{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2381251de13a0c13447b9f359be320f12/hcics", "tags" : [ "Computing","Machine","Pervasive","Wearable","computing,","hcics","learning,","processing,","signal","vis" ], "intraHash" : "381251de13a0c13447b9f359be320f12", "interHash" : "d53c528916deac562e62b7a2671e5612", "label" : "What's in the Eyes for Context-Awareness?", "user" : "hcics", "description" : "", "date" : "2024-07-11 10:05:52", "changeDate" : "2024-07-11 10:11:36", "count" : 1, "pub-type": "article", "journal": "IEEE Pervasive Computing", "year": "2011", "url": "", "author": [ "Andreas Bulling","Daniel Roggen","Gerhard Tröster" ], "authors": [ {"first" : "Andreas", "last" : "Bulling"}, {"first" : "Daniel", "last" : "Roggen"}, {"first" : "Gerhard", "last" : "Tröster"} ], "volume": "10","number": "2","pages": "48-57","abstract": "Eye movements are a rich source of information about a person's context. Analyzing the link between eye movements and cognition might even allow us to develop cognition-aware pervasive computing systems that assess a person's cognitive context.", "doi" : "10.1109/MPRV.2010.49", "bibtexKey": "bulling11_pcm" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28488a975de92a7205c78b3fc95ff2326/hcics", "tags" : [ "Feature","Ubiquitous","and","computing","evaluation","hcics","processing,","selection,","signal","vis" ], "intraHash" : "8488a975de92a7205c78b3fc95ff2326", "interHash" : "c57e2090594a9bf59fee711ed93db230", "label" : "Eye Movement Analysis for Activity Recognition Using Electrooculography", "user" : "hcics", "description" : "", "date" : "2024-07-11 10:05:52", "changeDate" : "2024-07-11 10:11:36", "count" : 1, "pub-type": "article", "journal": "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)", "year": "2011", "url": "", "author": [ "Andreas Bulling","Jamie A. Ward","Hans Gellersen","Gerhard Tröster" ], "authors": [ {"first" : "Andreas", "last" : "Bulling"}, {"first" : "Jamie A.", "last" : "Ward"}, {"first" : "Hans", "last" : "Gellersen"}, {"first" : "Gerhard", "last" : "Tröster"} ], "volume": "33","number": "4","pages": "741-753","note": "spotlight","abstract": "In this work we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data was recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals - saccades, fixations, and blinks - and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance feature selection (mRMR). We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video, and browsing the web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1% and recall of 70.5% over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.", "doi" : "10.1109/TPAMI.2010.86", "bibtexKey": "bulling11_pami" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c53294d6481cba5b14a2c0bda6f17e14/hcics", "tags" : [ "Activity","Recognition,","Sensing","Wearable","context","hcics","processing,","recognition,","signal","vis" ], "intraHash" : "c53294d6481cba5b14a2c0bda6f17e14", "interHash" : "18251e851470e03fb239784bb0202691", "label" : "Signal processing technologies for activity-aware smart textiles", "user" : "hcics", "description" : "", "date" : "2024-07-11 10:05:52", "changeDate" : "2024-07-11 10:11:36", "count" : 1, "pub-type": "inbook", "booktitle": "Woodhead Publishing Series in Textiles","publisher":"Woodhead Publishing Limited", "year": "2013", "url": "", "author": [ "Daniel Roggen","Andreas Bulling","Gerhard Tröster" ], "authors": [ {"first" : "Daniel", "last" : "Roggen"}, {"first" : "Andreas", "last" : "Bulling"}, {"first" : "Gerhard", "last" : "Tröster"} ], "editor": [ "Tünde Kirstein" ], "editors": [ {"first" : "Tünde", "last" : "Kirstein"} ], "number": "139","pages": "329-366","abstract": "Garments made of smart textiles have an enormous potential for embedding sensors in close proximity to the body in an unobtrusive and comfortable manner. Combined with signal processing and pattern recognition technologies, complex high-level information about human behaviors or situations can be inferred from the sensor data. The goal of this chapter is to introduce the reader to the design of activity-aware systems that use body-worn sensors, such as those that can be made available through smart textiles. We start this chapter by emphasizing recent trends towards \u2018wearable\u2019 sensing and computing and we present several examples of activity-aware applications. Then we outline the role that smart textiles can play in activity-aware applications, but also the challenges that they pose. We conclude by discussing the design process followed to devise