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Infrared dim and small target detection based on total variation and multiple noise constraints modeling 22

Infrared dim and small target detection based on total varia...

引用

4th international conference on image processing and Machine vision, IPMV 2022

作者： Wang, Xiaowen Xia, Xiaoyan Li, Qiao Xue, Wei School Of Computer Science And Technology Anhui University Of Technology China

ISBN: (纸本)9781450395823

To improve the ability of infrared dim small target detection algorithm based on traditional infrared patch-image (IPI) model, a new detection model based on total variation and multiple noise constraints is proposed. We firstly transform the original infrared image into an IPI, and then the total variational regularization constrains the background patch-image in order to reduce the noise on the target image. In the meantime, the edge information of the image can be preserved to avoid excessive smoothness of the restored background image. Additionally, considering the lack of noise distribution in the patch-image, the combined and norm are introduced to describe the noise more accurately. the experimental results show that the proposed method can suppress the background clutter better and improve detection performance effectively. © 2022 ACM.

关键词： image enhancement

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Building and evaluation of an Algerian Cultural Heritage dataset using convolutional neural networks 4

Building and evaluation of an Algerian Cultural Heritage dat...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Djelliout, Toufik Aliane, Hassina University of Bejaia Algeria Faculty of Exact Sciences Department of Computer Science Bejaia Algeria University of Bejaia Algeria Research Center for Scientific and Technical Information Algiers Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Preservation of cultural heritage is a field of high importance. Recently people are sharing architectural monument images on social media. In this paper, we try to recognize architectural monuments in digital photographs of Algerian cultural heritage using a convolutional neural network (CNN). As no datasets support the diversity of Algerian monuments, adapted for CNN training, we built a new dataset and made available to the public. AlgHeritage dataset consists of 20,000 images that can serve as a benchmark for various research fields, as it combines numerous real-world challenges. We evaluate our dataset with three CNN models MobileNetV3, InceptionV3 and InceptionResNetV2, and compare it with two other datasets. MobileNetV3 with fine-tuning produces a good accuracy of 93.29% on the AlgHeritage dataset in comparison with other datasets. the AlgHeritage dataset is available at https://***/3O38FOe. © 2022 IEEE.

关键词： image recognition

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A survey on fish health monitoring approaches using computer vision techniques

A survey on fish health monitoring approaches using computer...

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Computing and Communication Systems (I3CS), international conference on

作者： Earningstone R Sangma Goutam Saha Department of Information Technology North Eastern Hill University Shillong Meghalaya India

Sustainable automation in lish culture is still a prominent research issue having immense societal benelits. One of the pertinent issues in this automation process is lish health monitoring using state-of-the-art computer vision techniques. Here, the process involves underwater video image processing, which is still a pertinent area of research to date. there are several approaches by past researchers in this area using computer vision techniques. In this paper, an endeavor has been initiated to discuss some of those research initiatives. A comparison of performance involving these techniques have been discussed here. From those studies, some of the pertinent future scopes of research also have been highlighted.

关键词： Surveys computer vision Automation Communication systems image processing Fish Research initiatives

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Improving Accuracy of image Clustering Using Convolutional Neural Network and Learning from Confusion 28th

Improving Accuracy of Image Clustering Using Convolutional N...

引用

28th international conference on image processing, computer vision, and pattern recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Mokter, Md Farhad Oh, Jung Hwan Department of Computer Science and Engineering University of North Texas DentonTX United States

ISBN: (纸本)9783031859328

image clustering is a very useful but difficult task in machine learning and computer vision. Recently, convolutional neural network (CNN) is taking a major role in image clustering in which it is used to extract features to be used for an objective function of similarity or dissimilarity measures. there are two problems in the image clustering methods using CNN. First, it is still challenging to estimate a correct number of clusters initially, which decides the quality of the clustering result mostly. Second, a clustering algorithm frequently clusters some images with semantically different contents, into a same cluster (due to their similar color distributions) and generates incorrect clustering result. In this paper we propose a framework for image clustering with CNN and learning from confusion, which can address these problems. the experiments show that the proposed framework increases image clustering effectiveness significantly. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Multi-scale Features Fusion Network for Single image Deraining 10

Multi-scale Features Fusion Network for Single Image Deraini...

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10th IEEE Joint international Information Technology and Artificial Intelligence conference, ITAIC 2022

作者： Lai, Yanming Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University Ministry of Education Key Laboratory of Nondestructive Testing Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Single image rain removal is an important research direction in the field of computer vision. In this paper, the Multi-scale Features Fusion Network (MFFN) is presented for rain removal. MFFN is mainly composed of Multi-features Fusion Module (MFM) and Dual Attention Module (DAM). In the MFM, we make the improved dense block and the dilation convolutions to form the feature extraction branches, which is conducive to improve the receptive fields of network. Subsequently, the bottom-up connection method is adopted between branches to help different branches make full use of image information. then, feature branches are merged to fusion different features. DAM is consisted of purposed SA Block and standard SE Block. the purpose of DAM is to reduce the non-rain features which is extracted by the network. Experiments show that MFFN can obtain the better result of rain removal than several advanced methods. © 2022 IEEE.

关键词： computer vision

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IT and I 2022 - Selected Papers of the 4th international Scientific conference "Information Technology and Implementation". conference Proceedings

IT and I 2022 - Selected Papers of the 4th International Sci...

