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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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On the Influence of image Features on the Performance of Deep Learning Models in Human-Object Interaction Detection

On the Influence of Image Features on the Performance of Dee...

引用

International conference on Computer Vision and Graphics, ICCVG 2022

作者： GrzĄbka, Marcin Iwanowski, Marcin Sarwas, Grzegorz Institute of Control and Industrial Electronics Warsaw University of Technology ul. Koszykowa 75 Warszawa00-662 Poland

ISBN: (纸本)9783031220241

In recent years Human Object Interaction (HOI) detection has experienced rapid performance growth mainly due to the development of various deep learning-based methods and algorithms. One of the most popular approaches to this task is three-stream architecture consisting of three processing paths: human-stream, object-stream, and relation-stream, gathering information on the scene using features extracted from particular image elements. The total amount of features depends on the detailed processing schemes and model parameters. The increase in the number of features does not necessarily increase the final efficiency of the HOI detection method. This paper focuses on the relation between the quantity of features used to detect HOI and its predictive efficiency. In our study, we investigate the influence of the number of features on the final detection results. Several experiments were conducted to validate various model configurations of different features considered. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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Real-time Stereoscopic Display of Atomic Force Microscopy images for Nano Robotic Manipulation 18

Real-time Stereoscopic Display of Atomic Force Microscopy Im...

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18th International conference on Nano/Micro Engineered and Molecular systems, NEMS 2023

作者： Liu, xinyu So, Chun Ho Xue, Yuxuan Wang, Yichen Zhang, Jiawei Lai, King Wai Chiu xi, Ning The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong City University of Hong Kong Department of Biomedical Engineering Hong Kong Shenzhen Academy of Robotics Guangdong Shenzhen518000 China

ISBN: (纸本)9798350333480

We developed a nano-operating system that features a stereoscopic display of AFM images that is updated in real time. In order to accurately judge the positional relationship between the nanoparticles and carry out operations, the operator is able to use this system to not only observe the change process of the nano-environment in real time while the AFM is operating, but also obtain depth information with the help of a real-time stereoscopic display. At the same time, we give the operator with a corresponding perspective in real time by obtaining the changes in the operator's head posture. This helps to increase the sense of presence and improves operational efficiency. © 2023 IEEE.

关键词： Stereo image processing

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An Evaluation of Medical image Analysis using image Segmentation and Deep Learning Techniques for Brain-Tumour 2

An Evaluation of Medical Image Analysis using Image Segmenta...

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2nd International conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sinha, Himanshu Kelley School of Business Indiana University Bloomington NapervilleIL United States

ISBN: (纸本)9798331541217

An aberrant mass of rapidly proliferating brain cells, known as a brain tumour, may develop into various forms of cancer. The segmentation of brain tumours by MRI scans is a challenging but crucial process with several medical analytic applications. Radiologists or clinical specialists are responsible for performing a tedious and time-consuming process that involves identifying, segmenting, and extracting contaminated tumour regions from MRI images. The different anatomical structures of human organs may be imagined via image-processing ideas. The human brain is notoriously difficult for basic imaging techniques to identify abnormal structures. A large number of researchers looked at several rapid and accurate algorithms for brain tumour (BT) detection and classification. Recent advances in DL-based automated systems have allowed for faster and more accurate segmentation of DT. This research makes use of a DenseNet50 model for medical picture classification of brain malignancies that is built on a pre-trained CNN model made possible by DL. The model is available on Kaggle. An optimiser like Adam and a categorical cross-entropy loss function were utilised to train a model. A suggested DenseNet50 model outperformed other comparative advanced DL models and attained an experimental accuracy of 95%. The visual results also highlight the impressive outcomes of the MRI brain tumour segmentation system, which will help doctors automatically detect and treat brain tumours. The results acquired demonstrate the effective identification of tumorous images, which is extremely beneficial for the diagnosis process and future applications. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation 5

A microfluidic system, utilising image processing methods, f...

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Louka, Marinos Passos, Andreas Inglezakis, Antonis Loizou, Constantinos Kaliviotis, Efstathios Cyprus Univ Technol Dept Mech Engn & Mat Sci & Engn Limassol Cyprus EMBIO Diagnost Nicosia Cyprus

ISBN: (纸本)9781665462198

Hemostasis is a defence mechanism that prevents blood losses in cases of vessel injuries, and other related disorders. In many cases, patients need to frequently monitor their blood coagulation tendency in order to regulate their medication. In addition, red blood cell aggregation (RBCA) is related to blood inflammation, and it appears elevated in many pathological conditions. Blood coagulation and RBCA can be studied by analysing the dynamic changes of light transmittance though a clotting/aggregating sample, and indeed various works in the literature exploit this approach. In this work, blood coagulation and RBCA are examined by utilising single drops of blood in an inexpensive camera-based microfluidic system, designed for low computational and production cost. Results are compared with a microscopy-camera system, with both setups utilizing the same custom made microchannel. Three image processing algorithms are developed to analyze the averaged light intensity, and the local structural chracteristics of blood, through a binarization and region classification method, using logical operations. The results illustrate the repeatability of the technique and the donorto-donor variation within the proposed approach. Based on the image processing analysis, the developed coagulation and aggregation indices show great potential of utilisation in an inexpensive and robust point of care device.

