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Adaptive Color Optimization Algorithm Based on machine vision 2

Adaptive Color Optimization Algorithm Based on Machine Visio...

引用

2nd IEEE International conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Yang, Ning Jiang, Tianyu Sun, Xin College of Art and Design Changchun Humanities and Sciences College Jilin Changchun China

ISBN: (纸本)9798350372892

With the rapid development of computer technology, there are more and more electronic products such as smart phones, digital cameras, cameras and other kinds of electronic products, and people can easily obtain a variety of images for recording their life and work. However, due to the limitations of photographic technology, there is often a certain gap between the images people obtain and the reality of the scene, such as underexposure, overexposure, low contrast and so on. Therefore, how to adjust the image to improve the visual effect of the image has become a popular topic. The innovation of this paper is to improve the computational efficiency of the algorithm under the premise of ensuring image color clarity, contrast and sharpness, so as to achieve fast and efficient intelligent optimization of image color. The experimental results showed that the algorithm could achieve good color optimization effects in different scenes and image types, and had strong practicality and promotion value. The Mean Shift algorithm had a processing time of 0.4-0.55ms for image color optimization, which was relatively efficient. © 2024 IEEE.

关键词： adaptive color optimization machine vision mean shift algorithm RGB space

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Ovarian Tumor Ultrasound image Segmentation with Deep Neural Networks

Ovarian Tumor Ultrasound Image Segmentation with Deep Neural...

引用

iranian machine vision and image processing (MVIP)

作者： Mahnaz Siahpoosh Maryam Imani Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

The precise and automated segmentation of ovarian tumors in medical images plays a pivotal role in the treatment of ovarian cancer in women. U-Net has demonstrated remarkable success in the field of medical image segmentation. However, due to its small receptive field, U-Net faces challenges in extracting global context information. Moreover, due to the significant variation in scale and size among tumors, it is essential to employ a network capable of effectively extracting information at Multiple scales. In this study, we present a U-Net-based network named PCU-Net for the segmentation of ovarian tumors, incorporating ConvMixer and Pyramid Dilated Convolution (PDC) modules. The ConvMixer module captures global context information by utilizing large-size kernels. The PDC module integrates local and global contextual patterns through utilization of parallel dilated convolution with different dilation rate. Furthermore, our model has fewer parameters than U-Net. We assess the proposed method’s performance using the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) dataset. The results indicate that in comparison to U-Net, our proposed PCU-Net exhibits an improvement of 4.23% in terms of Intersection over Union (IoU) and 2.99% in terms of Dice Similarity Coefficient (DSC).

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Research on Appearance Quality of Nectarine Based on machine vision

Research on Appearance Quality of Nectarine Based on Machine...

引用

International conference on image processing, Computer vision and machine Learning (ICICML)

作者： Jin, Du Yan, Yang Panzhihua Univ Sch Intelligent Mfg Panzhihua Sichuan Peoples R China

ISBN: (纸本)9781665464680

Aiming at the problem of complex surface defect types of nectarines and low efficiency of manual sorting, a surface defect detection method based on pseudo color color space features is proposed. First, image acquisition is carried out for nectarines to be detected through the image acquisition platform, and the acquired image is denoised by linear gray-scale transformation and bilateral filtering. Then, the principle of gray-scale image color space conversion is used to transform it into pseudo color color space, and the pseudo color enhancement method is adopted to further increase the discrimination between defects and non defects, and the defect area is obtained by otsu threshold segmentation. Finally, the influence of non defect area is removed by mathematical morphology processing. The experiment is simulated in MATLAB software, and the experimental results show that the method has high recognition and segmentation ability for surface defects of nectarines of various quality levels.

关键词： machine vision Nectarine Pseudo color processing Defect identification image segmentation

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Research on Visual Recognition of Honeysuckle Based on an Improved YOLO v5 Algorithm 5

Research on Visual Recognition of Honeysuckle Based on an Im...

