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IS and t International Symposium on electronic imaging 2024: 22nd image processing: algorithms and systems, IPAS 2024

the proceedings contain 34 papers. the topics discussed include: extending lidar depth range using stereo depth estimation on intensity data;transformers for microscopy slide image segmentation of invasive melanoma;intracell capacitances in pixel-level hybrid bond image sensor cells;self-attention enhanced recognition: a unified model for handwriting and scene-text recognition with improved inference;evaluation of subjective video quality of television displays;experimental study on reducing wavelength dependency of spatial resolution characteristics of a digital camera by MtF correction;comparative analysis of user experience in virtual reality (VR) and mixed reality (MR) systems using eye-tracking measurements;RAiv: researchable archives for interactive visualizations;mobile 3D mapping of Erdstall facilities;real-time stereoscopic image-parallel path tracing;automatic reading order sequencing: a novel reading order generator for text-based documents;and improving video deepfake detection: a DCt-based approach with patch-level analysis.

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image processing: algorithms and systems XXI

Image Processing: Algorithms and Systems XXI

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IS and t International Symposium on electronic imaging: 21st image processing: algorithms and systems, IPAS 2023

the proceedings contain 174 papers. the topics discussed include: appearance segmentation and documentation applied to cultural heritage surfaces;end-to-end evaluation of practical video analytics systems for face detection and recognition;testing the role of vision spatial processing in color deficiency;a lightweight exposure bracketing strategy for HDR imaging without access to camera raw;style transfer for improved visualization of underdrawings and ghost paintings: an application to a work by Vincent van Gogh;tangible extended reality with sensor fusion;orca: an end-to-end video object removal framework with cropping interested region and quality assessment;simulation and design of a burst mode 20mfps global shutter high conversion gain CMOS image sensor in a standard 180nm CMOS image sensor process using sequential transfer gates;3D joist perception, detection, and climbing for hexapod robot;synthetic speech attribution using self-supervised audio spectrogram transformer;incidence of stereo blindness in a recent VR distance perception user study;mesh distance for dimension reduction and visualization of numerical simulation data;and teaching color science to EECS students using interactive tutorials: tools and lessons.

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image processing: algorithms and systems XX

Image Processing: Algorithms and Systems XX

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IS and t International Symposium on electronic imaging: 20th image processing: algorithms and systems, IPAS 2022

the proceedings contain 154 papers. the topics discussed include: feature-driven 3d range geometry compression via spatially-aware depth encoding;open source deep learning inference libraries for autonomous driving systems;problems in image target-based color correction;improvement of aerial image by simulations;recognition-aware learned image compression;artist-specific style transfer for semantic segmentation of paintings: the value of large corpora of surrogate artworks;data visualization of crime data using immersive virtual reality;a comparison of non-experts and experts using DSIS method;contrast enhancement: cross-modal learning approach for medical images;a continuous bitstream-based blind video quality assessment using multi-layer perceptron;correspondences for image and video reconstruction;design and analysis on low-power and low-noise single slope ADC for digital pixel sensors;incremental two-network approach to develop a purity analyzer system for canola seeds;advantage of machine learning over maximum likelihood in limited-angle low-photon x-ray tomography;image montage detection based on image segmentation and robust hashing techniques;chatbot integrated with machine learning deployed in the cloud and performance evaluation;and the relationship between vision and simulated remote vision system air refueling performance.

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Development of color correction system for medical images using color charts 4

Development of color correction system for medical images us...

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Ultra-High-Definition imaging systems iv 2021

作者： Kambara, t. Ogino, Y. Maeda, A. Kajimura, Y. DNP Corporation USA 2099 Gateway Place Suite 490 San JoseCA95110 United States Dai Nippon Printing Co. Ltd. 1-1-1 Ichigaya kagacho Shinjuku-ku Tokyo162-8001 Japan

ISBN: (数字)9781510642546

ISBN: (纸本)9781510642539

For image devices such as cameras and displays, it is difficult to obtain consistent color reproducibility due to individual manufacturer designs. We propose to improve the color reproducibility of medical images by using color charts. With the spread of COVID-19 infection, the need for telemedicine is increasing from the perspective of infection prevention. In addition, the digitization of medical images will progress in the future, and it is required that the color reproducibility of supervised data at the time of image acquisition be improved for learning/diagnosis assistance by AI. Colors in the specialized field of medical image processing are largely processed in non-standardized methods, raising many challenges in use cases where color is used for diagnostics. Smartphones are used in telemedicine, but color reproducibility may decrease due to differences in lighting as well as equipment, which may lead to a decrease in diagnostic accuracy. We have developed color charts and appropriate image processing algorithms for medical use cases. In the AI accuracy verification in pathological diagnosis, it was also confirmed that the AI diagnosis accuracy can be improved by performing image processing using a color chart. © 2021 spie. © 2021 spie. All rights reserved.

