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Eye-position tracking stereoscopic display using image shifting optics

Eye-position tracking stereoscopic display using image shift...

Conference on Stereoscopic Displays and Applications VII/Complementary Conference - The Engineering Reality of Virtual Reality 1996

作者： Imai, H Imai, M Ogura, Y Kubota, K NEC CORP LTD OPTOELECTR RES LABSMIYAMAE KUKAWASAKIKANAGAWA 216JAPAN

ISBN: (纸本)0819420271

We have developed an eye-position tracking stereoscopic projector which employs image shifting optics. The display allows 3D images to be viewed without special glasses from any position along a lateral axis. Its image shifting device contains a plane parallel glass plate and is installed in a liquid crystal projector. Refraction produced by inclination of the glass plate shifts the optical axis of the projected image. Since the only moving part in the optics is the lightweight glass plate, the response of the image shifting device is both fast and precise enough for interactive 3D-CAD and virtual reality applications. To improve the system's interactive response, we have widened the stereoscopic viewing area by adding a device which causes the image to vibrate laterally. Also we use a new tracking algorithm which reduces the tracking error that would ordinarily be created by delay time. Experimental results confirm the success of these improvements.

关键词： Glasses Detection and tracking algorithms Projection systems Eye Liquid crystals Stereoscopic displays Virtual reality 3D displays Magnetic sensors 3D image processing

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Development of a diagnosis tool, based on deep learning algorithms and infrared images, applicable to condition monitoring of induction motors under transient regime 46

Development of a diagnosis tool, based on deep learning algo...

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46th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Redon, P. Picazo Rodenas, M. J. Antonino-Daviu, J. Hlth Inst Carlos III Ctr Invest Biomed Red CIBER Madrid Spain Univ Hosp Generalitat Valenciana Dept Engn Clin Valencia Spain Univ Politecn Valencia Inst Tecnol Energia Valencia Spain

ISBN: (纸本)9781728154145

Infrared thermography can be a very useful technique for condition monitoring because the most common faults suffered by induction motors cause a temperature rise in the motor's frame. Moreover, this technique is non-intrusive, affordable and very sensitive due to the substantial technical progress in the design and development of new thermal cameras. However, data interpretation and decision making from the resulting infrared images is one of the major limitations of this technique, because it is directly dependent on the operator's experience. Several automated expert systems have been developed using machine learning and, to a lesser extent, with deep learning algorithms. The objective of this paper is to develop a diagnosis tool, based on infrared imaging and deep learning algorithms, applicable to induction motors working in transient conditions. The developed classifier, after training, presents high accuracy levels, classifying the images into one of the five considered scenarios and even at the early stages of the transient state. This methodology can be applied in a broad variety of scenarios with substantial cost saving and offering high-safety standards.

关键词： induction motors fault diagnosis infrared thermography deep learning image processing

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Polyp segmentation in NBI colonoscopy

Polyp segmentation in NBI colonoscopy

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2009 - Workshop on Image processing for Medicine: algorithms - systems - Applications, BVM 2009

作者： Gross, Sebastian Kennel, Manuel Stehle, Thomas Wulff, Jonas Tischendorf, Jens Trautwein, Christian Aach, Til Institute of Imaging and Computer Vision RWTH Aachen University Germany Medical Department III RWTH Aachen University Hospital Germany

ISBN: (纸本)9783540938590

Endoscopic screening of the colon (colonoscopy) is performed to prevent cancer and to support therapy. During intervention colon polyps are located, inspected and, if need be, removed by the investigator. We propose a segmentation algorithm as a part of an automatic polyp classification system for colonoscopic Narrow-Band images. Our approach includes multi-scale filtering for noise reduction, suppression of small blood vessels, and enhancement of major edges. Results of the subsequent edge detection are compared to a set of elliptic templates and evaluated. We validated our algorithm on our polyp database with images acquired during routine colonoscopic examinations. The presented results show the reliable segmentation performance of our method and its robustness to image variations.

关键词： Blood vessels

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FUSION OF STEREO VIEWS - ESTIMATING STRUCTURE AND MOTION USING A ROBUST METHOD

FUSION OF STEREO VIEWS - ESTIMATING STRUCTURE AND MOTION USI...

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CONF ON SENSOR FUSION 3 : 3-D PERCEPTION AND RECOGNITION

作者： WENG, JY COHEN, P Ecole Polytechnique and Ctr. de Recherche Informatique de Montreal (United States) Ecole Polytechnique (Canada)

ISBN: (纸本)0819404500

This paper concerns the estimation of motion parameters and scene structure through the fusion of successive stereo pairs. While a least-squares estimator is quite stable in the presence of well-behaved noise, it gives disastrous results when the input data are contaminated with a few outliers. Due to difficulties in stereo and temporal image matching, such outliers cannot be easily eliminated within the feature matching stage. Therefore, immunity to outliers is essential to motion and structure estimation algorithms. The robust estimator described in this paper reduces the influence of outliers so that the estimates are not very sensitive to gross errors in the input data. Experiments with real world images are presented with automatically established stereo and temporal matching. The accuracy of the estimated motion and depth map of the real scene is partially validated with the ground truth. Results show that the robust estimator is stable in the presence of outliers.