引用

5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Shao, Gang Xiong, Haoyu Qin, Jianshuang Li, Guimin Jiang, Yulian Southwest Minzu University College of Electrical Engineering Chengdu China Southwest Minzu University College of Electronic and Information Chengdu China

ISBN: (纸本)9798350374407

With the continuous evolution of machine vision technology, its application in agriculture, forestry, and other fields has become one of the focal points of research. This paper proposes an improved model based on machine learning specifically for the visual recognition of honeysuckle. Firstly, by constructing and optimizing the dataset, and utilizing Baidu PaddlePaddle BML for training set creation and fine-tuning, the model is enabled to better learn and understand the characteristics of honeysuckle. Secondly, advanced image preprocessing techniques, including bilateral filtering and the RGB color space model, are employed to enhance the segmentation precision and accuracy of honeysuckle. Subsequently, an innovative Coordinate Attention (CA) mechanism is introduced, allowing the model to focus more on pixel-level important information, thereby improving the recognition performance of honeysuckle. Experimental results demonstrate that the proposed algorithm achieves remarkable performance in the task of honeysuckle recognition, with an average precision of $\mathbf{7 8. 6 \%}$, representing a $\mathbf{1 0. 1 \%}$ improvement compared to the unmodified algorithm. This ensures high-speed recognition based on YOLO v5 while effectively enhancing the algorithm's robustness. © 2024 IEEE.

关键词： machine vision

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Boundary Alignment Loss-Guided Cross-Modal Attention Network for Referring image Segmentation 7

Boundary Alignment Loss-Guided Cross-Modal Attention Network...

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7th International conference on machine Learning and Natural Language processing, MLNLP 2024

作者： Zhao, Lingyun Sun, Huifang Li, Longfei Sun, Qihang Nan, Wang Gu, Bing Shandong University of Science and Technology TaiAn China

ISBN: (纸本)9798350354973

Referring image segmentation is an advanced computer vision task that aims to accurately segment specific objects in computer vision images using algorithms to understand natural language descriptions. Previous methods rely on explicit human commands to recognize target objects or categories, and lack the ability to actively reason the thin object boundaries, degrading segmentation performance. To solve this problem, this paper proposes a boundary alignment loss-guided cross-modal attention network for referring image segmentation, namely BACNet. Technologically, we firstly introduce a cross-modal attention coupling module (CACM) to realize the bidirectional interaction between language and visual features, effectively exploring the fine-grained information across different modalities. Then, we design a boundary alignment loss (BAL) to refine segmentation boundaries through a differentiable direction vector prediction within an end-to-end framework, significantly enhancing the accuracy of prediction map in aligning with true object boundaries. Specifically, by guiding the Predicted Boundary Descriptors (PDBs) towards the Ground Truth Boundaries (GTBs), BAL dynamically adapts during training, eliminating the need for explicit boundary detection and progressively improving the model performance. Experiments demonstrate the effectiveness of the proposed method on challenging referring image segmentation datasets, exhibiting higher segmentation quality. © 2024 IEEE.

关键词： image segmentation

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Deep Graph Convolutional Neural Network based on Adaptive Sampling and Aggregation Strategy 2

Deep Graph Convolutional Neural Network based on Adaptive Sa...

引用

2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Wang, Xueli Zhang, Wei Li, Gege Zhou, Lin Liu, Hongkai Ye, Zhonglin School of Computer Qinghai Normal University Xining810008 China Qinghai Provincial Key Laboratory of Tibetan Information Processing and Machine Translation Xining810008 China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki851-0193 Japan

ISBN: (纸本)9798331543037

Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and usually only consider direct neighborhood. When sampling high-order neighborhood, it is easy to produce over-smoothing, which affects the model performance and accuracy. Therefore, this paper proposes an adaptive sampling and aggregation deep graph convolutional network (Adaptive Sampling and Aggregation Depth Graph Convolutional Network, ASA-GCN). A sampling strategy based on node importance is designed to adaptively sample high-order neighbors of nodes. Meanwhile, an aggregation strategy based on self-attention mechanism is proposed to adaptively aggregate node information within a given neighborhood. Finally, experimental results on multiple benchmark graph datasets show that the model has superior performance on the node classification task. ©2024 IEEE.

关键词： Convolutional neural networks

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Research on Size Measurement Method of Underwater Parts Based on machine vision 3

Research on Size Measurement Method of Underwater Parts Base...