关键词： telemedicine

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Investigation on Automated Visual SMD-PCB Inspection based on Multimodal One-class Novelty Detection 3

Investigation on Automated Visual SMD-PCB Inspection based o...

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Multimodal Sensing and Artificial Intelligence: technologies and Applications III 2023

作者： Liu, Zheng Wang, Qichao Nestler, Rico Notni, Gunther Technische Universität Ilmenau Gustav-Kirchhoff-Platz 2 IlmenauD-98693 Germany ZBS e.V. Werner-von-Siemens-Str. 12 IlmenauD-98693 Germany Fraunhofer Institute for Applied Optics and Precision Engineering Albert-Einstein-Straße 7 JenaD-07743 Germany

ISBN: (纸本)9781510664517

In electronics manufacturing, the inspection of defects of electrical components on printed circuit boards (SMD-PCB) is an import part of the production chain. this process is normally implemented by automatic optical inspection (AOI) systems based on classical computer vision and multimodal imaging. Despite the highly developed image processing, misclassifications can occur due to the different, variable appearance of objects and defects and constantly emerging defect types, which can only be avoided by constant manual supervision and adaption. therefore, a lot of manpower is needed to do this or to perform a subjective follow-up. In this paper, we present a new method using the principle of multimodal deep learning-based one-class novelty-detection to support AOIs and operators to detect defects more accurate or to determine whether something needs to be changed. By combining with a given AOI classification a powerful adaptive AOI system can be realized. to evaluate the performance of the multimodal novelty-detector, we conducted experiments with SMD-PCB-components imaged in texture and geometric modalities. Based on the idea of one-class-detection only normal data is needed to form training sets. Annotated defect data which is normally only insufficiently available, is only used in the tests. We report about some experiments in accordance with the consistence of data categories to investigate the applicability of this approach in different scenarios. Hereby we compared different state-of-the-art one-class novelty detection techniques using image data of different modalities. Besides the influence of different data fusion methods are discussed to find a good way to use this data and to show the benefits using multimodal data. Our experiments show an outstanding performance of defect detection using multimodal data based on our approach. Our best value of the widely known AUROC reaches more than 0.99 with real test data. © 2023 spie.

关键词： Defects

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image processing: algorithms and systems XIX

Image Processing: Algorithms and Systems XIX

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19th image processing: algorithms and systems Conference, IPAS 2021

the proceedings contain 166 papers. the topics discussed include: data visualization and analysis of playing styles in tennis;crosstalk minimization method for eye-tracking-based 3D display;detecting deepfakes with Haralick’s texture properties;turkey behavior identification system with a GUI using deep learning and video analytics;projection methods for finding intersection of two convex sets and their use in signal processing problems;high-speed inline computational imaging for area scan cameras;interdisciplinary immersive experiences within artistic research, social and cognitive sciences;a single chip PPG sensor with enhanced IR sensitivity for low power and small size;single chip auto-valet parking system with tDA4VMID SoC;boosting computer vision performance by enhancing camera ISP;transfer learning with style transfer between the photorealistic and artistic domain;knowledge distillation from multispectral images for fish freshness estimation;next best light position: a self-configuring approach for the reflectance transformation imaging acquisition process;experimental study for revising visual noise measurement of ISO 15739;and computational identification of significant actors in paintings through symbols and attributes.