activity-aware systems: the choice of sensors, the available data processing methods, and the evaluation techniques. We discuss recent data processing methods that address the challenges resulting from the use of smart textiles.", "doi" : "10.1533/9780857093530.2.329", "bibtexKey": "roggen13_wpt" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b3bb778cf3609f98508db5321c4a6f3c/thomasrichter", "tags" : [ "Abdominal;Reproducibility","Compression;Humans;Image","Computed","Computer-Assisted;Observation;ROC","Curve;Data","Curve;Radiography,","Nonparametric;Tomography,","Processing,","Results;Statistics,","Under","X-Ray","analysis;CT;HDR-VDP;JPEG2000","biological","coding;Computed","coding;Medical","coding;medical","coefficients;abdomen;computed","compression;MS-SSIM;PSNR;Spearman","compression;diagnostic","compression;image","correlation","diagnostic","difference","fidelity","image","imaging;Computed","imaging;PSNR;Radiology;Transform","metric;Adult;Area","metrics;multiscale","of","organs;computerised","predictor;image","processing;sensitivity","radiography;image","range","rank","ratio;Abdomen;Biomedical","science;Hospitals;Image","signal-to-noise","similarity;peak","structural","tomography;Computer","tomography;JPEG2000;image","tomography;data","tomography;high-dynamic","visual" ], "intraHash" : "b3bb778cf3609f98508db5321c4a6f3c", "interHash" : "39d4c0d08b7efb8cbf0f7a1c55cf1acd", "label" : "A Comparison of Three Image Fidelity Metrics of Different Computational Principles for JPEG2000 Compressed Abdomen CT Images", "user" : "thomasrichter", "description" : "", "date" : "2016-03-10 09:18:49", "changeDate" : "2016-03-10 08:20:00", "count" : 3, "pub-type": "article", "journal": "Medical Imaging, IEEE Transactions on", "year": "2010", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5482182", "author": [ "Kil Joong Kim","Bohyoung Kim","R. Mantiuk","T. Richter","Hyunna Lee","Heung-Sik Kang","Jinwook Seo","Kyoung Ho Lee" ], "authors": [ {"first" : "Kil Joong", "last" : "Kim"}, {"first" : "Bohyoung", "last" : "Kim"}, {"first" : "R.", "last" : "Mantiuk"}, {"first" : "T.", "last" : "Richter"}, {"first" : "Hyunna", "last" : "Lee"}, {"first" : "Heung-Sik", "last" : "Kang"}, {"first" : "Jinwook", "last" : "Seo"}, {"first" : "Kyoung Ho", "last" : "Lee"} ], "volume": "29","number": "8","pages": "1496-1503","abstract": "This study aimed to evaluate three image fidelity metrics of different computational principles-peak signal-to-noise ratio (PSNR), high-dynamic range visual difference predictor (HDR-VDP), and multiscale structural similarity (MS-SSIM)-in measuring the fidelity of JPEG2000 compressed abdomen computed tomography images from a viewpoint of visually lossless compression. Three hundred images with 0.67- or 5-mm section thickness were compressed to one of five compression ratios ranging from reversible compression to 15:1. The fidelity of each compressed image was measured by five radiologists' visual analyses (distinguishable or indistinguishable from the original) and the three metrics. The Spearman rank correlation coefficients of the PSNR, HDR-VDP, and MS-SSIM values with the number of readers responding as indistinguishable were 0.86, 0.94, and 0.86, respectively. Using the pooled readers' responses as the reference standard, the area under the receiver-operating-characteristic curve for the HDR-VDP (0.99) was significantly greater than that for the PSNR (0.95) (p <; 0.001) and for the MS-SSIM (0.96) (p = 0.003), and there was no significant difference between the PSNR and MS-SSIM (p = 0.70). In measuring the image fidelity, the HDR-VDP outperforms the PSNR and MS-SSIM, and the MS-SSIM and PSNR are comparable.", "issn" : "0278-0062", "doi" : "10.1109/TMI.2010.2049655", "bibtexKey": "kim2010comparison" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b3bb778cf3609f98508db5321c4a6f3c/rainerreichel", "tags" : [ "Abdominal;Reproducibility","Compression;Humans;Image","Computed","Computer-Assisted;Observation;ROC","Curve;Data","Curve;Radiography,","Nonparametric;Tomography,","Processing,","Results;Statistics,","Under","X-Ray","analysis;CT;HDR-VDP;JPEG2000","biological","coding;Computed","coding;Medical","coding;medical","coefficients;abdomen;computed","compression;MS-SSIM;PSNR;Spearman","compression;diagnostic","compression;image","correlation","diagnostic","difference","fidelity","image","imaging;Computed","imaging;PSNR;Radiology;Transform","metric;Adult;Area","metrics;multiscale","of","organs;computerised","predictor;image","processing;sensitivity","radiography;image","range","rank","ratio;Abdomen;Biomedical","science;Hospitals;Image","signal-to-noise","similarity;peak","structural","tomography;Computer","tomography;JPEG2000;image","tomography;data","tomography;high-dynamic","visual" ], "intraHash" : "b3bb778cf3609f98508db5321c4a6f3c", "interHash" : "39d4c0d08b7efb8cbf0f7a1c55cf1acd", "label" : "A