TY  - JOUR
A1  - Bruns, Erich
A1  - Bimber, Oliver
T1  - Phone-to-Phone Communication for Adaptive Image Classification
N2  - In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide – a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments.
KW  - Peer-to-Peer-Netz
KW  - Bilderkennung
KW  - Museumsführer
KW  - Ad-hoc-Netz
KW  - Phone-to-phone communication
KW  - adaptive image classification
KW  - mobile ad-hoc networks
KW  - museum guidance system
Y1  - 2008
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080722-13685
ER  - 
TY  - JOUR
A1  - Brombach, Benjamin
A1  - Bruns, Erich
A1  - Bimber, Oliver
T1  - Subobject Detection through Spatial Relationships on Mobile Phones
N2  - We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. Finally, all detected subobjects are labeled and the user can select the object of interest for retrieving corresponding multimedia information. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. We show that the recognition of subobjects using spatial relationships is up to 68% faster than related approaches without spatial relationships. Results of a field experiment in a local museum illustrate that unexperienced users reach an average recognition rate for subobjects of 85.6% under realistic conditions.
KW  - Objekterkennung
KW  - Smartphone
KW  - Subobjekterkennung
KW  - Räumliche Beziehungen
KW  - Neuronales Netz
KW  - Museumsführer
KW  - Subobject Detection
KW  - Spatial Relationships
KW  - Neural Networks
KW  - Museum Guidance
Y1  - 2008
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20081007-14296
ER  - 
TY  - JOUR
A1  - Jiang, Mingze
A1  - Rößler, Christiane
A1  - Wellmann, Eva
A1  - Klaver, Jop
A1  - Kleiner, Florian
A1  - Schmatz, Joyce
T1  - Workflow for high-resolution phase segmentation of cement clinker fromcombined BSE image and EDX spectral data
JF  - Journal of Microscopy
N2  - Burning of clinker is the most influencing step of cement quality during the production process. Appropriate characterisation for quality control and decision-making is therefore the critical point to maintain a stable production but also for the development of alternative cements. Scanning electron microscopy (SEM) in combination with energy dispersive X-ray spectroscopy (EDX) delivers spatially resolved phase and chemical information for cement clinker. This data can be used to quantify phase fractions and chemical composition of identified phases.

The contribution aims to provide an overview of phase fraction quantification by semi-automatic phase segmentation using high-resolution backscattered electron (BSE) images and lower-resolved EDX element maps. Therefore, a tool for image analysis was developed that uses state-of-the-art algorithms for pixel-wise image segmentation and labelling in combination with a decision tree that allows searching for specific clinker phases. Results show that this tool can be applied to segment sub-micron scale clinker phases and to get a quantification of all phase fractions. In addition, statistical evaluation of the data is implemented within the tool to reveal whether the imaged area is representative for all clinker phases.
KW  - Zementklinker
KW  - Bildsegmentierung
KW  - Rasterelektronenmikroskopie
KW  - cement clinker
KW  - image segmentation
KW  - EDX
KW  - superpixel
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20211215-45449
UR  - https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.13072
VL  - 2021
SP  - 1
EP  - 7
PB  - Wiley-Blackwell
CY  - Oxford
ER  -