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Kurtosis-based connected components method for detecting small and transparent foreign objects in semiopaque bottles

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JOURNAL OF electronic imaging 2020年第4期29卷 041009-041009页

作者： Belhadj, Djallel Nuzillard, Danielle Vrabie, Valeriu Haddad, Madjid Univ Reims CReSTIC EA 3804 Reims France Segula Engn Rueil Malmaison France

In recent years, driven by new standards and image brand, product quality has become highly critical in the food industry. Particularly concerning the beverage industry, the potential presence of very small foreign objects, especially glass fragments, has to be checked. the visible optical domain offers an advantageous alternative solution to the expensive and intrusive x-ray systems. However, such a solution requires the development of robust processing algorithms satisfying real-time industrial constraints, which is a very challenging task. A detection method based on kurtosis and local information that emphasizes connected components is proposed, as well as two robust criteria that give a greater robustness to the detector. A specific kurtosis is calculated using an inclined frame definition that is based on both time and spatial dimensions. Such kurtosis increases the detection capacity of the moving objects and allows the estimation of their direction without adding more costs. the kurtosis being very sensitive to outliers, noisy objects that are very small are filtered, allowing efficient detection of foreign objects, such as glass fragments, as long as they are bigger than the noisy objects. In case of big foreign objects, the low sensitivity of the kurtosis is compensated for by the large detected surface. the proposed detection method can be directly applied on video sequences acquired by a standard RGB camera in industrial environments. the experimental results show the effectiveness of the method in detecting real random foreign objects, regardless of their size or their transparency, in various semiopaque bottles. (C) 2020 spie and IS&t

关键词： object detection kurtosis image processing foreign body detection semiopaque bottles RGB camera

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An active contour model for medical image segmentation using a quaternion framework 18

An active contour model for medical image segmentation using...

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18th image processing: algorithms and systems Conference, IPAS 2020

作者： Voronin, V. Zhdanova, M. Semenishchev, E. Zelensky, A. Agaian, S. Moscow State University of Technology "STANKIN" Moscow Russia Don State Technical University Lab. «Mathematical methods of image processing and computer vision Intelligent Systems» Gagarina 1 Rostov on Don Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

this paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. to solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. the presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. the experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods. © 2020, Society for imaging Science and technology

关键词： Adaptive filtering

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image processing: algorithms and systems XVIII

Image Processing: Algorithms and Systems XVIII

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18th image processing: algorithms and systems Conference, IPAS 2020

the proceedings contain 283 papers. the topics discussed include: a new training model for object detection in aerial images;managing crops across spatial and temporal scales - the roles of UAS and satellite remote sensing;a gaze-contingent system for foveated multiresolution visualization of vector and volumetric data;capturing and 3D rendering of optical behavior: the physical approach to realism;tereoscopic 3D optic flow distortions caused by mismatches between image acquisition and display parameters (JISt-first);active shooter response training environment for a building evacuation in a collaborative virtual environment;learning a CNN on multiple sclerosis lesion segmentation with self-supervision;reducing invertible embedding distortion using graph matching model;creating high-resolution 360-degree single-line 25k video content for modern conference rooms using film compositing techniques;spectral reproduction: drivers, use cases and workflow;SPECtRANEt: a deep model for skin oxygenation measurement from multi-spectral data;automotive image quality concepts for the next SAE levels: color separation and contrast detection probability;a 4-tap global shutter pixel with enhanced IR sensitivity for VGA time-of-flight CMOS image sensors;small object bird detection in infrared drone videos using mask R-CNN deep learning;real-world fence removal from a single-image via deep neural network;and perceptual quality assessment of enhanced images using a crowd-sourcing framework.

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Color Restoration of Multispectral images: Near-Infrared (NIR) filter-to-Color (RGB) image 18

Color Restoration of Multispectral Images: Near-Infrared (NI...

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18th image processing: algorithms and systems Conference, IPAS 2020

作者： trongtirakul, thaweesak Chiracharit, Werapon Agaian, Sos Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Computer Sciences CUNY Graduate Center College of Staten Island New York United States

Multispectral images captured by near-infrared (NIR) filtered devices have an attractive potential for numerous applications in computer vision, robot vision, and an artificial intelligence system. Although near-infrared (NIR) filtered devices display some unseen details, but their colors contain false colors affected main color components. therefore, the color restoration of near-infrared (NIR) filtered images is a challenge for human perception. to overcome the challenge, the proposed method has four main steps: i) color balance with chromatic adaptation;ii) color component switching;iii) Hue-Saturation-Lightness adjustment;and iv) image fusion using a proportional-weighing metric. the proposed results achieve to: i) restore RGB color component, ii) remove the color-overlapped effect and generate structure details close to the ground truth images;and iii) preserve overall brightness. © 2020, Society for imaging Science and technology

关键词： Color

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