关键词： Blood coagulation red blood cell aggregation image processing medical diagnostics POC

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A Fast Heterogeneous image Registration Method Based on the Human Skeleton Points 7

A Fast Heterogeneous Image Registration Method Based on the ...

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7th International conference on image, Vision and Computing, ICIVC 2022

作者： Wan, Duo xiao, Shanzhu College of Electronic Science and Technology National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha410073 China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Visible-infrared composite cameras are widely used in public health monitoring and intelligent security. Fast and accurate registration of visible-infrared images is the key to the further application of the information in different spectra. In the crowd scenario, aiming at the problem of poor consistency of traditional feature points such as corners, spots, and contour points in the two types of images, this paper proposes an effective method of heterogeneous image registration based on human skeleton points. Firstly, the human skeleton points are detected by a multi-person pose estimation network, which represents the common features of visible and infrared images decently. Then, the key points are matched by a top-down method that integrates the spatial information of point groups with the type of each point. Finally, the point pairs are put into the control points buffer and the transformational matrix is calculated. We capture and make a visible-infrared image sequence dataset in a crowd-oriented scene, and conduct qualitative and quantitative tests on it. The experiments show that the designed method outperforms traditional registration algorithms both in terms of accuracy and processing speed. © 2022 IEEE.

关键词： image registration

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Color Terms and Stable Diffusion 32

Color Terms and Stable Diffusion

引用

32st Color and Imaging conference - Color Science and Engineering systems, Technologies, and Applications, CIC 2024

作者： Moroney, Nathan Numantic Solutions United States

ISBN: (纸本)9780892083688

Stable diffusion is a generative algorithm for creating images from text prompts. This paper explores prompts with color terms and proposes a process to generate, visualize and assess these results. Automated prompts are used to generate and render a color term, an object and a context. The results are then evaluated using two dashboard views of the underlying images. First is a sampling based on a collection of frequently used color terms. Second is a sampling by object prompts, such as apples and boxes. This paper considers the following questions: how effectively are the colored objects generated? how do the colors generated by stable diffusion compare to human color naming? How might color terms be useful in visualizing properties and features of generative algorithms? The dashboard view of color terms suggests that less frequently used color terms may be generated less consistently. In addition, even the most common color terms can fail to be correctly generated. Likewise, objects with more frequent color associations, such as apples or pumpkins, will result in less accurate color generation. ©2024 Society for Imaging Science and Technology.

关键词： Color image processing

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An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD 17th

An Adaptive BSCO Algorithm of Solid Color Optimization for 3...

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17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Hsieh, Chao-Hsien Song, Yubo Wang, Zhen Li, Changfeng Qufu Normal Univ Jining 273165 Shandong Peoples R China

ISBN: (纸本)9783031192135;9783031192142

PIFuHD can generate high-resolution model in the process of human 3D reconstruction. However, PIFuHD will produce debris outside the human body when the image background is more complex. In order to solve this problem, this paper develops an adaptive BSCO algorithm for the background of human body and image of human body in 3D reconstruction system. The BSCO algorithm is divided into four steps in processing. First, BSCO algorithm uses Go-selfies to separate the background. Second, BSCO algorithm converges the RGB of all pixels of the character into a set. Third, BSCO algorithm finds the greatest difference from the set through HSV conversion. Fourth, BSCO algorithm weighs the set and then calculates the RGB score. The highest score of RGB is used as the RGB of the background after solid color optimization. The experimental results show that the proposed method improves the reconstruction effect of PIFuHD.

关键词： PIFuHD 3D reconstruction image processing Background adaptive

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Research on Three-Dimensional Point Cloud Reconstruction Method Based on Graph Neural Networks

Research on Three-Dimensional Point Cloud Reconstruction Met...

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image processing and Media Computing (ICIPMC), International conference on

作者： Ruocheng Ma xiang Gao Zhaoxiang Song Technology Center Beijing Qihu Technology Co. Ltd. Beijing China School of Computer Science and Engineering Xi'an Technological University Xi'an China School of Public Administration Northwest University Xi'an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

