anyone viewing the image would not see any difference between the original and modified image. However, neither technique is perfect or secures enough on sensitive data. The four factors in focus are the Rail Fence Cipher, RSA Encryption, Aztec Code, and LSB Steganography. The data will be passed one by one through all the processes, and the output from one will be passed into the next, thus providing the user with four-fold security. Once the final encrypted data in the form of an Aztec code is hidden inside the cover image, it will be visually indistinguishable from the original image, since only the least bits of the image has been altered to hide the image. This method is more secure than conventional methodologies since even if one factor is compromised, the sensitive data remains encrypted and undecipherable to all malicious intent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aztec code image Steganography LSB steganography Rail Fence Cipher RSA Encryption

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image Matching Based on Fast PCA-SIFT Descriptors with Automatic Determination of Dimensionality for PCA 2

Image Matching Based on Fast PCA-SIFT Descriptors with Autom...

引用

2nd IEEE International conference on Information Communication and Software Engineering (ICICSE) / 7th International conference on Mathematics and Artificial Intelligence (ICMAI)

作者： Zheng, Yi Zheng, Ping Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China

ISBN: (纸本)9781665482202

image matching is a key technology in the field of image processing. An effective image matching method based on fast PCA-SIFT descriptors is proposed and studied deeply. Firstly, the matrix left multiplication method is used to reduce the operation load of PCA and improve its operation speed. Secondly, we utilize the total interpretation proportion of the data variance of the top several principal components to determine the optimal dimension of the descriptor for PCA, thus the number of retained principal components can be determined automatically. Some intuitive and persuasive simulation experiments are carried out by using the proposed method. Experimental results demonstrate that the proposed method can automatically determine the number of retained principal components, and can reduce the operation load of PCA. The proposed image matching method can be used in the fields of three-dimensional reconstruction, cooperative augmented reality and teleoperation robots.

关键词： image processing image matching SIFT PCA dimensionality reduction automatic determination of dimensionality

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Full Reference image Quality Assessment (FR-IQA) of Pre-processed Structural Magnetic Resonance images 2

Full Reference Image Quality Assessment (FR-IQA) of Pre-proc...

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2nd IEEE International conference on Contemporary Computing and Communications, InC4 2024

作者： Swainson Sujana, D. Peter Augustine, D. Sheefa Ruby Grace, D. Christ Deemed to be University Department of Computer Science Bangalore India Sarah Tucker College Department of Computer Science Tirunelveli India

ISBN: (纸本)9798350383652

Deep learning-based Artificial Intelligence algorithms have surpassed human-level performance in many fields including medicine. Specifically in diagnosis using radiology images, deep neural networks empowered AI to excel by educating intricate nonlinear relationships which is a core part of the complicated radiology problems. However, these models require a massive amount of quality data for training. The accuracy of the deep learning model is based on the amount of training data and the quality of the trained data being fed. So, preprocessing the data from different capturing devices is inevitable. This study aimed to highlight some of the image quality metrics that can be used to quantify the efficiency of the chosen preprocessing pipeline. By quantifying the result of each preprocess step, the user can choose an optimal set of preprocesses that can greatly improve the image quality, leading to a high and accurate diagnosis through a deep learning model. Thus, this study detailed how the full reference image quality metrics can be used to validate the performance of sMRI preprocess tasks. © 2024 IEEE.

关键词： Radiology

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Colon Polyp image Segmentation Method Based on CNN and Transformer 2

Colon Polyp Image Segmentation Method Based on CNN and Trans...

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

作者： Zhao, Hong Mi, Shan An, Ding Tian, Weiquan Lanzhou University of Technology Lanzhou China Cccc Big Data Technology Co. Ltd. Lanzhou China

ISBN: (纸本)9798350360240

To solve the problem of low accuracy in traditional pure convolutional neural network models for intestinal polyp segmentation images, we propose a dual-branch multi-scale intestinal polyp segmentation model based on CNN and BiFormer. The model primarily consists of two main components: an encoder and a decoder. During the encoding stage, BiFormer employs a dual-layer routing attention mechanism as the multi-scale feature extractor in the encoder branch to capture global information. Simultaneously, a convolutional block is designed as the convolutional encoder branch to extract local semantic information and detailed features of polyp images. Additionally, an Enhanced Spatial Attention (ESA) mechanism is introduced in the convolutional block of each encoder layer to effectively utilize local spatial information and enhance the spatial localization accuracy of polyps, thereby further improving the model's segmentation performance. In the decoding stage, a Skip Fusion Block (SFB) is employed to comprehensively integrate semantic features from both the encoder and decoder, addressing issues such as discontinuous boundary segmentation and further enhancing segmentation accuracy. Our method was evaluated on the open-source datasets Kvasir-SEG, CVC-ClinicDB, and CVC-ColondB, achieving Dice coefficients of 0.888, 0.927, and 0.911, respectively, and mean Intersection over Union (mIoU) scores of 0.876, 0.902, and 0.860, respectively. Compared to traditional convolutional neural network-based models for intestinal polyp segmentation, our method demonstrates improvements of 2.1% in Dice coefficient and 2.5% in mIoU, highlighting its higher practical value. © 2024 IEEE.

关键词： Decoding

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