关键词： Cameras Error analysis imaging systems Motion estimation Calibration Image fusion Sensor fusion Stereoscopic cameras Motion analysis 3D image processing

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Challenges of medical image processing

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COMPUTER SCIENCE-RESEARCH AND DEVELOPMENT 2011年第1-2期26卷 5-13页

作者： Scholl, Ingrid Aach, Til Deserno, Thomas M. Kuhlen, Torsten FH Aachen Univ Appl Sci Fac Elect Engn & Informat Technol Aachen Germany Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Aachen Dept Med Informat Aachen Germany Rhein Westfal TH Aachen Virtual Real Grp Aachen Germany

In todays health care, imaging plays an important role throughout the entire clinical process from diagnostics and treatment planning to surgical procedures and follow up studies. Since most imaging modalities have gone directly digital, with continually increasing resolution, medical image processing has to face the challenges arising from large data volumes. In this paper, we discuss Kilo-to Terabyte challenges regarding (i) medical image management and image data mining, (ii) bioimaging, (iii) virtual reality in medical visualizations and (iv) neuroimaging. Due to the increasing amount of data, image processing and visualization algorithms have to be adjusted. Scalable algorithms and advanced parallelization techniques using graphical processing units have been developed. They are summarized in this paper. While such techniques are coping with the Kilo-to Terabyte challenge, the Petabyte level is already looming on the horizon. For this reason, medical image processing remains a vital field of research.

关键词： Medical imaging Bioimaging Neuroimaging Visualization Giga-Voxel Tera-Voxel Picture archiving and communication systems (PACS) Content-based image retrieval (CBIR) Virtual reality (VR) Graphics processing unit (GPU) programming Parallel algorithm Grid computing

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Parallel processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap 6

Parallel Processing over Spatial-Temporal Datasets from Geo,...

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IEEE 6th International Congress on Big Data (BigData Congress)

作者： Prasad, Sushil K. Aghajarian, Danial McDermott, Michael Shah, Dhara Mokbel, Mohamed Puri, Satish Rey, Sergio J. Shekhar, Shashi Xe, Yiqun Vatsavai, Ranga Raju Wang, Fusheng Liang, Yanhui Vo, Hoang Wang, Shaowen Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Marquette Univ Math Stat & Comp Sci Milwaukee WI 53233 USA Univ Calif Riverside Ctr Spatial Sci Riverside CA 92521 USA Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA SUNY Stony Brook Biomed Informat Dept Stony Brook NY 11794 USA Univ Illinois Geog & Geog Informat Sci Urbana IL USA

ISBN: (纸本)9781538619964

This vision paper reviews the current state-of art and lays out emerging research challenges in parallel processing of spatial-temporal large datasets relevant to a variety of scientific communities. The spatio-temporal data, whether captured through remote sensors (global earth observations), ground and ocean sensors (e.g., soil moisture sensors, buoys), social media and hand-held, traffic-related sensors and cameras, medical imaging (e.g., MRI), or large scale simulations (e.g., climate) have always been "big." A common thread among all these big collections of datasets is that they are spatial and temporal. processing and analyzing these datasets requires high-performance computing (HPC) infrastructures. Various agencies, scientific communities and increasingly the society at large rely on spatial data management, analysis, and spatial data mining to gain insights and produce actionable plans. Therefore, an ecosystem of integrated and reliable software infrastructure is required for spatial temporal big data management and analysis that will serve as crucial tools for solving a wide set of research problems from different scientific and engineering areas and to empower users with next-generation tools. This vision requires a multidisciplinary effort to significantly advance domain research and have a broad impact on the society. The areas of research discussed in this paper include (i) spatial data mining, (ii) data analytics over remote sensing data, (iii) processing medical images, (iv) spatial econometrics analyses, (v) Map-Reduce based systems for spatial computation and visualization, (vi) CyberGIS systems, and (vii) foundational parallel algorithms and data structures for polygonal datasets, and why HPC infrastructures, including harnessing graphics accelerators, are needed for time-critical applications.

关键词： High performance computing Spatial data mining Remote sensing data Medical images Spatial econometrics Map-reduce systems CyberGIS Parallel algorithms and data structures

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Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

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BIOINFORMATICS 2017年第7期33卷 1064-1072页

作者： Teodoro, George Kurc, Tahsin M. Taveira, Luis F. R. Melo, Alba C. M. A. Gao, Yi Kong, Jun Saltz, Joel H. Univ Brasilia Dept Comp Sci BR-70910900 Brasilia DF Brazil Emory Univ Biomed Informat Dept Atlanta GA 30322 USA SUNY Stony Brook Biomed Informat Dept Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA

Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential;(ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42 x compared to the results from the default parameters;(iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.