Comparison of Three Image Fidelity Metrics of Different Computational Principles for JPEG2000 Compressed Abdomen CT Images", "user" : "rainerreichel", "description" : "", "date" : "2016-03-03 17:45:04", "changeDate" : "2016-03-04 09:57:29", "count" : 3, "pub-type": "article", "journal": "Medical Imaging, IEEE Transactions on", "year": "2010", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5482182", "author": [ "Kil Joong Kim","Bohyoung Kim","R. Mantiuk","T. Richter","Hyunna Lee","Heung-Sik Kang","Jinwook Seo","Kyoung Ho Lee" ], "authors": [ {"first" : "Kil Joong", "last" : "Kim"}, {"first" : "Bohyoung", "last" : "Kim"}, {"first" : "R.", "last" : "Mantiuk"}, {"first" : "T.", "last" : "Richter"}, {"first" : "Hyunna", "last" : "Lee"}, {"first" : "Heung-Sik", "last" : "Kang"}, {"first" : "Jinwook", "last" : "Seo"}, {"first" : "Kyoung Ho", "last" : "Lee"} ], "volume": "29","number": "8","pages": "1496-1503","abstract": "This study aimed to evaluate three image fidelity metrics of different computational principles-peak signal-to-noise ratio (PSNR), high-dynamic range visual difference predictor (HDR-VDP), and multiscale structural similarity (MS-SSIM)-in measuring the fidelity of JPEG2000 compressed abdomen computed tomography images from a viewpoint of visually lossless compression. Three hundred images with 0.67- or 5-mm section thickness were compressed to one of five compression ratios ranging from reversible compression to 15:1. The fidelity of each compressed image was measured by five radiologists' visual analyses (distinguishable or indistinguishable from the original) and the three metrics. The Spearman rank correlation coefficients of the PSNR, HDR-VDP, and MS-SSIM values with the number of readers responding as indistinguishable were 0.86, 0.94, and 0.86, respectively. Using the pooled readers' responses as the reference standard, the area under the receiver-operating-characteristic curve for the HDR-VDP (0.99) was significantly greater than that for the PSNR (0.95) (p <; 0.001) and for the MS-SSIM (0.96) (p = 0.003), and there was no significant difference between the PSNR and MS-SSIM (p = 0.70). In measuring the image fidelity, the HDR-VDP outperforms the PSNR and MS-SSIM, and the MS-SSIM and PSNR are comparable.", "issn" : "0278-0062", "doi" : "10.1109/TMI.2010.2049655", "bibtexKey": "5482182" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b7bc5e3ac75ab70485f5ec320d2ee425/isw-bibliothek", "tags" : [ "FPGA,","ISW","automation","image","processing,","signal","welding" ], "intraHash" : "b7bc5e3ac75ab70485f5ec320d2ee425", "interHash" : "555b257b3b3cb769583447af1e633b6a", "label" : "Intelligent FPGA-based Low-cost Signal Pre-Processing Unit for 2D- and 3D-Laser Triangulation Sensors.Opto 2000, Erfurt, 09.-11.05.2000. Intelligent FPGA-based Low-cost Signal Pre-Processing Unit for 2D- and 3D-Laser Triangulation Sensors.Opto 2000, Erfurt, 09.-11.05.2000.", "user" : "isw-bibliothek", "description" : "", "date" : "2016-03-03 09:53:12", "changeDate" : "2016-03-03 09:18:35", "count" : 1, "pub-type": "misc", "year": "2000", "url": "", "author": [ "G. Pritschow","K. Haug","H. Horber","S. Müller" ], "authors": [ {"first" : "G.", "last" : "Pritschow"}, {"first" : "K.", "last" : "Haug"}, {"first" : "H.", "last" : "Horber"}, {"first" : "S.", "last" : "Müller"} ], "pages": "--","abstract": "Laser-stripe sensors are still very rare in industrial applications due to their high prices. Therefore a new FPGA-solution was introduced that combines high parallel processing power with very attractive prices. Low space requirements of the FPGA allow for easy integration into the sensor head with robust serial data transmission. The FPGA-unit is not only applicable to laser-stripe sensors with up to eight stripes but can also be used to implement filter algorithms for degraded sensor images. It was shown, that a new digital filter using an FIR-bandpass could drastically improve the profile quality of frames degraded by the impacts of a welding process. Due to the parallel processing structure of FPGAs it will be possible to implement even these complex image filters in real time.", "__markedentry" : "[xtl:6]", "bibtexKey": "PritschowHaugHorberEtAl2000" } ] }