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4th international Scientific conference "Information Technology and Implementation", IT and I 2022

the proceedings contain 34 papers. the topics discussed include: enhancement of information technology for person identification based on image quality features;modified neural network method for trend analysis of helicopter turboshaft engine parameters at flight modes;method of dynamic stock buffer management based on a connectionist expert system;decision support system for the safety of ship navigation based on optical color logic gates;development of an automatic system for monitoring the integrity of power cables using quasi-distributed fiber-optic sensors;anomalies analysis and detection using computer vision for finding defects in plant leaves images;investigation of artificial intelligence methods in the short-term and middle-term forecasting in financial sphere;recognition and classification apple fruits based on a convolutional neural network model;and optimization of marketing decisions based on machine learning: case for telecommunications.

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Low Light image Enhancement Using Autoencoder-Based Deep Neural Networks 28th

Low Light Image Enhancement Using Autoencoder-Based Deep Neu...

引用

作者： Chen, Kuei-Yu Leou, Jin-Jang National Chung Cheng University Chiayi621 Taiwan

ISBN: (纸本)9783031859328

In this study, a low light image enhancement approach using autoencoder-based deep neural networks is proposed. the proposed approach consists of two subnets, namely, feature extraction (FE) subnet and color enhancement (CE) subnet. FE subnet extracts image features from the low light image and enhances detailed textures, while CE subnet recovers the color information of the low light image and performs image denoising. A pre-processing technique, namely, relative global histogram stretching (RGHS) is employed in CE subnet. Finally, the processing results of FE and CE subnets are fused to generate the final enhanced image. Based on the experimental results obtained in this study, in terms of two objective performance metrics (PSNR (dB) and SSIM) and subjective evaluation, the performance of the proposed approach is better than those of five comparison approaches. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Research on image recognition based on neural network model learning algorithm

Research on image recognition based on neural network model ...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Shuxuan Feng Jun Lu Heilongjiang University Harbin China

the model based on object detection algorithm shows great advantages in accuracy and precision rate. However, due to the different sizes of the targets to be detected in the images, coupled with the interference of factors such as occlusion and scene complexity, at the same time, the object detection has the problem of too small a percentage, which is prone to miss detection and false detection. therefore, the performance of the model needs to be further improved. In this paper, based on the shortcomings of existing models, we propose the Invo-YOLOv5s model, which can well improve the accuracy of detection and anti-interference ability. We conducted experiments on the model on the helmet dataset, and the experiments showed that the Invo-YOLOv5s model was selected for training, and the final obtained model detection accuracy reached 94.9%, which is 2.3% higher than the accuracy of the original YOLO model.

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Research Study on Leukemia Detection Using Deep Learning Techniques 4th

Research Study on Leukemia Detection Using Deep Learning Tec...

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Maram, Rohan Raj Pappu, Sasibhushana Rao Satish, Aravapalli Rama Maram, Balajee Chitkara University HP Baddi India GITAM School of Technology Visakhapatnam India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Vijayawada India Apex Institute of Technology—Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9789819942831

Leukemia is a type of cancer found in blood, which commonly starts within the bone marrow and impacts the capacity of the body to combat infection. In a country like India, less than 1000 Lakh instances having leukemia are stated each time. Several of the not uncommon place signs and symptoms are pores and membrane outbreaks, hemorrhage, sensing tiredness, infection, and accelerated hazard of infections. It could be extremely critical to come across leukemia in the initial phases. Conventional techniques, along with limited studies on plasma stains, for detecting Leukemia are no longer cost-effective and rely heavily on skilled scientific personnel. To overcome these limitations, this study proposes the implementation of automated algorithms using image processing and the MATLAB software for the recognition and categorization of Leukemia. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： MATLAB

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Hierarchical Iris image Super Resolution based on Wavelet Transform 22

Hierarchical Iris Image Super Resolution based on Wavelet Tr...

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4th international conference on image processing and Machine vision, IPMV 2022

作者： Xia, Yufeng Li, Peipei Wang, Jia Zhang, Zhili Li, Duanling He, Zhaofeng Automation School Beijing University Of Posts And Telecommunications China Artificial Intelligence School Beijing University Of Posts And Telecommunications China Computer Science School Beijing University Of Posts And Telecommunications China

ISBN: (纸本)9781450395823

Iris images under the surveillance scenario are often low-quality, which makes the iris recognition challenging. Recently, deep learning-based methods are adopted to enhance the quality of iris images and achieve remarkable performance. However, these methods ignore the characteristics of the iris texture, which is important for iris recognition. In order to restore richer texture details, we propose a super-resolution network based on Wavelet with Transformer and Residual Attention Network (WTRAN). Specifically, we treat the low-resolution images as the low-frequency wavelet coefficients after wavelet decomposition and predict the corresponding high-frequency wavelet coefficients sequence. In order to extract detailed local features, we adopt both channel and spatial attention, and propose a Residual Dense Attention Block (RDAB). Furthermore, we propose a Convolutional Transformer Attention Module (CTAM) to integrate transformer and CNN to extract both the global topology and local texture details. In addition to constraining the quality of image generation, effective identity preserving constraints are also used to ensure the consistency of the super-resolution images in the high-level semantic space. Extensive experiments show that the proposed method has achieved competitive iris image super resolution results compared with the most advanced super-resolution method. © 2022 ACM.

关键词： Wavelet decomposition

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