The widespread utilization of three-dimensional reconstruction technology across various domains such as medicine, architecture, and transportation has led to a growing demand for precise positioning and high-precision modeling of targets. Three-dimensional reconstruction methodologies leveraging deep learning techniques have demonstrated significant advantages. Nonetheless, traditional three-dimensional reconstruction networks often encounter challenges related to loss of intricate image features, low point cloud density, and susceptibility to generating voids, consequently impacting quality and accuracy of three-dimensional reconstructions. To combat this challenge, this study introduces an algorithm based on graph neural networks for dynamically selecting central points as a replacement for the original point cloud enhancement strategy. Initially, the algorithm employs CAS method to identify appropriate central points that cover expanded spatial volumes within their respective neighborhoods. Subsequently, PointFlow algorithm is applied to forecast point clouds at these central points. The resulting point clouds are then refined by integrating original point cloud segments with interpolated point cloud segments, culminating in comprehensive, high-density three-dimensional point clouds representing the target scene. Relative to Point-MVSNet, the algorithm presented in this paper demonstrates a substantial 6.5% reduction in the average error of the reconstructed three-dimensional models. Notably, the resulting point cloud density and fidelity are elevated, showcasing richer detailed features while also exhibiting lower resource consumption compared to alternative three-dimensional reconstruction algorithms.

关键词： Point cloud compression Deep learning Solid modeling Heuristic algorithms Transportation Reconstruction algorithms Prediction algorithms

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Research Advanced in the image Segmentation

Research Advanced in the Image Segmentation

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2022 International conference on Applied Physics and Computing, ICAPC 2022

作者： Zhang, Yifan Deng, Wenqing Zheng, Kai Lu, xiaoyao School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China Lide International High School Nanchang China University of California Los Angeles Department of Mathematics Los Angeles United States Zhejiang Agricultural and Forestry University Department of Computer Science Hangzhou China

ISBN: (纸本)9781665463034

Due to the progress of computer technology, image processing-based applications become more and more important not only in advanced research fields but also in our daily life. For example, biomedical image processing, autonomous vehicles, video surveillance, computer vision, augmented reality, and many more are some hotspots in research as well as the applications. The technology of image segmentation is the basis of all these applications. In this review, we summarize the traditional image segmentation methods and algorithms including segmentation based on thresholding, segmentation based on graph theory, and segmentation based on region. Also, due to the exceptional performance of the CNN model, a series of CNN-based segmentation algorithms were proposed. Along with the invention of these new approaches, segmentation task has been pushed to a higher accuracy and efficiency level. Thus, we also summarize the major CNN-based model, such as the FCN model, the Encoder-Decoder designed architectures, the Multi-scale and Pyramid-based architectures, and Deeplab Family. Moreover, we provide some information on some popular datasets and metrics to rigorously evaluate a model, and we provide tables that contain the quantitative information on each model's performance. Even though the outcomes of current models have reached a significant level, there are still many problems that need to be tackled. At last, we provide our opinion regarding the current situation in image segmentation field. © 2022 IEEE.

关键词： Semantic Segmentation

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A generic model of semiconductor-based photon-counting detectors for spectral CT

A generic model of semiconductor-based photon-counting detec...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Bhattarai, Mridul Panta, Raj Kumar Clark, Darin Segars, W. Paul Abadi, Ehsan Samei, Ehsan Duke Univ Med Phys Grad Program Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Ctr Virtual Imaging Trials CVIT Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Quantitat Imaging & Anal Lab Durham NC 27708 USA

ISBN: (纸本)9781510671553;9781510671546

Photon-counting CT (PCCT) is an emerging x-ray spectral imaging technology. There are several PCCT prototypes available for clinical and experimental applications. Despite many published results, objective quantitative evaluation of PCCT systems remains challenging due to varied and unknown influences. In addition, CT acquisition geometry and image processing mechanisms are often proprietary, complicating the correlations between the detector's physical characteristics and the corresponding CT image quality, hindering fair comparisons between different technologies. This study aimed to provide a resource to address these challenges by developing a versatile model that accurately replicates the physics of any semiconductor-based photon-counting detector (PCD) and integrating it into a virtual imaging framework to generate detector-specific CT images. The methodology involved a Monte Carlo simulation to model x-ray photon interactions with PCDs and an analytical Gaussian charge sharing model to model charge-diffusion and -repulsion in the detector. Finally, the energy deposited post-crosstalk by source photons (1-120 keV) in the 3x3-pixel-neighborhood was translated into photon counts across two energy thresholds to compute detector-specific spatio-energetic covariance correlation matrices. This framework was validated against experimental measurements of a clinical CdTe-based PCCT, showcasing its accuracy in reproducing real-world scenarios. Additionally, to demonstrate the model's efficacy, we simulated key PCD materials (Si, CdTe, CZT) and designed standardized scanner components, developing virtual systems (DukeCounter scanners). These systems were used to "scan" a physics phantom (ACR) and anthropomorphic-computational models (XCAT). In low-energy (i.e., 5 keV for Si, 20 keV for CdTe and CZT) threshold images, for 125 mAs acquisitions, (noise magnitudes, HU for bone insert) measured in ACR phantom were (76.0 HU, 889.4 HU), (76.9 HU, 932.9 HU), (81.7 HU, 939.4 HU)

关键词： Sensors Silicon Computed tomography image quality Monte Carlo methods X-ray computed tomography Scanners systems modeling Reconstruction algorithms Semiconductors Cadmium telluride Photon counting

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