引用

2024 3rd International conference on Advances in Modern Physics Sciences and Engineering Technology, ICPSET 2024

作者： Zhong, Chaochun Ma, Qingliang Ma, Tingting Guangzhou Institute of Metrology and Testing Technology Guangzhou China

In order to measure the deformed parts under pressure, a size measurement method of underwater parts based on machine vision is proposed, whereas the relative position between the camera and target parts remains basically unchanged. Firstly, the imaging theory of the underwater camera is analyzed, and the mathematical relationship between the size of the target object and the number of pixels is established;Then, the underwater parts measurement system based on machine vision is built, and the image processing algorithm is studied to extract the target size;Thirdly, the genetic algorithm is studied to calibrate the camera measurement system;Finally, the relevant experiment is conducted on the underwater parts measurement system. Through the analysis of the results, the measurement uncertainty of the system is 0.0355mm, and the relative error is 0.1%. The underwater parts measurement system has practical value. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Underwater cameras

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A Method of Infrared image Mosaic Based on Keyframe Matching

A Method of Infrared Image Mosaic Based on Keyframe Matching

引用

2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

作者： Shao, Jianming Yu, Ya Xu, Zheng Ye, Chen Jia, Dachang Yan, Minghao Jiangsu Electric Power Company Wuxi Power Supply Company Wuxi China

ISBN: (纸本)9798350306545

Infrared images usually have a narrow field of view, requiring splicing of multiple image sequences to meet the application requirements of wide field of view and high resolution. However, due to the large overlap between adjacent frame images in infrared image stitching, it can lead to accumulated stitching errors and increased computational costs. An infrared image mosaic method based on keyframe matching is proposed. Firstly, key frames are selected as the stitching object based on the similarity of image statistical vectors. Then, the SURF algorithm is used for feature extraction and image registration. Finally, three typical image fusion methods are used to complete panoramic image stitching. The experiments results based on on infrared image datasets show that this method can significantly reduce computational complexity while ensuring the quality of stitching. © 2023 IEEE.

关键词： Feature extraction

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Layer-by-layer Learning In image processing For Unmanned Aerial Vehicle 7

Layer-by-layer Learning In Image Processing For Unmanned Aer...

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7th IEEE International conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Sineglazov, Victor Chynnyk, Petro NTUU Igor Sikorsky Kyiv Polytechnic Institute Dept. of Artificial Intelligence Kyiv Ukraine

ISBN: (纸本)9798331534141

This work explores an innovative approach to image processing that provides high efficiency and accuracy in computer vision tasks. In this work, step-by-step learning of quantum machine learning models is considered, in which each layer of the model specializes in specific functions, which significantly improves the quality of image recognition and classification. Special attention is paid to the adaptation of this approach to real-time conditions important for UAVs, where the processing of large volumes of data must take place under conditions of limited resources. The results of experiments are presented, which demonstrate the advantages of step-by-step learning in comparison with traditional methods of image processing, which allows to achieve higher accuracy and speed of data analysis during various tasks, such as monitoring, mapping and object detection in the field. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Training Auxiliary Prototypical Classifiers for Explainable Anomaly Detection in Medical image Segmentation 23

Training Auxiliary Prototypical Classifiers for Explainable ...

引用

23rd IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： Cho, Wonwoo Park, Jeonghoon Choo, Jaegul IKAIST Daejeon South Korea Letsur Inc Seoul South Korea

ISBN: (纸本)9781665493468

machine learning-based algorithms using fully convolutional networks (FCNs) have been a promising option for medical image segmentation. However, such deep networks silently fail if input samples are drawn far from the training data distribution, thus causing critical problems in automatic data processing pipelines. To overcome such outof-distribution (OoD) problems, we propose a novel OoD score formulation and its regularization strategy by applying an auxiliary add-on classifier to an intermediate layer of an FCN, where the auxiliary module is helfpul for analyzing the encoder output features by taking their class information into account. Our regularization strategy train the module along with the FCN via the principle of outlier exposure so that our model can be trained to distinguish OoD samples from normal ones without modifying the original network architecture. Our extensive experiment results demonstrate that the proposed approach can successfully conduct effective OoD detection without loss of segmentation performance. In addition, our module can provide reasonable explanation maps along with OoD scores, which can enable users to analyze the reliability of predictions.

关键词： Training image segmentation machine learning algorithms Pipelines Training data Network architecture Data processing

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