关键词： SEGMENTATION (Image processing) SENSITIVITY analysis SCALABILITY (systems engineering) FEASIBILITY studies IMAGE analysis algorithms

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Segmentation of blood vessel structures in colonoscopic NBI image data 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2011 - Workshop on Image processing for Medicine: algorithms - systems - Applications, BVM 2011

作者： Gross, Sebastian Palm, Stephan Behrens, Alexander Tischendorf, Jens J.W. Trautwein, Christian Aach, Til Lehrstuhl für Bildverarbeitung RWTH Aachen University 52056 Aachen Germany Medizinische Klinik III Universitätsklinikum Aachen 52057 Aachen Germany

ISBN: (数字)9783642193354

ISBN: (纸本)9783642193347

Bei der ärztlichen Einschätzung von Dickdarmpolypen kommt das neue Verfahren Narrow Band imaging zum Einsatz. Hierbei wird der Kontrast der charakteristischen Blutgefäße durch Modifikation der Beleuchtung deutlich angehoben, wodurch diese dann als Entscheidungsgrundlage herangezogen werden können. Aufbauend auf diesem Verfahren ist eine automatische Segmentierung der Blutgefäße und Klassifikation der Dickdarmpolypen möglich. In diesem Paper werden Verbesserungen für die Blutgefäßsegmentierung des von Stehle et al. vorgeschlagenen Verfahrens erläutert und Ergebnisse im Vergleich dargestellt. Es zeigt sich, dass sowohl die Qualität der Segmentierung als auch die Ergebnisse der automatisierten Klassifikation deutlich gesteigert werden konnten.

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Data acquisition and data analysis in colorimetric sensor arrays

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CHEMOMETRICS AND INTELLIGENT LABORATORY systems 2023年 241卷

作者： Hemmateenejad, Bahram Bordbar, Mohammad Mahdi Shojaeifard, Zahra Shiraz Univ Chem Dept Shiraz Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran

Responses in colorimetric sensor arrays are based on the monitoring color changes of chemical transducers by multichannel spectrophotometers or imaging recorders such as scanners, digital camera and smartphones. The images are then digitized using different image analysis algorithms. The array based methods provide a multidimensional data containing analytical, noise and outlier information. The inconsistent data can be removed or decreased by data pre-processing methods. Based on the goal of researches, the refined data can be processed by the pattern recognition methods for qualitative analysis or a by the multivariate regression models for quantitative analysis. This article explains (i) the mechanism of data collection using different types of readers, (ii) introduces the types and applications of algorithms and software for image digitalization, (iii) represents information about data preprocessing, variable selection and multivariate statistical methods and (iv) evaluates their applications in processing of colorimetric sensor array data published during the last decade.

关键词： Colorimetric sensor array Chemometrics Image analysis Data acquisition Pre-processing

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PACOME: Optimal multi-epoch combination of direct imaging observations for joint exoplanet detection and orbit estimation

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ASTRONOMY & ASTROPHYSICS 2023年 679卷

作者： Dallant, Jules Langlois, Maud Flasseur, Olivier Thiebaut, Eric Univ Lyon 1 Ctr Rech Astrophys Lyon CNRS UMR 5574 ENS Lyon F-69230 St Genis Laval France

Context. Exoplanet detections and characterizations via direct imaging require high contrast and high angular resolution. These requirements are typically pursued by combining (i) cutting-edge instrumental facilities equipped with extreme adaptive optics and coronagraphic systems, (ii) optimized differential imaging to introduce a diversity between the signals of the sought-for objects and that of the star, and (iii) dedicated (post-)processing algorithms to further eliminate the residual stellar ***. With respect to the third technique, substantial efforts have been undertaken over this last decade on the design of more efficient post-processing algorithms. The whole data collection and retrieval processes currently allow to detect massive exoplanets at angular separations greater than a few tenths of au. The performance remains upper-bounded at shorter angular separations due to the lack of diversity induced by the processing of each epoch of observations individually. We aim to propose a new algorithm that is able to combine several observations of the same star by accounting for the Keplerian orbital motion across epochs of the sought-for exoplanets in order to constructively co-add their weak ***. The proposed algorithm, PACOME, integrates an exploration of the plausible orbits of the sought-for objects within an end-to-end statistical detection and estimation formalism. The latter is extended to a multi-epoch combination of the maximum likelihood framework of PACO, which is a post-processing algorithm of single-epoch observations. From this, we derived a reliable multi-epoch detection criterion, interpretable both in terms of probability of detection and of false alarm. In addition, PACOME is able to produce a few plausible estimates of the orbital elements of the detected sources and provide their local error ***. We tested the proposed algorithm on several datasets obtained from the VLT/SPHERE instrument with IRDIS and IFS usin

关键词： instrumentation: high angular resolution techniques: image processing methods: statistical methods: data analysis planets and satellites: detection stars: individual: HR 8799

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